A2
Power transformers
and reactors
Analysis of AC transformer
reliability
TECHNICAL BROCHURES
September 2024 - Reference 939
TECHNICAL BROCHURE
Analysis of AC transformer
reliability
WG A2.62
Members
Stefan TENBOHLEN, Convenor
Roberto ASANO Jr.
Pablo PACHECO RAMOS
Simone SACCO
Shin YAMADA
Keneilwe SELEME
Gobi K SUPRAMANIAM
Jack HERRING
DE
BR
ES
IT
JP
ZA
MY
IE
Dan MARTIN, Secretary
Zeenat HANIF
Ed TENYENHUIS
Andrew COLLIER
ShengJi TEE
Piotr MANSKI
Martin BUELLESBACH
Cornelius PLATH
AU & NZ
DE
CA
DE
UK
PL
DE
DE
Copyright © 2024
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information available on this page".
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as to the accuracy or exhaustiveness of the information. All implied warranties and conditions are excluded to the
maximum extent permitted by law”.
ISBN : 978-2-85873-644-7
TB 939 - Analysis of AC transformer reliability
Acknowledgement
The members of CIGRE working group A2.62 first and foremost express their sincere thanks to the
representatives of the 66 utilities that have collected service and failure information and contributed to
this survey.
We also acknowledge the help and assistance from many colleagues and associates in carrying out
our tasks:
T. Abasse, J. Beardsall, R. Bronegard, J. Brown, K. Bryla, C. Clark, C. Gamez, L. Ganepola, M.
Gibson, J. Gragert, K. F. Guri, I. D. Hategan, R. Haug, J. Jagers, M. Jordanoff, S. Kleyboldt, M. Ling, T.
MacArthur, N. Majer, D. Ming, S. Mulquiney, S. Murali, K. Nanu, P. New, T. Olesen, P. Onions, L.
Perfetto, P. Picher, M. Shahid, A. Shkolnik, A. Singh, R. Sinton, R. de Smedt, U. Sundermann, J. Van
Peteghem, R. Willoughby, M. Wynd, S. Zhang.
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TB 939 - Analysis of AC transformer reliability
Executive Summary
The reliability of the global power transformer fleet was reviewed again, continuing CIGRE’s long legacy
of surveying started by Bossi et al forty years ago. As technology and usage changes it is beneficial to
find out any impacts on the long-lasting reliability of power grids. The industry is, on the whole, very
proactive at lowering the probability of failure, which is shown in these statistics.
In total, 425,000+ transformer-years of operation was collected, along with 1,204 major failures and
1,916 retirements from 66 utilities, where the countries are shown in Figure 1. A significant update in
this survey was the collection of retirement data, as most owners will reduce the chance of a failure on
their fleet by proactive replacement.
Replotting the A2.37 failure data as a function of age, the hazard rate has now fallen by more than half
since the last working group. Now, the failure rate is 0.1 %-0.2 % annually. The industry has placed
greater emphasis on improving reliability over the lifecycle of the transformer. In this survey there was
a focus on also understanding retirement rate because when an asset is retired obviously it cannot fail,
skewing the failure rate. A recommendation is for CIGRE to investigate producing a guidance framework
to help owners globally make decisions on when to retire, as such decisions will differ dependent upon
risk, costs, and customer tolerance. Complete lifecycle costs of running to failure versus retirement was
not explored, and so care must be taken when interpreting results.
When considering failure location, proportionally bushings are more of a problem than before, while
there are fewer unknown-location events. Proportions of other locations are fairly similar to before.
The hazard rate for scrapping units accelerated after twenty years. In contrast the hazard rate for all
major failures was more random with only a slight increase with age. It may be that certain
subcomponents of a transformer are expected to be replaced during the life of a transformer, while
other parts fail on a later age. There may also be economic considerations if a utility decides there is
not enough residual value in a failed transformer to justify a repair. However, this may change for
instance caused by supply chain shortages.
Data was sought from new applications of transformers, such as solar and wind farms. However, as
they are relatively new there has not been enough operational service-years and failures to make
statistically significant conclusions. A recommendation is to continue these surveys to continue to build
experience. A thorough discussion has been given on how the industry has improved the reliability of
power transformers, with advice how an owner can request a quality design and maintain longevity
over the long operating life of this asset class. Power transformers and reactors having a voltage at
least 100 kV were studied, and so it complements A2.68 which focussed on lower voltage GSUs.
Figure 1: Countries supplying data to survey.
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TB 939 - Analysis of AC transformer reliability
Table of content
List of Figures ..................................................................................................................... 7
List of Tables ....................................................................................................................... 9
Abbreviations and Definitions.......................................................................................... 10
1
Introduction ............................................................................................................. 11
1.1
Scope ....................................................................................................................................................... 11
1.2
Structure of Brochure ............................................................................................................................. 11
2
Key Country Updates since 2015 ........................................................................... 12
2.1
ASEAN Transmission Utilities ............................................................................................................... 12
2.2
Australia and New Zealand..................................................................................................................... 12
2.3
North America ......................................................................................................................................... 14
2.4
South Africa ............................................................................................................................................. 16
2.5
Spain ........................................................................................................................................................ 18
3
Measures to Increase Reliability ............................................................................ 19
3.1
Technical Specifications ........................................................................................................................ 19
3.2
Design ...................................................................................................................................................... 21
3.2.1
Dielectric Design ............................................................................................................................... 21
3.2.2
Mechanical Design ............................................................................................................................ 22
3.2.3
Thermal Design ................................................................................................................................. 23
3.2.4
Bushings............................................................................................................................................ 24
3.2.5
Tap Changers.................................................................................................................................... 25
3.3
Manufacturing, Materials and Testing ................................................................................................... 26
3.3.1
Core .................................................................................................................................................. 26
3.3.2
Windings............................................................................................................................................ 26
3.3.3
CTC conductors ................................................................................................................................ 27
3.3.4
Improving short-circuit strength ......................................................................................................... 27
3.3.5
Drying of active part .......................................................................................................................... 27
3.3.6
Insulating Materials ........................................................................................................................... 27
3.3.7
Acceptance Testing ........................................................................................................................... 28
3.3.8
On-site manufacturing technologies .................................................................................................. 28
3.3.9
On-site testing ................................................................................................................................... 28
3.4
Operation ................................................................................................................................................. 29
3.4.1
Monitoring and Digital Innovation ...................................................................................................... 29
3.5
Maintenance ............................................................................................................................................ 31
4
Interpretation of Life Data ....................................................................................... 33
4.1
Reliability Life Data Analysis ................................................................................................................. 33
4.2
Non-parametric Method .......................................................................................................................... 33
4.3
Weibull distribution ................................................................................................................................. 34
5
Methodology for Failure Data Collection ............................................................... 37
5.1
Definition of Failure and Retirement...................................................................................................... 37
5.2
Reliability Questionnaire ........................................................................................................................ 37
5.3
Classification of Failures ........................................................................................................................ 38
5.4
Data Collection and Limitations ............................................................................................................. 39
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TB 939 - Analysis of AC transformer reliability
5.4.1
5.4.2
Data collection ................................................................................................................................... 39
Data Limitations................................................................................................................................. 39
6
Results of Performed Reliability Survey ................................................................ 40
6.1
Investigated population .......................................................................................................................... 40
6.2
Failure and Retirement Rates ................................................................................................................. 44
6.3
Failure Data Analysis .............................................................................................................................. 48
6.3.1
Failure Mode Analysis ....................................................................................................................... 48
6.3.2
Failure Location Analysis................................................................................................................... 50
6.3.3
Failure Cause Analysis ...................................................................................................................... 52
6.3.4
External Effects Analysis ................................................................................................................... 54
6.3.5
Action Analysis .................................................................................................................................. 56
6.3.6
Detection Mode Analysis ................................................................................................................... 57
6.3.7
Comparison with CIGRE Survey of A2.37 ......................................................................................... 59
7
Hazard Curves ......................................................................................................... 63
7.1
Age Distribution ...................................................................................................................................... 63
7.2
Hazard curves for retirements caused by condition or age ................................................................ 67
7.3
Hazard Curves for All Major Failures..................................................................................................... 69
7.4
Hazard Curves for Scrapped Transformers .......................................................................................... 72
7.5
Hazard Curves for Scrapping by Continent .......................................................................................... 75
7.5.1
Africa ................................................................................................................................................. 76
7.5.2
East Asia ........................................................................................................................................... 76
7.5.3
Europe ............................................................................................................................................... 78
7.5.4
North America ................................................................................................................................... 79
7.5.5
Oceania ............................................................................................................................................. 81
7.5.6
South America ................................................................................................................................... 83
7.6
Combined Weibull distributions for failure and retirement (due to age or condition) ...................... 84
7.7
Effect of removing bushings and OLTC on hazard rate ...................................................................... 85
7.8
Kaplan-Meier Estimator .......................................................................................................................... 87
7.9
Combining data with the WG A2.37 survey .......................................................................................... 88
7.10
Summary .................................................................................................................................................. 91
8
Analysis of Failures of Transformers Connected to GIS, Wind Farm Transformers,
Transformers Filled with New Liquids, Shunt Reactors ................................................. 92
8.1.1
8.1.2
8.1.3
New liquids ........................................................................................................................................ 92
Wind and PV reliability....................................................................................................................... 92
Shunt reactor reliability ...................................................................................................................... 93
9
Conclusion and Recommendations ....................................................................... 96
10
References ............................................................................................................... 99
11
Appendix ................................................................................................................ 103
A)
Questionnaire – Excel Sheets .............................................................................................................. 103
B)
Questionnaire – Content of Pull-down Menus .................................................................................... 105
C)
Determination of Weibull confidence limits ........................................................................................ 108
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TB 939 - Analysis of AC transformer reliability
List of Figures
Figure 1: Countries supplying data to survey. ......................................................................................... 4
Figure 2: Number of transformer failures resulting in fires or explosions in Australia and New Zealand,
showing a decline in events from 2007 onwards [8]. ............................................................................ 13
Figure 3: Catastrophic failure rate derived from Australian and New Zealand events [8]. ................... 14
Figure 4: Failure mode for Australian and New Zealand transformers resulting in fires or explosions [8].
.............................................................................................................................................................. 14
Figure 5: Number of outages per transformer due to failed AC substation equipment. ....................... 15
Figure 6: Transformer unavailability. ..................................................................................................... 15
Figure 7: Transformer availability [10]. .................................................................................................. 16
Figure 8: Number of failures vs. transformer age in South Africa. ........................................................ 16
Figure 9: Failure location in South African failures. .............................................................................. 17
Figure 10: Typical accumulated axial forces graph for LV (left) and HV (right) windings. The y-axis
indicates along the height of the winding, away from its bottom. It shows the distribution of force. ..... 22
Figure 11: Field lines and losses 2D FEM. ........................................................................................... 23
Figure 12: Temperature with 2D CFD, showing an example of a temperature simulation output. These
structures represent windings, and the convecting flow of oil cooling the conductor. The cooling oil
enters at the bottom, and rises as it is heated. The left side shows natural convection of oil, while the
right side simulates faster flowing. ........................................................................................................ 24
Figure 13: Shapes of the Weibull Hazard Function [51]. ...................................................................... 35
Figure 14: Analysis of example data using Weibull distributions (x-axis: natural logarithm of age) [52].
.............................................................................................................................................................. 35
Figure 15: Hazard rate for coefficients in Figure 14. ............................................................................. 36
Figure 16: Countries supplying data to survey...................................................................................... 40
Figure 17: Age profile for in-service transformers. ................................................................................ 41
Figure 18: Population and transformer-years. ...................................................................................... 42
Figure 19: Percentage of transformer years by participating continents. ............................................. 42
Figure 20: Failure and retirement rate of individual utilities. ................................................................. 45
Figure 21: Failures and retirements according to voltage class. .......................................................... 45
Figure 22: Failure rate dependent upon voltage class and application. ............................................... 46
Figure 23: Retirement rate dependent upon voltage class and application. ......................................... 46
Figure 24: Failure mode analysis (780 failures). ................................................................................... 48
Figure 25: Failure mode analysis according to transformer application (substation 622 failures, shunt
reactors 49 failures, GSU 56 failures). .................................................................................................. 49
Figure 26: Failure location analysis for 848 failures. ............................................................................ 51
Figure 27: Failure location analysis and transformer application (substation 708 failures, shunt reactors
45 failures, GSU 59 failures). ................................................................................................................ 51
Figure 28: Failure cause analysis for 783 failures. ............................................................................... 52
Figure 29: Failure cause analysis according to transformer application (highest contributors only). .. 54
Figure 30: External effects analysis for 728 failures. ............................................................................ 55
Figure 31: External effects analysis and transformer application (substation 642 failures, shunt reactors
41 failures, GSU 45 failures). ................................................................................................................ 55
Figure 32: Action analysis for 778 major failures. ................................................................................. 56
Figure 33: Action analysis applied to transformer application (substation 674 failures, shunt reactors
41 failures, GSU 63 failures). ................................................................................................................ 57
Figure 34: Detection mode analysis for 769 transformers. ................................................................... 58
Figure 35: Detection mode analysis and transformer application (substation 660 failures, shunt reactors
46 failures, GSU 63 failures). ................................................................................................................ 59
Figure 36: Comparison of failure location. ............................................................................................ 60
Figure 37: Comparison of external effects. ........................................................................................... 60
Figure 38: Comparison of failure modes. .............................................................................................. 61
Figure 39: Comparison of failure causes. ............................................................................................. 61
Figure 40: Comparison of action taken after failure. ............................................................................. 62
Figure 41: Number of failures dependent on service age, when the transformer was either repaired and
returned to service or when it was scrapped. ....................................................................................... 63
Figure 42: Approach to deduce age distribution for failure. .................................................................. 64
Figure 43: Nameplates of oldest transformer recorded by survey. ....................................................... 65
Figure 44: African age distribution. ....................................................................................................... 65
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TB 939 - Analysis of AC transformer reliability
Figure 45: East Asian age distribution. ................................................................................................. 65
Figure 46: European age distribution. ................................................................................................... 66
Figure 47: North American age distribution. ......................................................................................... 66
Figure 48: Australian and New Zealand age distribution. ..................................................................... 66
Figure 49: South American age distribution. ......................................................................................... 67
Figure 50: Age distribution of retiring units. .......................................................................................... 67
Figure 51: Hazard function of retired units due to condition. ................................................................ 68
Figure 52: Age distribution of retirements by voltage class. ................................................................. 68
Figure 53: Hazard rate for retiring by voltage class. ............................................................................. 69
Figure 54: Fraction failing for all major failures as a function of age, 884 failures and 28,119 operating
units. ...................................................................................................................................................... 70
Figure 55: Hazard rate for all major failures, as a function of age. ....................................................... 70
Figure 56: Fraction failing, all major failures by voltage class. ............................................................. 71
Figure 57: Hazard rate, all major failures by voltage class. .................................................................. 71
Figure 58: All scrapped failures cumulative distribution function. ......................................................... 72
Figure 59. Hazard rate for all scrapped failures. Dashed line indicates upper 95 % confidence interval.
.............................................................................................................................................................. 73
Figure 60: Scrapped failures according to voltage class. ..................................................................... 74
Figure 61: Hazard rate of scrapped failures according to voltage class. .............................................. 74
Figure 62: African transformer failure and retirement rate. ................................................................... 76
Figure 63: East Asian transformer failure and retirement rate. ............................................................. 77
Figure 64: Curve fitting to East Asian failure data................................................................................. 77
Figure 65: Hazard rate of East Asian transformers. ............................................................................. 78
Figure 66: European transformer failure rate. There is an outlier at 70-years. ..................................... 78
Figure 67: Curve fitting to European failure data. ................................................................................. 79
Figure 68: Weibull distribution fitted to European failure data. The last datapoint is an outlier. ........... 79
Figure 69: North American transformer failure and retirement rate. ..................................................... 80
Figure 70: Curve fitting to North American failure data. ........................................................................ 80
Figure 71: Weibull distribution fitted to North American failure data. .................................................... 81
Figure 72: Australian and New Zealand transformer failure rate. ......................................................... 81
Figure 73: Curve fitting to Australian and New Zealand failure data. ................................................... 82
Figure 74: Weibull distribution fitted to Australian and New Zealand failure data. ............................... 82
Figure 75: South American transformer failure rate. ............................................................................. 83
Figure 76: Age distribution for failures where the unit was subsequently scrapped, and retirements
based on age or condition. .................................................................................................................... 84
Figure 77: Combined hazard rate for failures and retirements. ............................................................ 85
Figure 78: Bushing and OLTC removed for 100 – 199 kV transformers. ............................................. 86
Figure 79: Bushing and OLTC removed for 200 – 299 kV transformers. ............................................. 86
Figure 80: Bushing and OLTC removed for 300 – 499 kV transformers. ............................................. 87
Figure 81: Kaplan-Meier estimator, showing that the mean survival is at least sixty years. ................ 88
Figure 82: Comparison of hazard rates for WG A2.37 and WG A2.62. ................................................ 89
Figure 83: Comparison of voltage class hazard rates. ......................................................................... 90
Figure 84: Age distribution of failed reactors. ....................................................................................... 93
Figure 85: Excel questionnaire – Population data. ............................................................................. 103
Figure 86: Excel questionnaire – Age distribution. ............................................................................. 104
Figure 87: Excel questionnaire – Failure and retirement data. ........................................................... 104
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TB 939 - Analysis of AC transformer reliability
List of Tables
Table 1: Determining failure and retirement rates................................................................................. 34
Table 2: Transformer population data stratified by voltage class. ........................................................ 41
Table 3: Investigated transformer population........................................................................................ 43
Table 4: Investigated population of substation transformers. ............................................................... 44
Table 5: Investigated population of shunt reactor transformers............................................................ 44
Table 6: Investigated population of generator step-up transformers. ................................................... 44
Table 7: The failure and retirement rate of substation transformers depending on the voltage class. . 47
Table 8: The failure rate of shunt reactor transformers depending on the voltage class. .................... 47
Table 9: The failure rate of GSUs transformers depending on the voltage class. ................................ 47
Table 10: Failure mode analysis depending on the voltage class. ....................................................... 48
Table 11: Failure location depending on the voltage class. .................................................................. 50
Table 12: Failure cause analysis depending on the voltage class........................................................ 53
Table 13: External effects of failures depending on voltage class. ....................................................... 54
Table 14: Action analysis depending on voltage class. ........................................................................ 56
Table 15: Detection mode analysis depending on the voltage class. ................................................... 58
Table 16: Weibull parameters for retirements. Retirements / Survivors: 1268 / 27170 ....................... 68
Table 17: Coefficients for retirement distributions................................................................................. 69
Table 18: Coefficients for all major failures. .......................................................................................... 70
Table 19: Distribution coefficients for all failures, different voltage class transformers. ....................... 71
Table 20: Distribution fitting descriptives. ............................................................................................. 73
Table 21: Distribution coefficients for different voltage class transformers. ......................................... 75
Table 22: Regional statistics. Censors refers to the number of units still operating by the end of the data
collection period. ................................................................................................................................... 75
Table 23: Weibull distribution line coefficients. ..................................................................................... 83
Table 24: Weibull distribution parameters for failures and retirements. ............................................... 85
Table 25: Weibull distribution parameters for failures where bushings and OLTCs were removed from
analysis. ................................................................................................................................................ 87
Table 26: Transformer population statistics. ......................................................................................... 89
Table 27: WG37 line coefficients. ......................................................................................................... 91
Table 28: Shunt reactor failures resulting in the unit being scrapped. .................................................. 94
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TB 939 - Analysis of AC transformer reliability
Abbreviations and Definitions
AER – Australian Energy Regulator
BIL – Basic Insulation Level
CBM – Condition Based Maintenance
CTC – Continuously Transposed Conductor
DETC – Deenergised Tap Changer
DGA – Dissolved Gas Analysis
DP – Degree of Polymerisation (of cellulosic insulation)
FAT – Factory Acceptance Tests
GIS – Gas Insulated Switchgear
LV – Low Voltage (i.e. 110 to 240 V)
NERC - North American Electric Reliability Corporation
OIP – Oil Impregnated Paper
OLTC – On-load Tap Changer
pC – Pico Coulomb
RBM – Risk Based Maintenance
RIN – Regulatory Information Notices (which are required by the Australian energy regulator)
RIP – Resin Impregnated Paper
RIS – Resin Impregnated Synthetic
RSO – Recurrent Surge Oscillograph
TUP – Thermally Upgraded Paper
VFPF – Variable Frequency Power Factor
VSR – Variable Shunt Reactor
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TB 939 - Analysis of AC transformer reliability
1
Introduction
CIGRE has a rich history of collecting statistical data on failure, with the earliest transformer survey
being published in 1983 [1]. Learnings from these surveys have helped manufacturers improve their
products, users understand risks, and to plan maintenance. As a statistical analysis uses historical data
to forecast events, it is worth repeating this work periodically to ascertain how reliability has changed.
These days there is more use of renewables, an emphasis to decarbonise, interest in information
technology to oversee assets, and the emergence of electric vehicles. It will be interesting to observe
if any of these impacts the reliability of the power transformer. Thus, it is important to periodically
revisit failure statistics.
The last CIGRE international survey for transformer reliability was published in 2015 [2]. However, it
was not possible to compare failure rates because the age distribution of the investigated transformer
population was not known. Consequently, a new survey was performed which includes detailed
information about the individual age distributions.
1.1 Scope
1. Update of questionnaire regarding new experiences.
2. Conduction of a new survey about major failures and replacement for the period 2010
to 2019.
3. Analysis of failure data in terms of failure rate, location, mode and cause.
4. Individual analysis of failures of transformers connected to GIS, wind farm
transformers, transformers filled with new liquids and shunt reactors.
5. Determination of the hazard curve of failure and replacement for different transformer
populations.
1.2 Structure of Brochure
This brochure is divided into the following chapters:
Chapter 2 gives key updates on the use of failure statistics to make engineering decisions which have
happened worldwide since the 2015 survey.
Chapter 3 describes methods to improve reliability in the design and operation of a transformer.
Chapter 4 defines the interpretation of life data; the statistical analyses and techniques used in this
brochure to process data.
Chapter 5 introduces the methodology for data collection.
Chapter 6 presents the results of the reliability survey.
Chapter 7 gives the hazard curves and population statistics for failure and retirement.
Chapter 8 discusses failure of transformers connected to GIS, wind farms, using new liquids, and
connected to shunt reactors.
Chapter 9 provides conclusions and recommendations.
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TB 939 - Analysis of AC transformer reliability
2
Key Country Updates since 2015
Overviews were collected for the key national updates which have taken place since the last brochure,
for transformer reliability, was published in 2015. Responses were received from ASEAN (Malaysia,
Indonesia and Thailand), Australia and New Zealand, North America, South Africa and Spain.
2.1 ASEAN Transmission Utilities
This survey was completed by the major transmission grid utilities in Malaysia, Indonesia and Thailand.
These utility members have had a consistent demand growth on average of several percent annually,
constituting approximately 60 % of the energy demand of the ASEAN region.
Since 2015, most utilities in this region have shifted their maintenance paradigm from a fixed frequency
calendar-based maintenance to a more proactive and predictive CBM and RBM maintenance regime.
Online DGA, oil quality and online thermal scanning assessments have been made more frequent to
monitor critical assets. As bushing contributes to significant catastrophic and fire risk, all member
utilities have a preference to use RIP/RIS dry technology with polymeric/porcelain options for
capacitance graded bushings above 52 kV in order to reduce the risk of fire. This strategy has been
extended to also replace the existing OIP bushings in the transformer population.
The region is located mainly in a high isokeraunic level environment. Thus, some utilities specify the
lightning impulse withstand level (BIL) of the LV side winding to be one class higher, and protected
with medium voltage surge arrestors to reduce problems of potential repetitive transient faults.
ASEAN utilities faced a high number of tap changer failures over the years due to design, quality and
inadequate maintenance issues. Most utilities have made the decision to migrate to vacuum technology
or retrofit with oil filters, where appropriate, from renowned pre-selected suppliers which have been
found to effectively reduce the number of failures over the years.
Specification improvements have been implemented which utilize an epoxy bonded CTC conductor in
the windings, with the high radially inward stress having a safety margin up to 15 % higher than IEC
recommended limits. A challenge of operation in tropical climatic conditions is the high ambient
temperature, potentially resulting in faster insulation ageing. Therefore, many utilities decided to use
Thermally Upgraded paper with inhibited-class mineral oil, or ester liquid as a green option. A routine
test introduced has been the temperature rise at the maximum rating of the transformer to detect
thermal defects early. The acceptable partial discharge level for many designs has been reduced to 50
pC at 1.2x rated voltage. The degree of polymerisation is measured for paper after insulation processing
is required to be above 900, and the water content of solid insulation is measured using dielectric
response to be <1 % between the primary and secondary windings.
As few failures have been reported caused by reactive power loading on the tertiary winding of autotransformers, some utilities have changed their strategy to operate the tertiary in an unloaded or small
load condition. The flexibility of reactive compensation been compensated with the use of VSR
technology. This has been implemented effectively where it has been found to save space and
experience fewer switching transients compared to conventional fixed Static Var Compensators.
Some utilities have established a strategic framework to perform mid-life refurbishment at
approximately 18-25 years in service, which includes retrofitting of air bags, gasket replacement, oil
processing, cooling system upgrades, corrosion and painting rectification based on condition.
There has been a trend post-COVID for more remote monitoring to give improved asset visibility. As
most failure patterns were fast evolving and random in nature, efforts to digitalize asset using real time
has been implemented. Technology such as DGA multi or single gas monitoring and bushing monitoring
were preferred to assess critical asset in real time with a visualisation platform.
2.2 Australia and New Zealand
The Australian Energy Regulator (AER) obliges utilities to provide financial and technical information
annually in standardised spreadsheets, known as Regulatory Information Notices (RIN) [3], which are
available to the public. These spreadsheets have become more comprehensive over the years, and now
include data on asset size and age distribution. A utility also presents the number of failures and
retirements each year, but they do not need to present the age. The age distribution is for units in
12
TB 939 - Analysis of AC transformer reliability
service. Any assets in storage are not counted. Therefore, if a transformer fails it may be replaced by
one of the same age and the distribution stays the same. The regulator has been moving towards more
use of statistical calculations to justify engineering decisions [4].
In New Zealand, tables are provided in Annual Management Plans which show asset base and
population. Sometimes, condition based on a standard formula is used. This results in the age
population not always being readily available.
There has been a long history of recording failures and presenting statistics in the region, which started
with Petersen in 1996 [5], and Petersen again with Austin in 2004 [6]. As the likelihood of a power
transformer failing is very low, one utility is unlikely to experience enough failures to produce an
accurate probability model. Thus, the regional utilities are aware of the need to collaborate on large
scale data collections. In 2015 the last reliability brochure [2] spurred another update of Australian and
New Zealand failure statistic led by Marks and Martin [8, 9, 10, 7]. Key outcomes were:
•
•
•
•
•
•
6,057 Australian power transformers covered (at least 1 MVA). This was 97 % of the utilityowned fleet. The lowest voltage units surveyed had a 11 kV primary, although the secondary
side had a voltage above 1 kV (i.e. not a distribution-class transformer).
80,549 transformer-years of data used since 2000. In the 1990s the power industry was
privatised and dis-aggregated, thus many of the utilities were unable to provide data from
before this period.
187 failures and 313 retirements where the age was known.
Population statistics were stratified into classes for distribution (≤ 66 kV), subtransmission (110
and 132 kV), and transmission (≥ 200 kV).
Useful life appeared to end around 20 years, and then a wear-out period observed. No obviously
separate 'early life' region was noted because of the low number of early-failures.
Average failure rate 0.2 % per annum.
The survey was extended to also cover New Zealand power transformer failures. From an analysis of
92,119 service years, the statistics of fires and explosions was investigated (22 events occurring
between 2000 and 2015) [8]. The yearly number of fires and explosions appears to have fallen from
2007, attributed to better technology and condition monitoring strategies, as shown in Figure 2. When
considering the catastrophic failure rate, Figure 3, there appears distributions for early failure (up to
five years), random (twenty years), then and increasing rate after (wear-out). This has implications for
managing older transformer fleets. An analysis of failure modes, Figure 4, found that there were the
same number of OLTC initiated events as from bushings. The authors comment that many local utilities
have undertaken work to improve the reliability of bushings, since previous surveys had noted that they
were a significant contributor towards fires. The proportion of fires caused by bushings was suspected
to have dropped to a level similar to OLTCs.
Figure 2: Number of transformer failures resulting in fires or explosions in Australia and New
Zealand, showing a decline in events from 2007 onwards [8].
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TB 939 - Analysis of AC transformer reliability
Figure 3: Catastrophic failure rate derived from Australian and New Zealand events [8].
Figure 4: Failure mode for Australian and New Zealand transformers resulting in fires or
explosions [8].
2.3 North America
The North American Electric Reliability Corporation (NERC) publishes power (and transformer) reliability
statistics in an annual report to its members, which include all the utilities in Canada and USA. These
reports and statistics are available to the public on its website. The latest report [11] from July 2022
shows a steady improvement in transformer reliability over the last 10 years.
In particular, see below Figure 5 for the number of outages per transformer from 2017 to 2021 [13].
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TB 939 - Analysis of AC transformer reliability
Figure 5: Number of outages per transformer due to failed AC substation equipment.
Shown below in Figure 6 is transformer unavailability from 2017 to 2021 [11]. This also shows an
improvement.
Figure 6: Transformer unavailability.
For comparison, the statistics of transformer unavailability were gathered from 2014 (the time of the
earlier CIGRE TB 642). This is shown below in Figure 7 transformer unavailability in 2015 [12]. The
statistics were shown in a reverse way at that time (percent availability). This essentially shows a
transformer unavailability of 2.5 – 3.5 % compared to the 2021 where it is in the 0.25 % range.
15
TB 939 - Analysis of AC transformer reliability
Figure 7: Transformer availability [12].
This improvement in transformer reliability may be due to many factors including improved
maintenance, planned maintenance, reduced false trips etc. and only partially to reduced transformer
failures. Nonetheless there does seem to be a reduced transformer failure rate in North America since
the CIGRE TB 642 was published.
2.4 South Africa
The current failure data was obtained from Eskom’s distribution division. Data was collected from nine
regions of South Africa and analysed. Since the technical recommendations of the 2015 brochure [2],
Eskom implemented changing oil impregnated bushings to the resin type. This has significantly reduced
catastrophic power transformer failures. Figure 8 illustrates the age distribution of the failed power
transformers. Most failures occurred between the ages 30 and 39 years. The significance of assessing
failures with respect to age is to identify whether there are inherent challenges per group.
Figure 8: Number of failures vs. transformer age in South Africa.
The failures were categorised in terms of location and root cause, in relation to age group, to establish
their susceptibility to different network conditions. A significant proportion of failures, 50 %, has been
16
TB 939 - Analysis of AC transformer reliability
caused by the windings, Figure 9, which is similar to other studies. The next two highest contributors
of failure were caused by capacitance graded bushings (18 %) and tap changers (16 %).
The winding failures were mainly from power transformers aged 1 to 38 years, with the majority being
older within this age range. The majority of these failures were due to external short circuits, likely
cumulative high-current faults which exceeded the withstand capability of the transformer. All the
power transformers which failed on insulation were aged 32 to 51 years, caused by short circuit faults.
These short circuits also contributed to HV exit lead and tapping winding failures.
Failures which were caused by bushings were from units aged 11 – 42 years, with only three
transformers aged less than 20 years. Eskom Distribution has experienced a bushing failure from an
RIP type, but this could be replaced on site and the transformer returned to service. This is one of the
benefits of the implementation of bushing replacement programme, none of the transformers with
replaced RIP bushings experienced catastrophic failure due to bushing problems.
Tap changer failures resulted from network over-voltages (lightning impulses), dielectric faults, internal
faults and unsuccessful repairs. The minority of transformers experienced rare core failures, and these
defects were suspected to have been inherent.
South Africa’s diverse data collection with the support of technical investigations facilitated in improving
the plant and network operational conditions. This also encouraged proactiveness in the form of valueadded condition monitoring. Therefore, the key learnings and proposals to improve the reliability of
power transformers are as follows:
•
The implementation of recommendations such as the replacement of OIP to Dry (RIP/RIS)
bushings has benefitted Eskom positively.
•
Trend analysis of bushing dielectric dissipation factor and capacitance is implemented to ensure
proactive replacement of bushings.
•
Tap-changer mechanisms were refurbished to lengthen equipment life, and newly designed
power transformers use vacuum tap changers.
•
Winding failures are mitigated by monitoring relay protection records after nuisance tripping,
and continuously trending oil sample results to establish any developing faults (DGA).
•
Circuit breaker trips are monitored to trigger an investigation into understanding the frequency
and level of short circuits, which can damage the transformer.
Figure 9: Failure location in South African failures.
17
TB 939 - Analysis of AC transformer reliability
2.5 Spain
Since 2015 transformer reliability strategies have been updated by the main Spanish utilities with the
objective to improve the performance and life expectancy of their transformer fleet.
Most of these strategic updates in reliability are focused on technical specification updates and
maintenance activity enhancements. With regards to technical specifications changes, the requirement
of RIP bushings is now a common practice for voltage levels above 36 kV. The analysis of transferred
voltages to tertiary windings, or other floating winding terminals, is also an extended requirement for
both design and FAT stages. Spanish utilities seem interested in limiting transferred overvoltages which
may create issues or even failures, as has been experienced in the past.
In addition, transformers technical specifications have been updated to require the use of fibre optic
hot spot temperature sensors inside the first unit for heat run type tests. This is to validate the design
with a better accuracy in terms of winding hot spot temperature knowledge to assure proper life
expectancy. The installation of fibre optic temperature probes allows the utilities to perform on-line life
expectancy calculations as well, thanks to recent digitalization trends.
A change in some technical specifications was the requirement for the transformer to operate with
either mineral or ester oil. The units are required to be designed to be filled with any of these two
insulating liquids, which would allow the utility to eventually switch from one oil type to another at any
time during the transformer life, reducing risk of fire in most critical locations for example.
Finally, performing variable frequency power factor (VFPF) measurements for bushings, both during
factory acceptance testing (FAT) and on-site tests. Some utilities state that this allows to better evaluate
the status of the bushing, and to avoid future failures. Some utilities explain that several bushings
would have been accepted using the traditional 50 Hz power factor measurement only, but they could
be rejected during FAT stage and possible bushing failures would be minimized.
From a maintenance point of view, the major updates observed for the utilities are related to the
acquisition of specific monitoring equipment. Therefore, multi-gas DGA, bushing and conservator
membrane rupture monitors are being sequentially implemented in the existing transformer fleet. In
addition, many utilities are improving their digitalization and data management abilities. Usually
monitored parameters include variables like top oil, hot spot winding temperatures, ambient
temperature, load, as well as bushing capacitance and power factor.
One utility has been involved in an extensive transformer fleet renewal, as this utility has observed that
some of their recent failures were related to the aged solid insulation in old units.
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TB 939 - Analysis of AC transformer reliability
3
Measures to Increase Reliability
Reliability statistics provide a snapshot of failure over a certain time frame. This failure rate can change
as new technologies, design criteria, monitoring or asset management advancements are utilised, or if
stresses change. As the life of a power transformer can exceed 50 years, many changes are likely to
have taken place. This chapter provides an overview of the key improvements which have occurred so
the audience can understand and apply some of these techniques.
3.1 Technical Specifications
The power transformer Technical Specification formally describes all technical aspects requirements by
the purchaser:
•
•
•
•
•
•
Industry Standards to be applied
Main characteristics and rated value
o Power Transformer type (auto-transformer, transformer, number of phases, number
of windings, connection symbol and frequency etc.)
o Tapping range
o Service condition
o Rated power and voltage of each winding
o Short circuit impedance
o Type of cooling and temperature rise limits
o Highest voltage for equipment and insulation level for each winding
o Dimension and mass limitations
o Noise level
Manufacturing requirements
o Core
o Winding
o Active part assembly and processing
o Tank
Main components
o Insulating fluid
o Bushings
o OLTC/DETC
o Cooling system
Accessories
o Electrical accessories
o Mechanical accessories
Factory Acceptance Testing (FAT), which Type Testing is a part of
Other topics that can be part of a technical specification are quality management, quality assurance
and documentation requirements.
Regardless of the level of detail, the technical specification has a large impact on the future transformer
reliability. A power transformer can fail in different modes (electrical, dielectric, mechanical, etc.)
because of different causes like over-voltages and external short circuits. Such causes define the
stresses that a transformer must withstand. Failure can occur when a transformer cannot withstand
the stress applied due to defect or improper safety margin. The technical specification should cover
both defect and safety margin. It is recommended that the below quality management system
components and service conditions are declared:
-
Performances and service conditions (normal and unusual service conditions)
Service operating conditions
Installation environment (temperature, seismic disturbances, etc.)
Material and components
Testing (factory and site testing)
Transport, and storage conditions
Commissioning
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TB 939 - Analysis of AC transformer reliability
Transformers can be exposed to overvoltages. Therefore, technical specifications should declare the
rated voltage as well as the insulating level of the line end and neutral end. It must be recognized that
insulation levels are related to the rated voltage, see IEC standard 60076-3 [13] or [14] for guidance.
However other considerations such as the insulation coordination of the electrical system, network
neutral earthing and protection criteria should be included. Also related to dielectric stresses are the
regulation and tapping scheme, type of connection of the windings and type of insulation (uniform or
not uniform).
International standards specify dielectric tests to be performed during FAT as routine or type tests.
Standardized stress levels are recommended according to the expected operation for each system
voltage. It shall be recognized that due to economic reasons higher basic insulation levels (BIL) are not
strictly proportional to the maximum system voltage (Um), i.e. BIL/Um ratio is up to 4.3 for 245 kV
transformers while for 800 kV it is 2.7, as per IEC 60076-3 [13]. With this regard, there are various
available BIL levels which shall be selected by the users according to the expected system protection,
as in surge arresters, the criticality of operation and accepted risk.
In summary, it is important to underline that the IEC and IEEE standards offer more than one BIL level
respect to one rated voltage. The higher the specified voltage tests, the lower the risk of failure during
operation because the design of the insulation system will need to be adequate for the FAT tests,
however the cost will be higher. Therefore, it is recommended to review in detail the selected BIL for
each system voltage involved in the transformer design and the appropriate tests and test level, in
accordance with IEC 60076-3 [14].
Service conditions, both normal and unusual, will impact reliability and should be declared according to
IEC standard 60076-1 [15] or [14] for the transformer, tap changer and bushing components. It is well
known that external short-circuits faults are responsible for a significant portion of transformer failures.
In particular, the high level of through-fault overcurrent generates strong electrodynamic forces which
can cause a mechanical collapse of the winding. The ability of a transformer to withstand the high
stress related to such a high overcurrent is dependent upon design technology, material, assembly
quality, and drying parameters. Transformers are designed to withstand forces due to the specified
through-fault over-current, which should represent: the worst-case scenario (typically a three-phase
short circuit), the short circuit power of the network, and the network sequence impedance parameters.
For the mechanical withstand capability, the end user can require the short circuit withstand test.
According to IEC 60076-1 and IEC 60076-5 [16] the requirement and the test procedure must be
declared in the technical specifications. Otherwise, the user can require a theoretical evaluation of the
design; in that case IEC 60076-5 Annex A and Annex B requirements should be taken as standard in
the technical specifications.
Power transformers are highly efficient; however, some losses and heat are still generated. Heat is
dissipated by the cooling system, but still results in a temperature rise in the winding and core. Excessive
temperatures can affect the reliability of a transformer for the ageing process of insulation. Core, tank,
beams and metal structures also heat when subject to a variable magnetic field. Their temperatures
shall also be controlled to avoid damage to paint or the adjacent oil. In some cases, external tank
temperature limits shall also be specified taking into consideration the possibility of human or animal
contact.
Technical specifications should deal with the thermal performance and declare the ambient temperature
and the temperature rise for the top oil, winding and hot spot. From a design point of view, the
temperature during service is the main driver of cellulose ageing. Around the temperature range of
normal operation, the ageing rate of the insulation doubles for every six degrees Celsius, so even short
duration overtemperatures can significantly degrade the insulation material. Given this, it is important
to consider all possible loading conditions, as normal loading cycle, short time overload or long-time
overload in accordance with IEC 60076-7 [17] or [18] at given ambient temperature. Technical
specifications should also declare the material for the insulation. Solid insulation of conductors can be
thermally upgraded paper per IEC 60076-14 [19] or IEEE C57.12.00 [14], which offers a +12 degrees
temperature margin compared to traditional Kraft paper for unity loss of life (110 °C versus 98 °C) at
a reasonably low incremental cost. The IEEE calls for a minimum of thermally upgraded paper, whereas
the IEC allow standard Kraft paper. Also, fluid insulation impacts the ageing process so the type of
fluid, mineral oil or ester oil should be declared. Finally, storage conditions, commissioning and an
adequate cooling system have an impact on reliability so must be evaluated and, if the case, declared.
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TB 939 - Analysis of AC transformer reliability
Special tests and fibre optic winding hot spot sensors can be used to improve the thermal design
knowledge of the transformer. The installation of fibre optic sensors adds complexity to the transformer
manufacturing, but it allows an accurate hot spot temperature measurement during the FAT.
For a more precise parameterization of thermal models, an extended temperature rise test (type test)
is recommended in accordance with IEC 60076-7, annex G [17], [20] and IEEE C57.119, clauses 9, 10
and 11. The data recorded may be used to determine those thermal characteristics, which are needed
to solve the transformer loading guide equations. The three temperature rise tests (100 %, 70 % and
125 %) should be performed with measurement of the hot spot temperature by fibre optics. The
extended temperature rise overload test provides additional information on the thermal behaviour in
overload operation. During the overload test, care should be taken to ensure that all fans are in
operation and thus the cooling behaviour at maximum cooling is mapped.
The ageing of the winding conductor cellulose insulation material depends mainly on its water content,
the concentration of oxygen dissolved in the oil, and the temperature. As the cellulose degrades, it
becomes brittle which will limit the transformer life. The condition of the insulation material is
characterized by the degree of polymerization. The water content of the insulation liquid should be as
low as possible by preventing moisture ingress into the transformer and doing oil maintenance. To limit
moisture in the oil, it is recommended to specify the breathing system of the transformer. The CIGRE
Technical Brochure 528 can support end-users to prepare a technical specification [21].
3.2 Design
Power transformer design is a crucial contributor to future transformer reliability during service. The
transformer design should comply with performance parameters such as rated power, rated voltage,
short circuit impedance, losses, etc; and requirements for thermal, electric, dielectric and mechanical
performance per the technical specification and international standards, which are validated through
factory acceptance testing (FAT) after manufacturing.
In general, manufacturers keep track of factory test failures and in-service field failures during the
warranty period. Failures are investigated for root cause analysis to identify possible improvements for
design rules and standards to further improve reliability.
During the design stage, and prior to any raw material purchasing by the manufacturer, one of the
most common actions to confirm the design adequacy to the purchaser’s requirements is to perform a
design review. The CIGRE Technical Brochure 529, published by WG A2.36 [22] provides a useful
guideline to conduct efficient design review meetings by purchasers or their representatives, and
manufacturers of power transformers. The main goals of a transformer design review are to confirm
that the provided design meets the intended performance in service, and that the manufacturers use
proven materials, design tools, methodology and expertise to assure the product is suitable for the
application.
Below are recent transformer design improvements (observed during the last decade) to enhance
transformed reliability during service. These improvements should be analysed in detail during a design
review meeting.
3.2.1
Dielectric Design
Changing the insulation system technology (i.e. to dry type transformers or to dry type bushings) may
not directly impact reliability but it may lessen the consequence of a failure. Less flammable K class
fluids (ester oils, for example), or synthetic solid insulating materials like aramid paper are currently
available and can be evaluated by users in accordance with the transformer application. More
information can be found in CIGRE Technical Brochure 856 [23], for dielectric performance of insulating
liquids for transformers.
In relation to transient overvoltage issues, sometimes it is desirable to perform reduced transferred
voltage measurements at non-grounded terminals during FAT (RSO test), to confirm that the resultant
overvoltage due to capacitive coupling between adjacent windings will not result in higher voltage than
the design value for non-grounded terminals. This request is common in transformers with double low
voltage systems, where one of the low voltage windings in delta connection might not be immediately
grounded after disconnection from a generator and may suffer from high switching transient voltages.
21
TB 939 - Analysis of AC transformer reliability
3.2.2
Mechanical Design
Most of the recent improvements in transformer design has been a result of improvements in the tools
for evaluation of the mechanical structures.
Large power transformers are heavy pieces of complex equipment inside a metal tank. During
operation, these pieces are seated on a base full of liquid with positive pressure inside. Its tank is
subjected to different stresses during the manufacturing and transport phases, while it is desirable that
it remains sealed with no rust during the complete transformer life. The tank design shall consider all
these aspects and any special requirement that shall be informed by the users, such as transportation
modes, wind speed, pollution and expected seismic requirements. The specification of overpressure
and special design with or without valves to withstand or relieve sudden pressure development may
also be evaluated by the users. With this regard, the transformers tank should be designed to withstand
internal arc failures without tank rupture, therefore, limiting the risk of fire. In the last decade some
transformer manufacturers have developed flexible tank design concepts based on CIGRE WG A2.33
recommendations in “Guide for Transformer Fire Safety Practices” [24].
The most common mechanical failure in a power transformer is caused by external short circuits,
resulting in a loss of clamping pressure in the active part, windings buckling and other mechanical
effects in the coils. Figure 10 illustrates the typical accumulated axial forces across the height of the
winding for both LV and HV voltage parts within a two-winding transformer. The mechanical structure
and the clamping must cope with those forces.
Figure 10: Typical accumulated axial forces graph for LV (left) and HV (right) windings. The yaxis indicates along the height of the winding, away from its bottom. It shows the distribution of
force.
With regards to the external short circuit test, it is not common to perform short circuit tests due to
budgetary limitations, limited testing facilities, and project time restrictions. However, performing a
short circuit special test is becoming increasingly common for medium size units. If it is not possible to
perform a short-circuit test, then the IEC 60076-5 standard [16] is followed as a global reference to
establish any possible similarity with a reference transformer which has been previously tested (Annex
B). If finally, no test or no similar reference is available, then, the current practise is to review in detail
the manufacturers short circuit calculations as per CIGRE TB 529 [22] and IEC 60076-5 [16] reference
documents.
As per CIGRE Technical Brochure 529, the short circuit calculation should involve the forces produced
on all windings, taps and output terminals and their direction; the location of the most critical stresses
where conductors cross and at winding terminals, and demonstration that winding design enables
windings to withstand the prescribed fault level among others.
In terms of clamping pressure issues in the active part, this might have an important relation to the
number and level of short circuits that the transformer has experienced historically. During every short
circuit, the axial forces push the clamping rings up and down, and so the clamping pressure may relax
over time until the windings finally become loose. Eventually the winding can collapse under short
circuit and mechanical failure takes place. To improve the reliability under a high number of short circuit
events, it is recommended to review the clamping pressure applied on the coils during the design
22
TB 939 - Analysis of AC transformer reliability
review, and to agree on a force level and locking mechanism that could provide a proper and permanent
compression during service.
3.2.3
Thermal Design
It is well-known that the temperatures inside the transformer windings depend on the distribution of
local losses and the oil flow. Magnetic finite element simulations should be used to calculate the losses
in each winding conductor considering the effect of the flux penetrating the conductors. The conductor
shape and orientation, with respect to the flux direction, needs to be considered. Figure 11 shows
typical magnetic field lines and loss generation for a two-winding transformer.
LV
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LV
HV
Figure 11: Field lines and losses 2D FEM.
A circuit solver can be used to calculate possible circulating currents between parallel conductors or
winding segments that could add additional losses. If significant current harmonics are expected, they
should also be considered in this calculation. The local winding temperatures and the hot spot is
determined by solving the thermal network considering the distribution of losses and the oil flow
through the windings. For a detailed analysis, the temperature distribution can be simulated using
Computational Fluid Dynamic (CFD) software such as shown in Figure 12.
23
TB 939 - Analysis of AC transformer reliability
Figure 12: Temperature with 2D CFD, showing an example of a temperature simulation output.
These structures represent windings, and the convecting flow of oil cooling the conductor. The
cooling oil enters at the bottom, and rises as it is heated. The left side shows natural convection
of oil, while the right side simulates faster flowing.
The result of those simulations, together with the heat run test measurement performed during FAT,
can be used as inputs to a monitoring system, condition assessment or as a base for a “digital twin”
i.e. for simulating the performance in emergency situations.
For critical units, it is recommended that the winding hot spot temperature is obtained by applying an
accurate calculated hot spot factor based on Annex B in IEC 60076-2 standard [25], instead of
traditional general factors available in Annex K in IEC 60076-7 standard [17] (like 1.1 for small or 1.3
for large power transformers) or clause 6 in IEEE 1538 standard, which may result in a significant
deviation from the actual winding hot spot temperature rises. During a design review, it is
recommended to be attentive to the hot spot temperature rise estimation and its location. Direct hot
spot measurements during temperature rise tests are recommended for critical units, as they allow to
validate the winding hot spot calculation.
Nowadays, some actions are performed to minimise cooling issues. The complete or partial loss of
cooling capacity may lead to overheating which can deteriorate the conductor insulation resulting in
dielectric failure. From a design point of view, it is recommended to specify redundant cooling
equipment, such as one or two additional radiators, one or two additional fans, one additional pump
and bypassing pipes for pumps maintenance (if pumps are used). This redundant cooling equipment
will avoid loss of cooling capacity and therefore, will minimize the risk of thermal failure. In recent years
some utilities have requested special “no-cooling” temperature rise tests for critical transformers, with
the intention to obtain the recommended maximum durations the unit can operate at 100 % after a
complete loss of cooling without exceeding certain agreed temperature levels for winding hot spots and
top oil. This kind of test might be of interest for main power transformers in nuclear generation stations
or critical substation transformers.
3.2.4
Bushings
The accumulation of service experience from completely dry, capacitively graded bushings with
polymeric insulation has contributed to safer products with much reduced consequences in the event
of violent failures compared to oil filled products. Advancements in design tools have enabled better
simulations of various mechanical, electrical, and thermal scenarios contributing to more reliable
products. Recently introduced products with insulation made from synthetics (RIS) will eliminate many
of the problems related to the ingression of moisture. The introduction of new and more powerful
condition assessment techniques and accumulated knowledge, given in the CIGRE Bushing Reliability
brochure [26], IEEE Std C57.12.200 [27] and the latest revision of IEC 60599 [28], will play a major
role in increasing bushing reliability once fully implemented by the utility industry. On-line monitoring
systems will contribute to earlier detection of certain types of problems. It is valuable to note that,
different from large power transformers, bushings are rarely designed for each specific application and
user. Most available bushings are standardized according to certain traditional and voltage levels. This
may cause difficulties in regions or with customer with mixed standards. Montenegro et al. presented
24
TB 939 - Analysis of AC transformer reliability
examples of this challenge for Brazil in 2022 [29]. While Brazilian standardization tradition is currently
moving toward IEC standards, some IEC requirements are not suitable to local requirements such as
average ambient temperature which has a huge impact in the thermal stability of bushings due to its
small volume. Other requirements such as bushing overloadability, according to customer specs, are
also difficult to access and there is a risk of over or under sizing.
While there are reported incidents of bushings failing after as little as 10 to 12 years, these should be
considered the exception as it is not unknown for well-engineered and maintained bushings to be in
service for 40, 50 years or more [26]. It should however be noted that for many transformers and
shunt reactors, bushings are the component most exposed to environmental and electrical stresses,
and they are also often attractive to birds and animals. Bushing maintenance and replacement
strategies vary from region to region and customer to customer, but it is generally considered that a
bushing failure has a high risk of catastrophic consequences and visual inspections on or above the
tank top can be problematic. Offline testing is particularly valuable to catch potential problems as early
as possible, but also requires an outage so is typically only performed every 2-4 years and therefore
unlikely to catch the immediate implications of external influences such as extreme weather events.
Online monitoring is therefore increasingly used at higher voltage ratings to complement the offline
testing schedule. It should however be noted that the typical solutions utilising the tap adapter as
sensing point are not without their own risks and should only be applied in conjunction with an
engineering study or in coordination with the transformer or shunt reactor manufacturer. Online
bushing monitoring options range in cost and complexity with Sum of Currents currently the most
popular option, this is relatively easy to retrofit and focuses on the relative change of capacitance and
dissipation factor and is often complemented with partial discharge monitoring. The voltage comparison
method is more resilient to grid disturbances or imbalances, but requires an external voltage reference
so is more complicated and expensive to apply.
3.2.5
Tap Changers
In principle, the on-load tap-changer is designed to change tap position and hence vary the turns ratio
of the transformer whilst it is both energised and on load. It performs this function without any
interruption of the supply. This is achieved with mechanically operated devices that will select the
various tap positions and switch the load currents and step voltages. In some applications typically in
low power in which transformer ratio does not require frequent changing, Off Circuit Tap Changer
(OCTC) or De-energised Tap Changer (DETC) are utilised. These require the transformer to be deenergised first, before the manual hand or rotary wheel/slider switch is used. A system must be in place
to prevent operation during energisation, as this can be fatal to staff.
On-load tap-changing can be employed by using various switching principles.
(i) High speed transition resistor type switching; and
(ii) Transition reactor (preventive auto-transformer) type switching where reactor type on-load tap
changers are normally designed to be applied to the low winding of the transformer
The basic functional principle of OLTC serves to connect the desired tap of tapped winding under load
divided into 2 types of OLTC which includes:
(i) Diverter switch type – consists of separate diverter and tap selector
This type of tap-changer consists of change-over selectors and tap selectors, designed to select tap
connections, and transfer switches (oil switches or vacuum interrupters), designed to break and
make current and, therefore, perform the arcing duty of the tap changing operations. Selectors and
transfer switches are located in separate liquid compartments. This design of a tap-changer tends
to be used on the larger current transformers.
(ii) Selector switch type – consists of a combined diverter switch and tap selector unit
This type of tap-changer will incorporate a selector switch (arcing tap switch) which performs the
functions of making/breaking current and selection of tap connections, combining the duties of a
tap selector and a diverter switch. The selector switch and the change-over selector, if they exist,
are contained in one single compartment. This design of a tap-changer tends to be used on the
smaller MVA transformers.
25
TB 939 - Analysis of AC transformer reliability
OLTCs mostly have high-speed resistor switching and tungsten-copper alloy contacts to quickly
extinguish the arc at the zero-current point. Typically, this type of technology requires more
maintenance due to oil carbonisation, contaminants and contact wear-out. Demand for higher plant
reliability, lower asset life cycle, safety and environmental concerns have contributed to new innovations
in vacuum-type technology. The new improvement significantly reduces life cycle cost, minimises oil
contamination, improves the mechanical and electrical life of tap changers, reduces maintenance, and
ensures safe and reliable operation.
The OLTC has major moving parts, thus the mechanical movement can contribute to the wear of
components over time. Typical failure modes for an OLTC are electrical, mechanical, chemical and
thermal stresses which require maintenance and assessment at prescribed intervals by the OEM, or
internal operating procedures, to avoid failures or further degradation. Resistance and dynamic current
measurements are being implemented, as they are suitable for detecting problems [30]. In some
utilities, online monitoring of OLTC has been implemented to monitor the number of operations, contact
wear, DGA, oil moisture/dielectric breakdown, vibro-acoustic monitoring and Motor Torque/Energy as
a way to reduce operating expenditure and improve reliability.
3.3 Manufacturing, Materials and Testing
Reliable power transformers are the by-product of safe, clean and dry manufacturing facilities, with
optimised processes and well-trained personnel. Power transformers are physically large, heavy, highcost assets with long lead times. They take many months to design and manufacture, and typically
requiring specialist transport solutions. To ensure timely delivery of any large transformer, production
facilities should first focus on safety and the prevention of accidents. Safety is the enabler for quality,
however for the reliable production an audited Quality Management System (QMS) complying with
standards such as ISO 9001 should be considered the minimum requirement. In addition, any quality
system should prioritise prevention and mitigation of potential problems above simple quality control
checks. For manufacturing technology, several superior techniques are being used to reduce
manufacturing time while also improving product quality. High quality standards, Manufacturing
Execution Systems (MES) and where practical automated manufacturing processes are essential to
supplement design improvements and ensure what is designed is also what is produced.
New inspection and quality control technologies are being deployed during factory assembly, on-site
assembly, on-site refurbishment and on-site testing. When working with any new supplier it is
recommended to audit the manufacturing plant capabilities in line with CIGRE TB 530 [31] guide for
conducting factory capability assessments, and to include for witness inspections of key components
such as windings and active part assembly or pre-tanking.
3.3.1
Core
As the transformer core is made up of very many very thin laminations, it is extremely important these
are free of burrs and a high degree of automation for slitting and cutting operations can help to achieve
better dimensional accuracy. Operators must ensure joints are tightly butted up without overlapping.
Step-lap joints can be more labour intensive, but can also help to achieve lower core loss and noise
levels. Robots are also used in the core stacking process, which allow highly accurate step-lap joints to
be made and provide a significant reduction in manufacturing time.
Elliptical jigs and use of upper core clamps for handling of incomplete cores are important to ensure
laminations do not move during changes in the semi-finished core orientation changes and/or
transportation.
3.3.2
Windings
Larger and complex windings are mostly produced manually relying on the skills, experience and
training that has been provided to those performing the task. When attending a production location,
topics such as cleanliness of the production areas and alignment of winding spacers are indicators of
where time is being invested in the prevention of future problems.
Vertical winding machines with conductor feed systems aid operators for the winding of large-capacity
and complicated transformer coils. Automated winding machines can be helpful for smaller and simpler
windings.
Pressing, drying and accurate sizing of individual windings saves valuable time later in the production
process and allows for the identification of potential problems ahead of active part assembly.
26
TB 939 - Analysis of AC transformer reliability
3.3.3
CTC conductors
The continuously transposed cable (CTC) conductor is preferably epoxy bonded type for greater short
circuit strength. Netted CTC conductors are also used. There have been attempts to improve the
winding space factor significantly by using a cable in which number of parallel rectangular insulated
conductors are bonded edge-to-edge with epoxy.
3.3.4
Improving short-circuit strength
Failure of transformers due to short circuits is a major concern for transformer users. To enhance the
short-circuit withstand capability of windings, a work-hardened copper material is commonly used. CTC
conductors can be of the epoxy-bonded type to enhance their short-circuit strength.
3.3.5
Drying of active part
The combination of heat and vacuum are used to ensure that as much as possible moisture and all air
bubbles have been removed from the insulation system, providing electrical integrity and a long service
life. Factory processing with heat and vacuum is impossible to duplicate in the field or in most service
facilities. When a transformer is opened the time exposed to the atmosphere must be limited, and the
oil should not be drained below the level of the top of the coils unless absolutely necessary. All efforts
must be taken to keep air bubbles out of the insulation structure.
When manufacturing a new transformer, the time between the active part leaving the oven and being
under vacuum is important. The time needed for this operation should be considered in the engineering
phase and should not be underestimated by those producing a new design for the first time.
3.3.6
Insulating Materials
Thermally upgraded paper and aramid-based materials can be used either for complete windings or
selected areas of a winding that will be subjected to higher thermal stress [19].
Mineral oil has been used as an insulating liquid for longer than a century due to its low cost and
excellent cooling and insulating properties. Its suitability, together with pressboard insulation, is well
proven for up to UHV transformers. Its viscosity is low resulting in a higher flow rate which is crucial
for naturally cooled transformers.
Natural esters (vegetable oils)
These liquids have a higher flash point temperature and improved biodegradability compared to mineral
oil. Their heat conductivity is higher than that of mineral oil, compensating to some extent their inferior
cooling properties due to high viscosity. These cooling properties are also considered in IEC 60076-14,
which allows an oil temperature rise 20 K higher than for mineral at rated load [19]. It should be
mentioned that the use of esters requires special attention to winding thermal design because of the
high viscosity.
Water solubility in them is many times higher than that in the mineral oil, giving higher ageing stability
because of reduced water content in the cellulose insulation. Their main shortcomings are a purchase
higher cost than mineral oil, accelerated oxidative ageing if exposed to atmosphere over long periods,
special considerations for lower temperatures and the need for separate handling and additional
processing equipment.
Synthetic esters
A high flashpoint temperature, good thermal stability, anti-oxidation properties, good biodegradability,
long life are the main advantages of synthetic esters. The main disadvantage is upfront cost, compared
to other fluids.
Silicone liquid
This liquid gives an excellent thermal performance. It has a high flashpoint temperature and hence it
is used in traction transformers and applications where compact design and high operation
temperatures are expected.
Its main disadvantages are poor biodegradability, high cost and inferior cooling due to high viscosity.
The disadvantages of esters and silicone are that they need to be replaced after ageing and the
experience is very limited for high voltage application.
27
TB 939 - Analysis of AC transformer reliability
3.3.7
Acceptance Testing
Following the manufacture of any new transformer or a significant refurbishment, it is important to
carry out through testing to ensure the agreed acceptance criteria have been achieved, in addition the
first unit in any series should undergo type testing. Tools such sweep frequency response analysis
(SFRA) can provide a fingerprint prior to transportation. The ongoing consistency of transformers can
be ascertained by comparing their mutual SFRA results in order to detect winding faults [32].
The frequency domain spectroscopy (FDS) and polarization and depolarization current (PDC) method
are non-destructive insulation testing methods for diagnosing the moisture content of high-voltage
equipment. During the manufacturing process, the moisture content is measured [33].
3.3.8
On-site manufacturing technologies
Due to the large size and weight of the UHVAC transformers, which are now in commercial operation
in China, on-site assembly was employed to ensure safe and reliable transportation. On-site assembly
was carried out in a temporary factory and mobile kerosene vapor phase drying equipment was used
to remove moisture from the active parts [34].
In Japan, the omission of site tests (withstand voltage test and temperature rise test) has been
considered and applied up to 500 kV transformers, and even more widely to field-assembled
transformers. Unified quality control items have been clarified and reliably controlled, which has enabled
the omission of field tests, rationalization of on-site verification tests and cost reduction [35]. On-site
refurbishment of 16 transmission transformers was carried out at transmission service providers in
Australia between 2014 and 2019. The main experiences of this retrofit were reported. Specific
problems found during the refurbishment included silver corrosion of selector switches inside the OLTC,
rupture of the conservator bag, difficulties in replacing the 330 kV bushing and checking the residual
winding clamping pressure [36].
On-site replacement of a damaged OLTC in a 40-year-old 250 MVA, 400 kV transformer with no winding
degradation was reported. A drying process with lubricant and vacuuming was carried out on site to
remove water from the winding insulation generated during this work. Induced voltage tests up to 110
% Un were carried out using a high frequency mobile generator to check the insulation condition of the
windings and no specific PD source was identified. Detailed preparation of the site work, highly skilled
engineers and technicians and insulation condition assessment equipment were essential elements [37].
During on-site repair, large quantities of water may be introduced into the cellulose, which must be
removed. Low-frequency heating (LFH) technology, where low-frequency power is supplied to the highvoltage windings of a transformer by shorting the secondary windings, can significantly reduce the
moisture content of the cellulose in the transformer. With this method, the low applied frequency (low
mHz value) means that leakage flux is negligible compared to that at power frequency, and highly
uniform winding temperatures can be achieved. In the case carried out with HVDC transformers, the
cores and coils were exposed for several weeks, but the LFH technology reduced the moisture content
of the cellulose to less than 0.5 %, after which a full field induction test was carried out [38].
3.3.9
On-site testing
As transport and commissioning place high stresses on transformers, on-site testing during
commissioning ensures the functional integrity of the transformer and provides a basic set of functional
data for asset and life management. In addition, in the case of external or internal failures of
transformer components such as bushings, on-site testing gives clear instructions for on-site repairs.
Conducting on-site tests can therefore help to plan repair measures in advance and minimize costs and
downtime. The technical requirements for on-site testing system for power transformers after
commissioning and the appropriate solutions for testing are described [39] and [40].
While it is routine to carry out high voltage (HV) testing at the transformer factory, there are now
techniques to carry out the same HV tests on site where the transformers are installed. There are many
instances where it is beneficial to be able to carry out HV testing on site, such as confirming internal
transformer repairs or winding replacement, diagnosing transformer outgassing, assessing potential
damage to transformers following a fault, transport accident or earthquake, assessing transformer
condition and checking the survival of old spare transformers.
If transformer damage occurs in the tests, the extent of the damage is more limited than in the case
of a grid accident. Thus, avoiding catastrophic damage to transformers and connected equipment and
28
TB 939 - Analysis of AC transformer reliability
reducing risk is one of the greatest benefits of HV testing. The mobile equipment used for HV testing
was described and cases were reported where it contributed significantly to confirming transformer
repairs [41].
Sensitive on-site partial discharge tests can be performed by unconventional PD measurement, such as
acoustic and electro-magnetic ultrahigh frequency (UHF) measurements. These methods have proven
to be sensitive in detecting PD and can assist in determining the location of PD faults. Compared to
electrical measurement methods, the UHF method is more immune against external noises and
disturbances in most on-site applications. Hence, it allows an easier differentiation of external and
internal PD. This makes the UHF method suitable for both, its use in the manufacturer’s test laboratory
(low ambient noise with e.g., shielding and noise blocking efforts) and on-site after transportation and
installation of the transformer (usually elevated ambient noise level) [42].
3.4 Operation
Specifications for new transformers or any refurbishment reflect the expected operational requirements.
This was simpler in the past when historical information could be relied upon as the basis for future
needs. Considering the long-life expectancy of power transformers relative to the rapid changes in grid
operations, this is increasingly difficult and there is greater potential for transformers to be operated in
excess of their original specification.
The failure rate of new or refurbished transformers is very low, but not zero. Before putting a new or
refurbished transformer into operation, it is recommended to record a fingerprint of the transformer in
its installed condition with the SFRA test. Furthermore, it is recommended to plan for shorter time
intervals between inspections for the first 6 months of operation.
Electrical power grids are the infrastructure most impacted by the needs of decarbonization, which
combined with the life cycles needed for investment and long asset lives results in close coordination
being required between operations and maintenance organisations. This should also be reviewed when
adding large semi-conductor-based loads, generation capacity, or large applications requiring frequent
start-stop or disconnection.
Changing load profiles can in part be due to changes in weather, where high temperatures have a
compounding effect of higher loading requirements at a time of high ambient temperatures. If
continued over long durations they can have a negative effect on not just the ageing of the main
insulation, but also limit potential overload capacity of other key components such as tap changers and
bushings. For extremely low temperatures, special attention and considerations may be necessary
depending on the insulating fluid type.
Gas-in-oil generation is an indicator of transformer overloading. This can be trended and interpreted
through Dissolved Gas Analysis (DGA) [43], [44], [28]. The oil sampling interval is often dependent
upon the actual condition of the transformer. While samples taken and analysed in a laboratory remain
valuable, they reflect a snap-shot in time and dependent upon the skill level of those taking the sample
[43], how the sample is transported, and how quickly the fluid can be analysed and interpreted. While
not recommended to completely replace the taking of physical oil samples, online DGA analysers provide
both the option for continuous (many samples per day) monitoring and provide industry standard
interpretation of the combination of gases and the potential to warn of accelerating rates of change.
Transformer overloadability is impacted by the moisture level of the insulation system [45]. As the
cellulosic insulation heats up it reaches a threshold temperature when bubbles of steam are generated,
which can cause a dielectric failure across the oil gap. A wet transformer with a water content of 3 %
by weight of dry insulation can have a bubbling inception temperature as low as 130 °C. Other potential
indicators of reduced available performance include but are not limited to higher-than-expected
operating temperatures, differences in the temperature of the main tank and OLTC tank fluids, slow
operation of the OLTC drive mechanism and changes in the bushing capacitance or dissipation factor.
3.4.1
Monitoring and Digital Innovation
Failures are generally considered undesirable; catastrophic failures bring unpredictability and expensive
consequences. While the ageing of materials and components at differing rates is relatively well known,
less well understood is the changing nature of both generation and loading profiles and the impact
these are having on power transformers and their critical component parts. As the electricity grid
becomes increasingly dynamic it is also increasingly important to better observe a wider cross section
29
TB 939 - Analysis of AC transformer reliability
of a transformer fleet. CIGRE TB 248 2004 Economics of Transformer Management [46] introduces the
principles of investing in online monitoring. As of 2023, these basic principles remain true. However,
the use of digital technologies, such as online and remote monitoring, are now much better placed to
significantly enhance existing asset management practices and in some cases make the difference
between identifying a problem while still repairable and a catastrophic failure. In addition to providing
insights into the changes occurring inside of the transformer tank, online monitoring can provide near
real time trending of multiple key parameters, enabling comparison between parameters on a single
transformer and between transformers in either the same sub-station or multiple substations, across a
whole fleet. Modern electronics, processing power and software tools facilitate the visualisation of
combinations of multiple data sources such as temperatures, loading, DGA, bushings and tap changers.
The ability to compare key parameters and provide trending is several orders of magnitude higher than
what can be achieved by yearly, quarterly, or even weekly inspections alone. Remote monitoring can
also reduce the need for personnel to travel to the site, and state of the art monitoring systems further
allow the importing of offline data such as standard oil reports. Data exchange with other software
tools can provide prognostic insights, to indicate the required time horizon for maintenance and
corrective actions, and sufficient oversight for asset or operations managers to prioritize conditionbased maintenance over time-based requirements.
The monitoring of temperatures and the calculated hotspot are fundamental to ageing and remaining
life calculations. It is therefore valuable to start doing this as soon as possible. The use of fibre optic
probes is also increasing; however, it is typically only economical to insert these probes when
manufacturing a new transformer or as part of a major refurbishment. Other areas worth considering
at the design and specification stage is the use of communications capable instruments and selfdehydrating breathers, which typically are enhanced versions of the traditional purely electromechanical devices. As the grid dynamic is changing, together with the increasing demand for
electricity, transformers are expected to be utilized more using new sensing technologies. The
heightened level of monitoring and operation information will be of use to domain experts providing
remote services and a prerequisite for any future artificial intelligence ormachine learning based tools,
which require as many data points as possible.
Digital innovation is gathering pace, however monitoring systems should not be considered fit and
forgotten, they require correct application and configuration to prevent false alarms. When retrofitting,
it is recommended that online solutions are offered following a fleet condition assessment, and systems
are offered based on where they can demonstrate the greatest impact on the system reliability or
operational efficiency. Transformer online monitoring systems can typically provide on-asset data
acquisition, analysis, storage and visualisation; however, they provide significantly more value when
connected to upstream systems and software tools, these can take the form of substation-level
condition monitoring, SCADA or Asset Performance Management (APM) tools. As transformers play
such a significant role in grid availability, the greatest value is when transformer data is integrated into
enterprise level APM tools covering all key substations and asset types. Power grids are also increasingly
exposed to extreme weather conditions, which as a minimum can impact the assets’ ageing. Following
an extreme weather event, connected assets can enable operators to better visualize their entire
enterprise and prioritize resources.
Connectivity, whether within a sub-station, an enterprise or to an external 3rd party provider, presents
its own risks in that each connection point is a potential exposure to bad actors and cyber threats.
Cyber security should now be considered a license to operate and apply relevant product standards
such as IEC 62443-4-2 [47] and IEEE 1686 [48] should be considered as the minimum requirement at
the transformer - asset interface level.
Connectivity is increasingly important for those wishing to move to condition-based maintenance,
optimise power network operations, or catch developing problems at the earliest possible stage.
Assuming the transformer monitoring information can be passed to higher level systems, this allows
the asset owner to benefit from the support of Domain Experts located in other countries, and the use
of tools such as Augmented Reality and Digital Twins. All the above are critical to power infrastructure
operators who are increasing facing a brain drain, losing many of their most experienced staff, while
facing the challenges of integrating volatile generation sources and changing load profiles.
Digital tools and innovation have been applied to the engineering process for many years. The
manufacturing of power transformers, which are low volume items and very labour intensive, can
30
TB 939 - Analysis of AC transformer reliability
however also benefit from digital technologies. These can range from ensuring the manufacturing teams
have the up-to-date information to a full ERP (Enterprise Resource Planning) & MES (Manufacturing
Execution Systems). As a minimum all manufacturing locations should have a centralised quality control
system in place.
3.5 Maintenance
The maintenance performed on a transformer during its time in service will significantly and directly
impact the reliability of the transformer. Well performed and regular maintenance may detect emerging
issues in the transformer early enough to prevent a failure. A brief discussion on maintenance is given
below. A more thorough and deeper discussion on maintenance can be found in CIGRE TB 445 [30]
and IEEE C57.93-2019 [49].
The manufacturer’s recommended maintenance (usually found in the transformer instruction book)
should be used at a starting point guide for maintenance planning.
Generally, there should be more focused and routine maintenance for the OLTC. The manufacturer can
provide a detailed maintenance schedule based on time and operations that is specific to the OLTC
model.
Capacitance graded bushings should be tested regularly to detect any dielectric changes.
Transformer coolers and pumps require more frequent maintenance and overhaul to ensure they
remain in excellent working condition.
Routine electrical testing of the transformer and thorough investigation of all testing discrepancies can
prevent a transformer failure. Today there are many advanced test methods including SFRA, FDS and
Leakage Reactance to assist in transformer diagnosis.
Regular oil sampling for dissolved gas analysis (DGA) and physical properties has become standard in
the industry. There are numerous industry standards and references on interpretation and operational
limits.
It is vital that every transformer has an inventory of critical spare bushings and accessories. When a
bushing or accessory issue is found, the downtime to replace the component can be minimized (versus
sourcing the component which may have several months lead-time).
Since transformers have been shown to last > 50 years, it is very important to have well established
maintenance procedures. This can ensure that transformers remain reliable throughout generations of
maintenance staff.
Critical transformers can benefit from an expert condition assessment by the transformer OEM or
another transformer expert. Recommendations on specific maintenance from the expert condition
assessment can assist to extend critical transformer reliability. See also CIGRE TB 761 [50] for detailed
information on transformer condition assessment.
Transformer reliability can be vastly improved by performing a planned mid-life refurbishment (30 – 40
years) that may include bushing replacement, oil upgrade, protective accessory replacement, leak
repairs, complete electrical testing and transformer dry out. In North America in particular, there is a
large industry push to significantly extend transformer life due to the very large, aged, transformer
installed base. While this mid-life refurbishment is a large cost, it is far less than transformer
replacement or the cost of a transformer failure.
Critical transformers can also benefit from monitoring such as the gas in oil, bushings, cooling, and
partial discharge.
Below is a generic and simple recommended maintenance plan for transformers:
Recommended Monthly Maintenance (energized):
•
•
•
•
Visual inspection of the transformer
Record winding and liquid gauge temperatures including maximum since last reading
Check pressure vacuum gauge (if sealed unit)
Compare to last month’s results
Recommended Quarterly Check (energized):
31
TB 939 - Analysis of AC transformer reliability
•
•
•
•
•
•
•
Do monthly-check items
Check liquid level of the main tank (and all oil-filled compartments)
Check bushing liquid level with camera or binoculars
If sealed oil preservation system - check tank pressure and N2 bottle
Examine coolers/radiators for dirt accumulation - clean if necessary
Inspect control cabinet for damage, excess heating, corrosion, loose connections, moisture
etc.
Compare to last quarter’s results
Recommended Major Maintenance (every 3 – 8 years) (planned -outage):
•
•
•
•
•
•
•
•
•
•
•
•
•
Do quarterly checks
Check oil pumps – for noise, overheating, flutter of oil gauge etc
Check fans – check for debris, all fans operational, correct rotation is correct, stages
operate, check line currents
Check pump valves – check for leaks and proper open/close operation
Take oil sample from transformer and tap changer compartment (for DGA and oil physical)
Insulation resistance test
Insulation power factor
Bushing Power Factor - examine & clean all bushings, arresters, hardware
OLTC inspection and test
Inspect breathers and any screen openings (PRD etc.)
Check air bladder for oil leakage
Tank inspection for welded joints, piping, rust, leaks
Clean control cabinet interior as needed
32
TB 939 - Analysis of AC transformer reliability
4
Interpretation of Life Data
4.1 Reliability Life Data Analysis
Life Data Analysis (LDA) is a statistical and engineering technique used to analyse and interpret data
related to the life or reliability of a product, system, or component. It is commonly applied in the field
of reliability engineering and is particularly useful in assessing the reliability and durability of equipment,
devices, and systems over time. A very early instance of statistics being used to model failure was army
officers falling off horses [51].
The primary focus of Life Data Analysis is to understand and model the distribution of time-to-failure
or time-between-failures for a given system. This involves collecting data on the occurrence of failures
or events, such as component malfunctions, and then analysing the patterns and trends in the data to
make predictions about the reliability of the system. The key concept is the analysis of life data with
the help of statistical distributions.
For this brochure, both Microsoft Excel and the Reliability toolbox in Python [52] have been used to
analyse data. These methods have subtle differences in their approach, which was considered during
the evaluation. The Excel spreadsheet method followed the IEC 61649 standard for applying the Weibull
distribution [53]. The term data censoring refers to when the asset still has not failed. For instance, at
the end of this survey most transformers had not failed, and therefore this data has been censored.
4.2 Non-parametric Method
Hazard rate 𝜆 was calculated, for each year 𝑖, using the number of failures, retirements and survivors,
Equation 4.1 and 4.2. A failure or retirement was counted when the transformer was subsequently
scrapped assuming that the transformer has really reached end of life in this case. Repairable failures
were not counted. Sometimes, a utility would keep a retired transformer as a spare which was also not
counted.
Data was collected for failures, retirements and service-years for operating transformers from 2010 to
2019 inclusive, a ten-year period. Any transformer failing before 2010 was obviously not recorded by
this survey.
𝜆(𝑖) =
𝑛 𝑓𝑎𝑖𝑙𝑢𝑟𝑒𝑠(𝑖)
𝑛 𝑠𝑢𝑟𝑣𝑖𝑣𝑜𝑟𝑠(𝑖)
Equation 4.1
And
𝜆𝑅𝑒𝑡𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑠 (𝑖) =
𝑛 𝑟𝑒𝑡𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑠(𝑖)
𝑛 𝑠𝑢𝑟𝑣𝑖𝑣𝑜𝑟𝑠(𝑖)
Equation 4.2
To calculate the rate of failure for transformers in the first year of operation (Table 1):
1. Note the number of transformers reported to have failed (column D).
2. The number of survivors includes the censored units in column C. Only censored units
for the last ten years were counted because of the left-truncation of the survey only
counting from 2010 onwards.
3. Failures or retirements surviving their first year were also counted.
Therefore, when referring to the columns and rows in the table:
𝐹1 =
𝐷1
𝑆𝑢𝑚(𝐶1: 𝐶10) + 𝑠𝑢𝑚(𝐷2: 𝐷10) + 𝑠𝑢𝑚(𝐸2: 𝐸10)
Equation 4.3
33
TB 939 - Analysis of AC transformer reliability
Table 1: Determining failure and retirement rates.
A
B
C
D
E
F
G
Acquisition year
Age
Censored
Failures at
age t
Retirements
at age t
Failure rate
Retirement rate
1
2019
1
0
0
0
0.00000
0.00000
2
2018
2
0
0
0
0.00000
0.00000
3
2017
3
0
2
0
0.01481
0.00000
4
2016
4
0
1
0
0.00559
0.00000
5
2015
5
3
1
0
0.00442
0.00000
6
2014
6
10
0
0
0.00000
0.00000
7
2013
7
15
0
0
0.00000
0.00000
8
2012
8
20
0
0
0.00000
0.00000
9
2011
9
16
0
0
0.00000
0.00000
10
2010
10
16
0
0
0.00000
0.00000
11
2009
11
25
0
0
0.00000
0.00000
12
2008
12
28
0
0
0.00000
0.00000
13
2007
13
43
2
0
0.01361
0.00000
4.3 Weibull distribution
The probability density function (PDF) 𝑓(𝑡), and cumulative distribution function (CDF) 𝐹(𝑡) are given
respectively in Equations 4.4 and 4.5, where t is time and 𝛽 and 𝜂 are the shape and scale parameters
of the distribution. In order to also account for censored data, the ranks of the failures were adjusted
using the method proposed in [53].
𝑡𝛽−1 −( 𝑡 )
𝑓(𝑡) = 𝛽 ∙ 𝛽 ∙ 𝑒 𝜂
𝜂
𝛽
Equation 4.4
𝐹(𝑡) = 1 − 𝑒 −(𝑡⁄𝜂)
𝛽
Equation 4.5
Shown in Figure 13, if 𝛽 = 1, the hazard rate will not depend on time. For values of 𝛽 larger than 1 the
failure probability will increase with time. In case 𝛽 larger than 2 a gradually increasing failure
probability will occur. So, an age limit for the operation of the components would be advisable. If 𝛽 ≈
3.7, the hazard rate will follow the normal distribution.
34
TB 939 - Analysis of AC transformer reliability
h(t)
Figure 13: Shapes of the Weibull Hazard Function [54].
If the Weibull distribution applies then the data will follow a straight line, when the failures are plotted
on a logarithmic scale. If they do not, then more than one distribution may be present. For instance,
in Figure 14 the failures did not follow one straight line, and they seemed to fit into three groups (infant,
random and aged) [55]. Some of the ‘aged’ population failures will still be random (since this distribution
is unbounded), which is not differentiated between by this method. However, the Reliability toolbox in
Python can fit a competing risk model to the data and derive coefficients for the two distributions. Both
methods were used in this working group to check for consistency.
The Python Reliability package [52] was used to fit the Weibull distribution to the data, and then provide
the line parameters for shape and scale. Once the shape and scale are known, then the instantaneous
failure rate can be plotted.
Figure 14: Analysis of example data using Weibull distributions (x-axis: natural logarithm of age)
[55].
The 𝛽 parameter is the line gradient, and the characteristic life 𝜂 is:
𝜂 = exp (−
𝑦𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡
)
𝛽
Equation 4.6
The 𝛽 value provides information on the failure rate. If it is less than one, the failure rate is falling for
instance in infant mortality. A value of one indicates a constant failure rate, i.e. random failures. A value
greater than one indicates that the failure rate is increasing, for instance wear-out. For Figure 14, the
random distribution has a 𝛽 very near one, and the aged distribution has a higher 𝛽.
35
TB 939 - Analysis of AC transformer reliability
The characteristic life 𝜂 is the age when 63.2 % of units have failed. While taking the ± 95 % confidence
limits into account, this life can be compared across transformer fleets. The mean time before failure
can be calculated in Microsoft Excel using the gamma function 𝛤 along with the β and η parameters:
𝑀𝑇𝐵𝐹 = 𝜂𝛤(1 + 1⁄𝛽 )
Equation 4.7
The hazard rate is:
λ(t) = 𝛽 ·
𝑡 𝛽−1
𝜂𝛽
Equation 4.8
Using the coefficients shown in Figure 14, β = 3.5851 and η is 107.27 which is calculated using Equation
4.6, the hazard rate is shown in Figure 15.
1.2%
Hazard rate (%)
1.0%
0.8%
0.6%
0.4%
0.2%
0.0%
0
10
20
30
40
50
60
Age (years)
Figure 15: Hazard rate for coefficients in Figure 14.
36
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TB 939 - Analysis of AC transformer reliability
5
Methodology for Failure Data Collection
In this chapter, the data collection methodology which was developed and updated by the working
group is presented and discussed. A uniform way of collecting, compiling and presenting data was used
in this survey.
5.1 Definition of Failure and Retirement
Based on the experience of working group A2.37, the decision was taken to limit the data collection to
only major failures of transformers and reactors operating at 100 kV and above in this study.
Additionally, to the previous working group analysis, retirement data was also collected for this work.
A transformer may be retired before it actually fails, therefore not be included in a failure list, and so
the failure rate calculation may underestimate the number of transformers which have failed
functionally. The retirements were also used to calculate hazard curves by being included as
suspensions.
In general, a major failure was defined as any situation which required the transformer to be removed
from service for longer than a week for investigation, remedial work or replacement. The necessary
repairs could have involved major remedial work, maybe requiring the transformer to be removed from
its installation site and returned to the factory. Also, a major failure would require at least the opening
of the transformer or the tap changer tank, or an exchange of the bushings. A reliable indication that
the transformer condition prevents its safe operation is considered a major failure as well. In some
cases, failures were assigned as major if remedial work was shorter than one week but extensive work
with oil processing had to be done (e.g. change of bushing).
For the enhanced analysis performed by WG A2.62, the transformer retirements data was also collected
and analysed. Two types of retirements are distinguished in this report: retirement due to condition/age
and, retirement due to network requirement. In the first case, the transformer is retired from service
before it fails, after detection of a condition that prevents from safe or reliable operation. A controlled
outage combined with scrapping is the typical scenario for this type of retirement, and in practice this
would be the desired end of life of a transformer. For the second type of retirement, the transformer is
retired without a real concerning issue, but due to major network upgrades like voltage level, rated
power or loss requirements; the unit does not have necessary to be scrapped, as it could still be used
as a spare unit or back-up.
5.2 Reliability Questionnaire
The Excel spreadsheet tool proposed by CIGRE WG A2.37 was updated to collect data in a standardized
way and in accordance with the definition of major failure in section 5.1 [2]. The spreadsheets are
given in Appendix A.
The first section of the questionnaire requests general information about the utility and the population
of the operating transformers for the indicated failure period. The population information should consist
of number of operating transformers dependent on transformer application and voltage class.
As knowledge of the age distribution of the transformer population allows the determination of the
hazard function, this information was of great interest for the evaluation of the failure data (section
two).
The third section captures the transformer failure and retirement data grouped into four categories as
follows:
▪
▪
Identification of the unit: application, year of manufacture.
Features of the unit: rated power, nominal voltage, number of phases, cooling system, type of
▪
▪
tap changer, type of bushing.
Detail of occurrence: year of failure, service years to failure, reason for disconnection.
Consequences of failure: external effects, failure location, failure mode, failure cause, action,
and detection mode.
A pull-down menu for each field suggests the possible answers and ensures that the answers are given
in a standardized way.
37
TB 939 - Analysis of AC transformer reliability
5.3 Classification of Failures
Failures were classified into failure location, failure causes and failure modes.
Failure location referred to the primary location (component) in the transformer where the failure was
initiated, and was classified as:
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Winding
Tapping Winding
Lead Exit
Tapping Leads
Phase to Phase Insulation
Winding to Ground Insulation
Winding to Winding Insulation
Electrical Screen
Bushings
Tap Changer
Core and Magnetic Circuit incl. Clamping
Flux Shunts
Tank, Conservator, Piping
Cooling Unit, Pump, Fan, Radiator
Tap Changer
Internal Surge Arresters
Tank
Current Transformer
Unknown
Failure cause referred to the cause of failure in the primary location where the failure was initiated, and
was defined as the circumstances during design, manufacture or application that led to the failure.
Failure causes were classified as:
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Design, Manufacturing or Materials
Transportation, Handling, or Storage
Loss of Clamping Pressure
Installation on Site
Improper Maintenance
Improper Repair
Abnormal Overload
Overvoltage
Overheating
Lightning
External Short Circuit
Repetitive Through Faults
Seismic Disturbances
Improper Application
External Pollution
Loss of Cooling
Vandalism
Abnormal Deterioration
Ageing
Collateral Damage
Unknown
Failure mode refers to the manner in which a failure occurred, and was categorised as electrical,
thermal, mechanical or contamination. The description of the ‘nature of failure’ as used in CIGRE WG
A2.18 [56] is similar to this one. The nature of failure and failure mode will thus be considered as being
38
TB 939 - Analysis of AC transformer reliability
equivalent, referring to the mode/nature of failure in the location where the failure was initiated. Thus,
failure mode was classified as:
▪
▪
▪
▪
▪
▪
Dielectric (Partial Discharge, Tracking, or Flashover)
Electrical (Open Circuit, Short Circuit, Poor Joint, Poor Contact)
Thermal (General Overheating, Localised Hotspot)
Physical Chemistry (Contamination (Moisture, Particles, Gas), Corrosion)
Mechanical (Bending, Breaking, Displacement, Loosening, Vibration)
Unknown
5.4 Data Collection and Limitations
5.4.1
Data collection
The methodology of failure data collection was done in a similar way as in the previous CIGRE working
group, A2.37 [2]. In addition to failure data, the retirement data was also collected for those units that
were removed in a controlled way, without tripping or failure events. The members of the Working
Group A2.62 approached the main utilities within their regions to invite them to participate in the data
collection process by providing their anonymous transformer fleet data, failure and retirement records.
The utilities which participated in this data collection process completed the Excel questionnaire given
in Annex A, and the responses were monitored by each of the leading Working Group member for each
utility [57]. Special attention was paid to the correct data entry, like the proper time frame when the
major failures occurred, the proper voltage level, the details about occurrences, the consequences of
failures and the retirement reasons. All the responses were compiled into a general database for
analysis.
To achieve data security and anonymity, the failure and retirement data from each source was made
anonymous by labelling with a code based on the geographical location and a sequence number.
Information about the transformer manufacturer was not collected.
5.4.2
Data Limitations
The accuracy of the outcomes of this Technical Brochure is directly linked to the quality of the data
collected by the survey. It was the objective of this WG A2.62 to compile more accurate and reliable
data compared to the previous reliability analysis performed by WG A2.37. For that reason, the data
collection process took longer than initially estimated due to the need to account for the volume and
quality of reliable data that could be used to build robust and realistic conclusions.
In addition, more details were requested to the utilities about the transformer fleet, like transformer
year of manufacturing and controlled transformer retirement details, which enable the Working Group
to obtain more useful conclusions after the data analysis stage. Although the requested data was
enhanced, and the most significant transformer information with regards to its characteristics and
failure or retirement details was compiled, there was still an important portion of information that was
not collected. Such as: the short circuit impedance value, the transformer technology (shell or core),
the use of transformer monitoring devices or the type of operation with regards to overloads, switching
operations and short circuit events. This limitation cannot be avoided if the data collection process is
to be simplified, so that utility engineers will participate in this initiative. Excessive data requests could
have resulted in utilities being unable to collaborate with the Working Group.
Regarding gathered transformer information, special attention was paid by the Working Group members
to help utilities understand the questionnaire requests, and to review the provided data to look for data
inconsistencies which could be corrected or even discarded. It is assumed that a minor portion of the
collected data might not be 100 % correct or may present some lack of accuracy, which should not
jeopardize the conclusions of this technical brochure as evaluated by the Working Group.
Finally, one of the main data limitations that was observed is that not all the countries or continents
participated on the data collection initiative equally. Much presence of European, Asian, North American,
and Oceanian utilities was noted, while African and South American utilities did not have such a
representative presence.
39
TB 939 - Analysis of AC transformer reliability
6
Results of Performed Reliability Survey
6.1 Investigated population
In total, 425,000+ transformer-years of operation was collected, along with 1,204 major failures and
1,916 retirements from 66 utilities, with the countries shown in
Figure 16. The reference period, i.e. the time interval of the recorded failures, was a maximum of 34
years. While the survey requested data from 2010 onwards, some utilities provided failures going back
further. The entire reference period was always considered in the analysis.
•
•
•
•
•
•
•
•
Number of major failures = 1,204
Number of retirements= 1,916
Number of transformers = 37,104
Total population = 425,294 transformer years
Number of utilities = 66
Number of countries = 27
Year of manufacture = 1919 - 2020
Reference period = 5 - 34 years
Figure 16: Countries supplying data to survey.
Table 2 represents the transformer’ population and failure data according to voltage class. Among the
collected data, 65 % of the transformer population had a system voltage in the range 100 ≤ U < 200
kV. The number of transformers decreases as the voltage class rises. Only six participating utilities had
transformers with voltage rating above 700 kV, comprising 0.68 % of the total population.
Figure 17 represents the age profile of transformers installed during 1926-2022. A significant proportion
of the transformer population is newer than 20 years. Only a small percentage of the transformers has
an age above 50 years.
40
TB 939 - Analysis of AC transformer reliability
Table 2: Transformer population data stratified by voltage class.
Population
Information
Highest System Voltage [kV]
No of utilities
100≤
200
U
<
200 ≤ U <
300
300 ≤ U <
500
500 ≤ U <
700
U ≥ 700
Total
60
54
39
16
6
66
No of
transformers
24,106
6,619
4,929
1,194
256
37,104
Transformeryears
268,887
86,881
51,506
15,561
2,459
425,294
No of failures
619
247
228
74
36
1,204
Failure rate
0.23 %
0.28 %
0.44 %
0.47 %
1.46 %
0.28 %
No of
retirements
1,153
422
194
76
71
1,916
1000
Transformers
800
600
400
200
0
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
Age (Years)
Figure 17: Age profile for in-service transformers.
The participating continents with number of transformers, transformer years and percentage of
transformer years are shown in Figure 18 and Figure 19 respectively. Transformer years are calculated
under the assumption that the total number of operational transformers and shunt reactors remains
the same during the reference period. Transformer years for each utility/country are obtained by
multiplying the number of transformers by the length of the reference period (in years) and shown in
Table 3. Most transformer years were contributed by Asia (34 %), followed by Europe (30 %) and
America (27 %). The contribution from Oceania (Australia and New Zealand) and Africa (South Africa)
to total transformer years was 7 % and 2 % respectively.
41
16000
14000
12000
10000
8000
6000
4000
2000
0
160000
140000
120000
100000
80000
60000
40000
20000
0
Europe
Asia
America Oceania
Continent
Transformers
TransformerYears
Transformers
TB 939 - Analysis of AC transformer reliability
Africa
Transformer Years
Figure 18: Population and transformer-years.
Africa
2%
Oceania
7%
Europe
30%
America
27%
Asia
34%
Figure 19: Percentage of transformer years by participating continents.
42
TB 939 - Analysis of AC transformer reliability
Table 3: Investigated transformer population.
Country/Utility
Number of transformers
Transformer-years
Austria
249
2,490
Belgium
1,457
8,742
Czech Republic
119
1,190
Denmark
70
700
France
1,272
25,440
Germany
1,347
13,733
Ireland
412
8,652
Italy
1,129
11,290
Kosovo
89
1,602
Latvia
301
3,311
Poland
227
2,270
Romania
303
3,030
Spain
2,686
30,058
Sweden
8
80
UK
1,351
13,911
Europe Total
11,020
126,499
Australia
2,140
24,692
New Zealand
460
4,191
Oceania Total
2,600
28,820
South Africa
1,225
10,325
Africa Total
1,225
10,325
Indonesia
2,125
27,625
Israel
640
13,440
Japan
2,012
18,030
South Korea
8,424
58,968
Malaysia
1,534
15,495
Thailand
840
12,600
Asia Total
15,575
146,158
Brazil
505
5,430
Canada
2,005
19,908
USA
4,174
87,654
America Total
6,684
113,492
43
TB 939 - Analysis of AC transformer reliability
Table 4 to 6 represent the investigated population for different transformer types. In the investigated
data, six utilities did not provide any information about the transformer’s application. Because of the
nature of these utilities these units are considered to be substation transformers.
The surveyed population primarily comprises substation transformers, accounting for 96 % of the total.
Shunt reactors and generator step-up transformers contribute 2.3 % and 1.7 %, respectively.
Table 4: Investigated population of substation transformers.
Population
Information
Highest System Voltage [kV]
100≤ U <
200
200 ≤ U <
300
300 ≤ U <
500
500 ≤ U <
700
U ≥ 700
Total
No of
transformers
23,654
5,988
4,551
903
196
34,855
Transformeryears
264,375
81,833
47,313
11,556
1,814
406,891
Table 5: Investigated population of shunt reactor transformers.
Population
Information
Highest System Voltage [kV]
100≤ U <
200
200 ≤ U <
300
300 ≤ U <
500
500 ≤ U <
700
U ≥ 700
Total
No of
transformers
238
235
285
138
60
956
Transformeryears
2,648
2,218
3,123
1,052
645
9,686
Table 6: Investigated population of generator step-up transformers.
Population
Information
Highest System Voltage [kV]
100≤ U <
200
200 ≤ U <
300
300 ≤ U <
500
500 ≤ U <
700
U ≥ 700
Total
No of
transformers
176
218
309
153
0
856
Transformeryears
1,538
1,500
1,568
2,953
0
7,559
6.2 Failure and Retirement Rates
The failure rates are calculated using the number of failures and transformer years with the assumption
that the number of in-operation transformers remains constant during the reference period. The full
failure population (1,204 failures) was used in this analysis. Retirements were divided into retirements
due to condition/age, i.e. transformer is retired/scrapped after detection of a condition that prevents
safe or reliable operation (e.g. bad DGA or partial discharges), and due to network requirements, i.e.
transformer is retired/scrapped because network requires new characteristics (e.g. different voltage
level, rating, losses, noises).
The responses to the survey regarding the number of failures and retirements were heterogeneous.
Figure 20 shows the rates of failure and retirement due to condition/age for individual operators. With
the help of the failure rate, an initial check of the consistency of the data supplied was carried out. If
the failure rate of a larger population was below 0.1 %, even after inquiry, the data was discarded
because probably not all failures were reported.
44
TB 939 - Analysis of AC transformer reliability
4,00%
3,50%
3,00%
Rate
2,50%
2,00%
1,50%
1,00%
0,50%
0,00%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65
Age (years)
Failure Rate
Retirement Rate
Figure 20: Failure and retirement rate of individual utilities.
Figure 21 shows the count of failed and/or retired transformers for the full dataset. Most failures and
retirements occurred in voltage class 100 ≤ U < 200 kV in part due to the larger population. Retirement
numbers are double that of failures up to 300 kV and above 700 kV. Transformers with a voltage rating
300 kV to 700 kV have an equal number of failures and retirements.
800
700
699
619
Transformers
600
454
500
400
300
247
200
282
228
140
137
57
100
74
45
31
36
58
13
0
100 ≤ U < 200
200 ≤ U < 300
300 ≤ U < 500
500 ≤ U < 700
U ≥ 700
Voltage [kV]
Failed
Retired due to condition/age
Retired due to network requirements
Figure 21: Failures and retirements according to voltage class.
The failure rates according to the voltage class for the substation, generator step-up transformers, and
shunt reactor transformers, are given in Figure 22 and Table 7 to 9 respectively. All failure rates are
below 1 %. The failure rate of GSU’s is higher in almost all voltage classes. However, it is important to
note that the number of failures, as well as the transformer population of GSU’s and shunt reactors, is
significantly lower than substation transformers. Thus, the calculated failure rates should be considered
with caution.
The failure rate increases as the voltage class rises. It is highest in transformers with voltage class ≥
700 kV (1.46 %) while lowest (0.23 %) in class 100 ≤ U < 200 kV.
45
TB 939 - Analysis of AC transformer reliability
2,0%
3.1%
Failure rate
1,5%
1,1%
1,0%
0,9%
0,7%
0,5%
0,4%
0,2%
0,1%
0,5%
0,4%
0,3%
0,8%
0,6%
0,5% 0,4%
0,5%
0,3%
0,0%
0,0%
0,0%
100≤ U < 200 200 ≤ U < 300 300 ≤ U < 500 500 ≤ U < 700
Substation transformers
Shunt reactors
U ≥ 700
Total
GSU transformers
Figure 22: Failure rate dependent upon voltage class and application.
2,0%
2,0%
2.15%
5.3%
Retirement rate
1,5%
1,3%
1,1%
1,0%
0,9%
0,6%
0,5%
0,4%
0,3%
0,3%0,3%
0,4%
0,2%
0,3%
0,3%
0,2%
0,1%
0,0%
0,0%
100≤ U < 200
200 ≤ U < 300
300 ≤ U < 500
Substation transformers
500 ≤ U < 700
Shunt reactors
U ≥ 700
Total
GSU transformers
Figure 23: Retirement rate dependent upon voltage class and application.
46
TB 939 - Analysis of AC transformer reliability
Table 7: The failure and retirement rate of substation transformers depending on the voltage
class.
Population
Information
Highest System Voltage [kV]
100≤ U <
200
200 ≤ U <
300
300 ≤ U <
500
500 ≤ U <
700
U ≥ 700
Total
No of failures
610
236
178
55
16
1,095
No of
retirements
659
244
112
37
24
1,076
Transformeryears
264,009
80,675
46,215
11,556
1,814
404,269
Failure rate
0.23 %
0.29 %
0.38 %
0.47 %
0.88 %
0.27 %
Retirement
rate
0.25 %
0.30 %
0.24 %
0.32 %
1.32 %
0.27 %
Table 8: The failure rate of shunt reactor transformers depending on the voltage class.
Population
Information
Highest System Voltage [kV]
100≤ U <
200
200 ≤ U <
300
300 ≤ U <
500
500 ≤ U <
700
U ≥ 700
Total
No of failures
3
1
16
6
20
46
No of
retirements
7
8
11
1
34
61
Transformeryears
2,648
2,218
3,123
1,052
645
9,686
Failure rate
0.11 %
0.04 %
0.51 %
0.57 %
3.1 %
0.47 %
Retirement
Rate
0.26 %
0.36 %
0.35 %
0.1 %
5.3 %
0.63 %
Table 9: The failure rate of GSUs transformers depending on the voltage class.
Population
Information
Highest System Voltage [kV]
100≤ U <
200
200 ≤ U <
300
300 ≤ U <
500
500 ≤ U <
700
U ≥ 700
Total
No of failures
6
10
34
13
0
63
No of
retirements
33
30
14
7
0
84
Transformeryears
1,538
1,500
1,568
2,953
0
7,559
Failure rate
0.39 %
0.66 %
1.1 %
0.44 %
0.0 %
0.83 %
Retirement
Rate
2.15 %
2%
0.89 %
0.23 %
0.0 %
1.11 %
47
TB 939 - Analysis of AC transformer reliability
6.3 Failure Data Analysis
The failure data of the full transformer population, with 1,204 major failures, were analysed as a
function of failure location, external effects, failure mode, failure cause, detection mode, and action
taken after failures. Data from four utilities was excluded because limited information had been provided
by the owner, to retain the high level of data quality used in this analysis.
6.3.1
Failure Mode Analysis
The mode describes the nature of the failure illustrating what had happened. The failure mode analysis
of all transformers according to voltage class is shown in Table 10. Dielectric failure means PD, tracking
and flashover. Electrical failure means open circuit, short circuit, poor joint, poor contact, ground
deterioration and floating potential [2].
Electrical (35.9 %) and dielectric (30.7 %) mode failures were the most prominent, followed by
mechanical (18.8 %) type failures. Electric mode failures were the major contributor in the 100 kV to
199 kV, 200 kV to 299 kV, and 500 kV to 699 kV voltage classes. Whereas dielectric mode failures were
most prominent in transformers with voltage ratings between 300 kV and 499 kV.
Table 10: Failure mode analysis depending on the voltage class.
Failure mode
Highest System Voltage [kV]
100…199
200…299
300…499
500…699
%
%
%
%
≥ 700
Total
%
%
Dielectric
92
24.9
46
29.5
70
44.6
24
36.4
10
31.3
242
31.0
Mechanical
89
24.1
29
18.6
22
14.0
4
6.1
5
15.6
149
19.1
Electrical
136
36.9
56
35.9
44
28.0
37
56.1
10
31.3
283
36.3
Thermal
10
2.7
8
5.1
12
7.6
1
1.5
1
3.1
32
4.1
Physical
Chemistry
13
3.5
5
3.2
2
1.3
0
0.0
2
6.3
22
2.8
Unknown
29
7.9
12
7.7
7
4.5
0
0.0
4
12.5
52
6.7
Total
369
156
157
66
32
Physical Chemistry
3%
Thermal
4%
Unknown
7%
Dielectric
31%
Electrical
36%
Mechanical
19%
Figure 24: Failure mode analysis (780 failures).
48
780
TB 939 - Analysis of AC transformer reliability
Figure 25 shows the failure mode according to the transformer application without considering
‘Unknown’ data given in Table 10. Substation and GSUs transformers had higher contributions of
electrical type failures, whereas shunt reactors had higher contributions of dielectric mode failures.
This data suggest that deeper attention should be paid to the prevention of the electrical and dielectric
failures, which represents about 67 % of total failures analysed, for all type of units: substation
transformers, GSU and shunt reactors. The industry should also focus on reducing the proportion of
unknown-type failures, although it is recognised that after a catastrophic failure this might not be
possible.
50%
40%
30%
20%
10%
0%
Dielectric
Mechanical
Substation transformers
Electrical
Shunt reactors
Thermal
Physical
Chemistry
GSU transformers
Figure 25: Failure mode analysis according to transformer application
(substation 622 failures, shunt reactors 49 failures, GSU 56 failures).
49
TB 939 - Analysis of AC transformer reliability
6.3.2
Failure Location Analysis
The failure location analysis according to voltage class and application is shown in Table 11. It can be
seen that windings, tap changers, and bushing-related faults are still the major contributors for all
voltage classes.
Table 11: Failure location depending on the voltage class.
Failure Location
Highest System Voltage [kV]
100…199
200…299
300…499
500…699
≥ 700
Total
Tapping Leads
4
1
0
3
0
8
Bushings
60
45
69
23
14
210
CT
0
1
1
0
0
2
Phase to phase
Insulation
2
2
1
0
0
5
Electrical screen
2
0
3
0
1
6
Windings
165
53
61
23
9
310
Winding to
ground
insulation
6
3
3
0
1
15
Tank,
Conservator,
Piping
9
4
6
0
2
21
Winding to
Winding
Insulation
6
3
1
1
0
11
Lead exit
16
8
12
4
0
40
Tap changer
73
60
16
10
0
158
Core and
magnetic circuit
5
5
7
2
4
23
Internal surge
arrester
1
0
1
0
0
2
Unknown
19
6
10
0
1
37
Total
368
191
191
66
32
848
Figure 26 shows the failure location for all 848 analysed failures. Failures were predominantly winding
(37 %), bushings (25 %), and tap changer (19 %) related. In only 4 % of the cases the failure location
was given as unknown.
50
TB 939 - Analysis of AC transformer reliability
Core & Magnetic
circuit
3%
Tap Changer
19%
Tapping Leads
1%
Bushings
25%
Lead Exit
5%
CT
0%
Winding / Winding
Insulation
1%
Unknown
4%
Tank, Conservator,
Piping
2%
Winding / Ground
Insulation
1%
Phase / Phase
Insulation
1%
Electrical Screen
1%
Windings
37%
Figure 26: Failure location analysis for 848 failures.
Substation transformers (Figure 27) follow a similar trend where windings (44 %), bushing (25 %), and
tap changer (21 %) related failures are the main contributors. It can be seen that in shunt reactors, 49
% of the failures occur at bushings followed by 31 % in windings and 11 % in core & magnetic circuits.
Whereas in GSUs most of the failures occur in windings (41 %), tap changer (15 %), bushings (15 %),
leads (12 %), and core & magnetic circuit (12 %). Note that failures for which location was not
described have been excluded from this analysis.
50%
40%
30%
20%
10%
0%
Bushings Windings
Leads
Substation transformers
Tap
Tank,
Changer Conserv.,
Piping
Shunt reactors
Core
Other
GSU transformers
Figure 27: Failure location analysis and transformer application
(substation 708 failures, shunt reactors 45 failures, GSU 59 failures).
The analysed data confirms that windings, bushings and tap changers are the main components where
the failure originated, and attention needs to be paid to the design, construction, operation and
maintenance of these components to improve the overall transformer fleet reliability.
51
TB 939 - Analysis of AC transformer reliability
6.3.3
Failure Cause Analysis
Determining the root cause of failure can be a difficult and extensive task. Transformer failures can be
attributed to different causes. Table 12 represents the failure cause analysis according to the voltage
class of the transformers. External short circuits, unknown, material, and other reasons are major
causes for transformer failure, shown in Figure 28. The substantial proportion of almost 40 % attributed
to unknown and other reasons underscores a significant issue, although the causes suggested in the
questionnaire actually cover most possible causes in the opinion of the WG members. This number has
increased by 5 % in comparison with WG A2.37. This high percentage might suggest more focus on
failure investigation procedures is required by utilities, or many failures have been catastrophic as
shown in chapter 6.3.4. If a transformer catches fire or explodes it might not be possible to determine
how this happened. Regardless, this finding is significant, highlighting that the primary step toward
enhancing reliability is understanding the root cause of failures. Because of the high proportion of
unknown and other reasons, interpretation of these numbers should be undertaken with caution.
In substation transformers, external short circuits (14 %) and material (9 %) were the most prominent
failure reasons after other reasons (22 %) and unknown reasons (16 %) as shown in Figure 29. In
shunt reactor transformers and GSUs, the dominant failure causes were material, ageing, unknown and
other reasons.
Material
11%
Ageing
10%
Overload Loss of clamping pressure
1%
2%
Vandalism
1%
External short circuit
13%
Overheating
3%
Overvoltage/lightning
5%
Other reasons
23%
Unknown
16%
Repetitive through
faults
1%
Oil Quality
External Pollution
4%
1%
Collateral damage
Transport/Site
1%
Installation Corrosive Sulphur
2%
2%
Improper
maintenance/Repair
4% Loss of cooling
1%
Figure 28: Failure cause analysis for 783 failures.
52
Design/Manufacturing
2%
TB 939 - Analysis of AC transformer reliability
Table 12: Failure cause analysis depending on the voltage class.
Failure cause
Highest System Voltage [kV]
100…199
200…299
300…499
%
%
%
500…699
≥ 700
%
Total
%
%
Overload
4
1.0
4
2.4
1
0.58
0
0
1
3.1
10
1.2
Ageing
27
7.3
12
7.8
20
11.6
12
20
4
12.5
75
9.5
External short
circuit
76
20.7
10
6.5
11
6.4
1
1.6
0
0
98
12.5
Loss of clamping
pressure
8
2.2
3
2.0
1
0.58
3
5
0
0
15
1.9
Vandalism
3
0.8
1
0.6
0
0
0
0
0
0
4
0.5
Material
31
8.4
11
7.2
26
15.2
12
20
7
21.8
87
11.1
Overheating
11
3.0
3
1.9
3
1.7
2
3.3
0
0
19
2.4
Over voltage
/Lightening
20
5.5
6
3.9
7
4.1
4
6.6
0
0
37
4.7
Oil Quality
22
6
2
1.2
2
1.1
2
3.3
0
0
28
3.5
External Pollution
5
1.3
1
0.6
4
2.2
0
0
0
0
10
1.2
Collateral
damage
3
0.8
1
0.6
2
1.1
0
0
0
0
6
0.7
Transport/Site
Installation
7
1.9
5
3.2
7
4.1
0
0
0
0
19
2.4
Corrosive
Sulphur
9
2.4
3
2.0
1
0.58
1
1.6
0
0
14
1.7
Improper
maintenance or
repair
9
2.4
12
7.8
5
2.9
1
1.6
3
9.3
30
3.8
Loss of cooling
2
0.5
1
0.6
1
0.58
0
0
0
0
4
0.5
Design/
Manufacturing
7
1.9
0
0
3
1.7
2
3.3
0
0
12
1.5
Repetitive
through faults
9
2.4
1
0.6
0
0
0
0
0
0
10
1.2
Other reasons
53
14.4
57
37.2
48
28.1
10
16.6
11
34.3
179
22.8
Unknown
61
16.6
20
13.0
29
17
10
16.6
6
18.7
126
16.1
Total
367
153
171
53
60
32
783
TB 939 - Analysis of AC transformer reliability
35%
30%
25%
20%
15%
10%
5%
0%
Substation transformers
Shunt reactors
GSU transformers
Figure 29: Failure cause analysis according to transformer application
(highest contributors only).
The analysed data shows that about 16 % of the failure causes are unknown, which may fit with a 14
% “explosion with fire effects” rate, as it will be presented in the next paragraph. Catastrophic failures
like transformer explosion with fire, may result in almost impossible failure cause identification.
On the other hand, there is a 23 % of other failure causes, which seems excessive. Future actions
should focus on better failure cause classification by utilities and review of the available failure cause
categories by the WG members.
6.3.4
External Effects Analysis
Table 13 represents the external effects due to failures with respect to voltage class. Most of the failures
did not have any external effect (61.8 %).
Table 13: External effects of failures depending on voltage class.
External effects
Highest System Voltage [kV]
100…199
200…299
300…499
500…699
%
%
%
%
≥ 700
Total
%
%
Explosion with
fire
37
10
12
9.1
33
23
16
26
6
18
104
14
Explosion w/o
fire
30
8.2
20
15
20
13
17
28.
3
15
46,
8
102
13
Leakage only
24
6.5
17
12
20
13
4
6.6
3
9.3
68
9.3
None
270
74
82
62
66
47
23
38
8
25
449
61
Unknown
3
0.8
0
0
2
1.4
0
0
0
0
5
0.6
Total
364
131
141
60
32
728
Figure 30 shows the external effects of 728 major failures. Most of the major failures did not have any
external effect (62 %). 14 % of failures led to fire while for an equal amount (14 %) of major failures,
external effects were related to explosions without fire. 9 % of failures resulted in leakage only.
54
TB 939 - Analysis of AC transformer reliability
Unknown
1%
Explosion with
fire
14%
Explosion w/o
fire
14%
Leakage Only
9%
None
62%
Figure 30: External effects analysis for 728 failures.
In substation and shunt reactors, the majority of the failures (64 % and 61 %) did not have any external
effects, as shown in Figure 31. Explosion without fire (36 %) was the most prominent outcome for
shunt reactors. Most of the failures either did not have any external effect on transformers or resulted
in an explosion with or without fire.
70%
60%
50%
40%
30%
20%
10%
0%
Explosion with
fire
Explosion w/o
fire
Substation transformers
Leakage Only
None
Unknown
Shunt reactors
GSU transformer
Figure 31: External effects analysis and transformer application
(substation 642 failures, shunt reactors 41 failures, GSU 45 failures).
As previously mentioned in chapter 6.3.3, the failures resulting in explosion with fire might result in
more difficult failure analysis than normal, as the failed component and failure cause might not be even
identified after the failure investigation process. Preventing a transformer fire is beneficial specially from
an environmental and safety point of view, but it would definitely help to the failure investigation
process. Therefore, future steps should be implemented to avoid fires after transformer explosion.
55
TB 939 - Analysis of AC transformer reliability
6.3.5
Action Analysis
The actions taken after a failure, according to voltage rating, are shown in Table 14 and Figure 32.
After the event, for all voltage classes, most of the transformers were taken out of service and scrapped.
43 % of transformers were repaired (23.6 % onsite and 19.4 % in workshop). Especially, shunt reactors
were scrapped quite often after a failure (63 %).
Table 14: Action analysis depending on voltage class.
Detection mode
Highest System Voltage [kV]
100…199
200…299
300…499
%
%
%
500…699
≥ 700
%
Total
%
%
Repair in factory
86
23.4
23
14.3
22
13.8
15
25
5
15.6
151
19.4
On-site repair
56
15.2
61
38.1
49
37.1
6
10
2
6.2
184
23.6
Scrapping
125
16.1
75
46.8
87
54.7
19
31.6
21
65.6
327
42.0
Unknown
100
27.2
1
0.6
1
0.6
20
33.3
4
12.5
128
16.4
Total
367
160
159
Scrapping
41%
60
32
Repair in
workshop
19%
Onsite repair
24%
Unknown
16%
Figure 32: Action analysis for 778 major failures.
56
778
TB 939 - Analysis of AC transformer reliability
70%
60%
50%
40%
30%
20%
10%
0%
Repair in workshop
Onsite repair
Substation transformers
Scrapping
Shunt reactors
Unknown
GSU transformers
Figure 33: Action analysis applied to transformer application
(substation 674 failures, shunt reactors 41 failures, GSU 63 failures).
The data analysis suggest that an important portion of the failed units were scrapped, however, it does
not mean that all the scrapped units failed catastrophically. In most of those cases, the life cost of a
new transformer is lower than the repair, and the utilities take the decision based on overall economic
solutions. In the recent years, there is a higher tendency to repair units, due to the increased delivery
time for new units.
6.3.6
Detection Mode Analysis
This analysis describes the test, methods, or devices used to detect the occurrence of a failure. Table
15 represents the methods (including unknown methods) according to the voltage class of the
transformer. Most of the failures were detected by conventional protection equipment. Differential
protection and Buchholz relay are the most common devices in all voltage classes as they collectively
detected 62.6 % of all failures. Regarding condition detection techniques, online monitoring shows a
poor condition more often than conventional DGA. This is particularly true at higher voltage levels,
where monitoring systems are more often installed. Small transformers might not be required to be
provided with the same level of monitoring equipment as high voltage ones. So, using monitoring
systems also for smaller transformers can be a measure to detect oncoming faults earlier.
Figure 35 shows the detection mode analysis according to transformer application. For shunt reactors
20 % of the failures were detected by online monitoring systems. Also, for higher voltage classes online
monitoring plays an important role to detect oncoming failures.
57
TB 939 - Analysis of AC transformer reliability
Table 15: Detection mode analysis depending on the voltage class.
Detection mode
Highest System Voltage [kV]
100…199
200…299
300…499
%
%
%
500…699
≥ 700
%
Total
%
%
Differential
protection relay
169
46.0
43
30.5
70
41.4
38
63.3
19
59.3
279
44.1
Buchholz relay
66
18
34
24.1
33
19.5
7
11.6
2
6.2
142
18.5
Over Current Relay
14
3.8
3
2.1
2
1.1
1
1.6
1
3.1
21
2.7
Pressure
device
relief
4
1.1
3
2.1
3
1.7
3
5
0
0
13
1.6
Laboratory DGA, Oil
Quality Test
8
2.1
3
2.1
10
5.9
2
3.3
1
3.1
24
3.1
Site Inspection
17
4.6
13
9.2
15
8.8
1
1.6
0
0
46
5.9
Online Monitoring
system
5
1.3
3
2.1
16
9.4
1
1.6
6
18.7
31
4.0
Other
offline
Diagnostics
7
1.9
8
5.6
2
1.1
4
6.6
0
0
21
2.7
Unknown
77
21
31
22
18
10.6
3
5
3
9.3
132
17.1
Total
367
141
169
60
32
Pressure relief
device
Figure 34: Detection mode analysis for 769 transformers.
58
769
TB 939 - Analysis of AC transformer reliability
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
Substation transformers
Shunt reactors
GSU transformers
Figure 35: Detection mode analysis and transformer application
(substation 660 failures, shunt reactors 46 failures, GSU 63 failures).
It is important to mention that almost 66.9 % of the failures were detected when the transformer
actually tripped during operation (by the differential protection relay, by the Buchholz relay, by the over
current relay or by the pressure relief device). About 15.7 % of the failures were detected in advance
a trip occurred, and the units were taken out from service by the utility in a planned manner. The
portion of detected failures before tripping should increase in the future; the use of condition monitoring
devices to foresee faulty conditions should be enhanced to avoid unpredictable failures during service,
which cause operational challenges to the utilities, as well as severe safety and reputational issues.
6.3.7
Comparison with CIGRE Survey of A2.37
A comparison was made with A2.37 using the failures with known classification (e.g. location, external
effects, mode, etc). Figure 36 shows the failure location comparison for A2.62 (blue) and A2.37
(orange). In both surveys, windings, bushings, and tap changer related failures were major
contributors. The most striking change is that, proportionally, more failures involve bushings. However,
fewer unknown types have been reported.
59
Failures (%)
TB 939 - Analysis of AC transformer reliability
40
35
30
25
20
15
10
5
0
A2.62
A2.37
Figure 36: Comparison of failure location.
The comparison of external effects of failures for the A2.62 and A2.37 surveys is given in Figure 37.
The majority of failures did not have any external effect in both surveys. Explosion or bursts, with or
without fire, and leakage are also featured among the major external effects in the A2.62 but had lower
contributions in A2.37. The analysis suggests that in the last decade, more units had catastrophic type
of failures compared to the units analysed in the 2000’s by the A2.37.
80
70
60
50
40
30
20
10
0
Explosion
with fire
Leakage
Only
None
A2.62
Explosion
w/o fire
Unknown
A2.37
Figure 37: Comparison of external effects.
The comparison of failure modes for A2.62 and A2.37 surveys is given in Figure 38. Dielectric mode
failure is the major contributor in A2.37 whereas A2.62 has higher contribution of electrical mode faults.
Both surveys also featured mechanical mode failures as a major contributor.
60
TB 939 - Analysis of AC transformer reliability
40
35
Failures (%)
30
25
20
15
10
5
0
Dielectric
Mechanical
Electrical
A2.62
Unknown
Thermal
Physical
Chemistry
A2.37
Figure 38: Comparison of failure modes.
The comparison of failure causes is shown in Figure 39. Both surveys have a considerable number of
failures for which causes are unknown. Besides this, design and manufacturing, ageing, and external
short circuits appeared to be major contributors in A2.37, whereas, in A2.62 external short circuits,
material, ageing, and other reasons are major causes for transformer failure.
Failures (%)
30
20
10
0
A2.62
A2.37
Figure 39: Comparison of failure causes.
Figure 40 represents the comparison of actions taken after failures. In both surveys, most of the
transformers were repaired on or off-site and about one-third of transformers were scrapped after a
major failure. It seems there is a larger tendency to scrap the failed units in the last decade, maybe
because there were more catastrophic failures (A2.62 results).
61
Failures (%)
TB 939 - Analysis of AC transformer reliability
45
40
35
30
25
20
15
10
5
0
Repair in
workshop
Onsite repair
A2.62
Unknown
Scrapping
A2.37
Figure 40: Comparison of action taken after failure.
No information of detection mode was provided in A2.37, so a comparison of detection mode could not
be performed.
62
TB 939 - Analysis of AC transformer reliability
7
Hazard Curves
Figure 41 shows the number of failures with the age of the transformer, for both when the unit was
repaired and returned to service, or scrapped. Later in this chapter a separate analysis is given for
when the transformer was scrapped after a failure. The number of failures is very similar across the full
service age. However, the age distribution is required to determine and to make conclusions about the
hazard rate.
Figure 41: Number of failures dependent on service age, when the transformer was either
repaired and returned to service or when it was scrapped.
7.1 Age Distribution
Fifty-eight of the respondents supplied the age distribution of their transformer fleet. This data was
analysed to investigate relationships between age, voltage class and probability of failure. The data
was sifted so only events happening from 2010 onwards was used in this analysis. Both nonparametric
and Weibull distribution methods were used (see chapter 4).
The data was combined and checked using an Excel spreadsheet, before being processed using Python
scripts. The flowchart in Figure 42 shows the approach, where applying both methods to investigate
the failure rate helps to understand the data.
63
TB 939 - Analysis of AC transformer reliability
𝜆(𝑡)
Individual
utility
response data
Combined age
distribution:
censors,
retirements &
failures.
Disaggregated
into continent
and voltage
class.
Explore fitted
Weibull
distributions
Extract Weibull
coefficients of
best fit
Non
parametric
Compare
both non
parametric &
Weibull
failure rates
Figure 42: Approach to deduce age distribution for failure.
The populations shown below in Figure 44 to Figure 49 are normalised to fleet size, to emphasise
features in the age distribution. Most authors propose the life expectancy of a power transformer to be
about forty to fifty years. In general:
•
•
•
•
•
Africa, East Asia and Oceania have recently installed a large proportion of units.
East Asia and South America have relatively new fleets.
Europe has a large proportion of new units, but also ones around fifty years.
Oceania has a high proportion of newer units, and the proportion of older ones is relatively flat.
North America has a high proportion of older units.
The oldest power transformer still in operation recorded by the survey was built in 1919. The nameplate
of this transformer is shown in Figure 43. This is a striking example of the longevity of transformers.
The relative weighting of the fleet age distribution will impact the accuracy of the failure rate calculation.
For instance, a fleet being young will create more uncertainty when attempting to extrapolate a failure
rate for aged transformers compared to an older fleet.
64
TB 939 - Analysis of AC transformer reliability
a) manufactured 1919
b) manufactured 1928
Figure 43: Nameplates of oldest transformer recorded by survey.
Africa
0.07
Fraction of fleet
0.06
0.05
0.04
0.03
0.02
0.01
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
Age (years)
Figure 44: African age distribution, average age: 31.1 yrs
East Asia
0.06
Fraction of fleet
0.05
0.04
0.03
0.02
0.01
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
Age (years)
Figure 45: East Asian age distribution, average age: 20.2 yrs
65
71
TB 939 - Analysis of AC transformer reliability
Europe
0.03
Fraction of fleet
0.025
0.02
0.015
0.01
0.005
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
Age (years)
Figure 46: European age distribution, average age: 28.1 yrs
North America
0.03
Fraction of fleet
0.025
0.02
0.015
0.01
0.005
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
Age (years)
Figure 47: North American
age distribution, average age: 37.5 yrs
Oceania
0.08
Fraction of fleet
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
Age (years)
Figure 48: Australian and New Zealand age distribution, average age: 18.7 yrs
66
TB 939 - Analysis of AC transformer reliability
South America
0.18
Fraction of fleet
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
Age (years)
Figure 49: South American age distribution, average age: 10.5 yrs
7.2 Hazard curves for retirements caused by condition or age
For the utilities providing the age distribution of their asset base there were 1,268 transformers retired
on either condition or age, from a surviving population of 27,170 units. After twenty years the fraction
failing accelerated. Fitting two Weibull distributions to this data indicated a random distribution, line 1,
(β1 = 0.9) and a highly age-dependent one, line 2, (β2 = 5.4), Figure 50, where the upper 95 %
confidence intervals in the shape and scale parameters were calculated using the method proposed by
Bain [58], [59]. The age dependency is unsurprising as many utilities retire on age. The characteristic
age η2 of these units is nearly 80 years. The hazard function is given in Figure 51, which appears
exponentially increasing because of the random distribution.
Although the Weibull distribution can use right-censored data it cannot use left truncated information.
Thus, there may be a small inaccuracy without the data being complete.
Line 2
Line 1
Figure 50: Age distribution of retiring units.
67
TB 939 - Analysis of AC transformer reliability
4.5%
Hazard-retirement rate (%)
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
0
10
20
30
40
50
60
70
Age (years)
Figure 51: Hazard function of retired units due to condition.
Table 16: Weibull parameters for retirements.
Retirements / Survivors: 1268 / 27170
Parameter
Estimate
Standard Error
Lower CI
Upper CI
η1
24,150
32,400
1,740
334,000
β1
0.91
0.16
0.65
1.29
η2
79.0
0.80
77.41
80.55
β2
5.24
0.13
5.00
5.50
The fraction failing and hazard rates for the individual voltage classes are given below in Figure 52 and
Figure 53. The larger voltage transformers may be more likely to be retired than the lower-voltage
classes because of their strategic importance and long project lead times.
Figure 52: Age distribution of retirements by voltage class.
68
TB 939 - Analysis of AC transformer reliability
100 - 199 kV
200 - 299 kV
0
20
300 - 499 kV
500 - 699 kV
18.0%
16.0%
Hazard rate
14.0%
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
0.0%
10
30
40
50
60
70
Age (years)
Figure 53: Hazard rate for retiring by voltage class.
Table 17: Coefficients for retirement distributions.
100 – 199 kV
200 – 299 kV
300 – 499 kV
500 – 699 kV
Retirements/Survivors
707/15,000
304/6,165
182/4,153
75/1,852
η1
27,281
7,229
6,442
65.07
β1
0.89
1.04
1.08
6.66
η2
2,977
79.14
70.50
β2
6.28
4.85
4.71
7.3 Hazard Curves for All Major Failures
The data was processed to show the hazard rate for all reported failures, which included when a
transformer was repaired and returned to service, as well as when scrapped. Fitting the Weibull
distribution to the data indicated that the failures were fairly random with a slight increase after several
decades, Figure 54, Figure 55 and Table 18. The β coefficient being 1.2 indicates this randomness.
Possible reasons include:
•
•
Some subcomponents are unlikely to last the full operating life of a transformer, and so are
designed to be replaceable. These subcomponents are failing periodically.
The events leading to a major failure are random, such as lightning, surges, design, operation
etc.
69
TB 939 - Analysis of AC transformer reliability
Figure 54: Fraction failing for all major failures as a function of age, 884 failures and 28,119
operating units.
0.6%
Hazard rate
0.5%
0.4%
0.3%
0.2%
0.1%
0.0%
0
10
20
30
40
50
60
70
Age (years)
Figure 55: Hazard rate for all major failures, as a function of age.
Table 18: Coefficients for all major failures.
Failures / Survivors: 884/28,119 (96.95204 % right censored)
Parameter
Estimate
Standard Error
Lower CI
Upper CI
η1
828
272
435
1,575
β1
1.20
0.09
1.03
1.39
η2
141
12.7
118
168
β2
3.50
0.46
2.70
4.51
70
TB 939 - Analysis of AC transformer reliability
The data was stratified by voltage class, again for all failures as shown in Figure 56 and Figure 57.
The hazard rate is reasonably flat for all classes until after twenty years, when the rate increases for
the 200 – 299 kV and 300 – 499 kV transformers. Only thirty-nine major failures were recorded for
the highest voltage transformers, so there might not be enough experience by the industry to make
statistically conclusive evaluations. The β coefficients for these distributions are around 1, indicating
again randomness.
Figure 56: Fraction failing, all major failures by voltage class.
100 - 199 kV
200 - 299 kV
0
20
300 - 499 kV
500 - 699 kV
1.4%
1.2%
Hazard rate
1.0%
0.8%
0.6%
0.4%
0.2%
0.0%
10
30
40
50
60
70
Age (years)
Figure 57: Hazard rate, all major failures by voltage class.
Table 19: Distribution coefficients for all failures, different voltage class transformers.
100 – 199 kV
200 – 299 kV
300 – 499 kV
500 – 699 kV
Failures/Survivors
459/15,294
217/6,286
169/4,214
39/1,126
η1
280
1569
812
249
β1
1.70
1.03
1.05
1.51
η2
85.6
102
117
β2
18.9
4.43
3.30
71
TB 939 - Analysis of AC transformer reliability
The line parameters indicate the following:
•
•
•
•
•
β1 shows that the first distribution covers random failures because it is around one.
β2 indicates a wear-out distribution because it is higher than one.
η1 is extremely high. It reflects the characteristic life from the random failure distribution, where
it will take a very long time for all transformers to fail from random events. Instead, over time
the failure rate will increase once the wear-out distribution dominates.
η2 is the characteristic life of the transformers, estimating when 63.2 % have failed based on
the above data.
The data is very right censored, e.g. nearly 99 %. The characteristic life assumes that no other
wear-out mechanisms become dominant. The mean life is estimated from the characteristic
1
life, 𝑇̅ = 𝜂 ∙ 𝛤( + 1), where 𝛤 is the gamma function. For the characteristic life of 86 years,
𝛽
•
given in Table 19, a mean life of 83 years can be calculated.
The shape of the trace in Figure 54 shows a slight inflection around 40 years, indicating the
start of the wear-out period.
7.4 Hazard Curves for Scrapped Transformers
Determining hazard curves for when a transformer was scrapped after a failure has the following two
advantages. One is that the probability of a transformer not being technically repairable after a fault is
known. Two is that different utilities may make differing decisions on whether a transformer is
economically repairable after the fault, or if they should instead invest in a new unit. Shown in Figure
58 is the fraction failing and requiring scrapped for the entire fleet. The trendline was best fitted using
two Weibull distributions, where the coefficients are given in Table 20. The hazard rate was then
plotted, Figure 59.
A striking difference from the all-failures Figure 54 is that the knee point is sooner, at between ten and
twenty years. This indicates that after twenty years the failure rate, resulting in the unit being scrapped,
has been increasing. Possibly, a utility may take the economics of repairing a failed unit into account
and make the decision to scrap instead. Also, a transformer may survive successive faults and then fail
catastrophically, caused by incremental damage.
Figure 58: All scrapped failures cumulative distribution function.
72
TB 939 - Analysis of AC transformer reliability
0.5%
Hazard rate (%)
0.4%
0.3%
0.2%
0.1%
0.0%
0
10
20
30
40
50
60
70
Age (years)
Figure 59. Hazard rate for all scrapped failures. Dashed line indicates upper 95 % confidence
interval.
Parameters of the above distribution fittings are given below in Table 20:
Failures / Right censored: 322/28,119 (98.9 % right censored).
Table 20: Distribution fitting descriptives.
Parameter
Estimate
Standard Error
Lower CI
Upper CI
η1
378,000
982,000
2,359
6.08e+07
β1
0.657
0.150
0.419
1.029
η2
222
23.9
179
274
β2
2.58
0.203
2.21
3.015
Graphs for different voltage classes are shown below, Figure 60. There were too few failures from the
>700 kV class and so these are not presented. The respective hazard rates are shown in Figure 61.
Most of the distributions were best fitted with two Weibull distributions, because the trendline was
clearly non-linear. For the 500-699 kV class, although the datapoints look to be following a curve, there
was insufficient data for the algorithm to fit the second distribution and so only a linear trendline was
used.
The different voltage classes are consistent in so far as the fraction failing begins to accelerate after a
few decades, shown by the bend in the trendlines indicating wear-out. There were not enough 500 –
699 kV failures to fit a second order trendline.
73
TB 939 - Analysis of AC transformer reliability
Figure 60: Scrapped failures according to voltage class.
100 - 199 kV
200 - 299 kV
0
20
300 - 499 kV
500 - 699 kV
0.7%
Hazard rate (%)
0.6%
0.5%
0.4%
0.3%
0.2%
0.1%
0.0%
10
30
40
50
60
70
Age (years)
Figure 61: Hazard rate of scrapped failures according to voltage class.
In general, as there are usually few early failures in a transformer population the trendline is less certain
than in the later wear-out phase. The Line 1 coefficients in Table 21 show general agreement in that
there is an early or random period where β is less than or around one, and then a second line where β
indicates a wear out type distribution. The high characteristic life for random failure distributions
indicates that they would take a very long time to fail if it were not for wear out. Some utilities may
have a very high characteristic life if they are retiring units as these are then not shown in the failure
distribution.
74
TB 939 - Analysis of AC transformer reliability
Table 21: Distribution coefficients for different voltage class transformers.
100 – 199 kV
200 – 299 kV
300 – 499 kV
500 – 699 kV
158/15,294
83/6,286
64/4,214
10/1,126
β
0.93
0.90
0.48
1.15
η
28.38
21
373
1.6
β
2.31
4.10
2.49
η
295
123
177
Failures/censoring
Line 1
Line 2
7.5 Hazard Curves for Scrapping by Continent
The utilities were grouped into the continents Africa, East Asia, Europe, North America, Oceania
(Australia and New Zealand) and South America. Only retirements removed on age or condition and
failures when the transformer was subsequently scrapped were used for this analysis.
The overview of the studied transformer populations is given in Table 22. It is noted that different
utilities may have differing criteria for when a transformer is repaired or scrapped. There is also an
economic consideration that a utility might not repair an old transformer where there is perceived little
economic value remaining, or if the transformer is no longer required within the network, or if there is
a spare. However, this may be currently changing because of global manufacturing constraints.
For retirements, only units which were removed due to age or condition were counted. Transformers
withdrawn because of new network requirements were not included. An example is a utility increasing
the voltage of a line and requiring different transformers.
The average failure rate, failures divided by service years, is remarkedly consistent across the
populations. Differences in retirement rate are likely to be explained by the differing criteria in how
organisations choose to withdraw a transformer, such as perceptions about risk and ease of
replacement.
Table 22: Regional statistics. Censors refers to the number of units still operating by the end of
the data collection period.
Africa
E. Asia
Europe
N. America
Oceania
S. America
676
5,793
10,911
6,907
2,200
212
Service
years
6,604
48,479
98,917
64,820
22,313
1,646
Failures
(scrapped)
43
60
107
74
31
3
Retirements
7
144
362
152
152
0
Av. Failure
rate (%)
0.6
0.1
0.1
0.1
0.1
0.2
Av.
Retirement
rate (%)
0.1
0.3
0.4
0.2
0.7
-
Number
censors
of
75
TB 939 - Analysis of AC transformer reliability
7.5.1
Africa
There is no discernible trend in the age distribution, Figure 62. This population had the smallest number
of transformers, and there may not have been a sufficient number of failures to ascertain whether there
is an age-related increase in hazard rate.
Figure 62: African transformer failure and retirement rate.
7.5.2
East Asia
This region has a high proportion of new units, and so there are fewer older transformers to fit the
Weibull distribution. There is a high proportion of retirements in Figure 63, which probably explains the
slight fall in failure rate after 40 years. The Weibull distribution was fitted using both the IEC 61649
algorithm written in Microsoft Excel, and the Python Reliability Toolbox, Figure 64. The oldest reported
failure was at 44 years, and so the divergence between the two fitting algorithms is likely to be due the
lack of older failures. The hazard rate is shown in Figure 65.
76
TB 939 - Analysis of AC transformer reliability
Figure 63: East Asian transformer failure and retirement rate.
IEC61649
Python Reliability toolbox
Actual data
10%
9%
8%
CDF (%)
7%
6%
5%
4%
3%
2%
1%
0%
0
10
20
30
40
50
Age (years)
Figure 64: Curve fitting to East Asian failure data.
77
60
70
TB 939 - Analysis of AC transformer reliability
Failure rate
Weibull
Upper CI
2.0%
Hazard rate
1.5%
1.0%
0.5%
0.0%
0
10
20
30
40
50
60
70
80
Age (years)
Figure 65: Hazard rate of East Asian transformers.
7.5.3
Europe
Hazard rates are shown in Figure 66, along the fitted Weibull distribution fitted to the data in Figure
68. Both curve fitting algorithms are very similar, Figure 67. There is a high proportion of retirements
after 50 years, which probably explains there is no significant age-related increase in hazard rate.
Figure 66: European transformer failure rate. There is an outlier at 70-years.
78
TB 939 - Analysis of AC transformer reliability
IEC61649
Python Reliability toolbox
Actual data
10%
9%
8%
CDF (%)
7%
6%
5%
4%
3%
2%
1%
0%
0
10
20
30
40
50
60
70
70
80
Age (years)
Figure 67: Curve fitting to European failure data.
Failure rate
Weibull
Upper CI
2.0%
Hazard rate
1.5%
1.0%
0.5%
0.0%
0
10
20
30
40
50
60
Age (years)
Figure 68: Weibull distribution fitted to European failure data. The last datapoint is an outlier.
7.5.4
North America
North American data is shown below in Figure 69, Figure 70, and the fitted Weibull distribution in Figure
71. The curve fitting algorithm converged to two competing wear-out distributions, which may be
because of the high proportion of old units, Figure 47, and subsequent focus on this part of the age
distribution. The hazard rate is shown in Figure 70.
79
TB 939 - Analysis of AC transformer reliability
Figure 69: North American transformer failure and retirement rate.
IEC61649
Python Reliability toolbox
Actual data
10%
9%
8%
CDF (%)
7%
6%
5%
4%
3%
2%
1%
0%
0
10
20
30
40
50
Age (years)
Figure 70: Curve fitting to North American failure data.
80
60
70
TB 939 - Analysis of AC transformer reliability
Failure rate
Weibull
Upper CI
2.0%
1.5%
1.0%
0.5%
0.0%
0
10
20
30
40
50
60
70
80
Age (years)
Figure 71: Weibull distribution fitted to North American failure data.
7.5.5
Oceania
Data for Australia and New Zealand is shown in Figure 72, with the Weibull distribution given in Figure
74. Curve fitting is shown in Figure 73. The two algorithms appear to match different regions of the
failure distribution better.
Figure 72: Australian and New Zealand transformer failure rate.
81
TB 939 - Analysis of AC transformer reliability
IEC61649
Python Reliability toolbox
Actual data
10%
9%
8%
CDF (%)
7%
6%
5%
4%
3%
2%
1%
0%
0
10
20
30
40
50
60
70
Age (years)
Figure 73: Curve fitting to Australian and New Zealand failure data.
Failure rate
Weibull
Upper CI
2.0%
Hazard rate
1.5%
1.0%
0.5%
0.0%
0
10
20
30
40
50
60
Age (years)
Figure 74: Weibull distribution fitted to Australian and New Zealand failure data.
82
70
TB 939 - Analysis of AC transformer reliability
7.5.6
South America
The South American transformer data is relatively young compared to the other continents. Therefore,
it is unsurprising that very few failures have been observed in Figure 75.
Failure rate
Retirement rate
5.0%
Hazard rate
4.0%
3.0%
2.0%
1.0%
0.0%
0
5
10
15
20
25
30
35
40
Age (years)
Figure 75: South American transformer failure rate.
Where possible, the Weibull distribution was fitted to the above failure rates, and the coefficients are
shown in Table 23 below.
Table 23: Weibull distribution line coefficients.
E. Asia
Europe
N. America
Oceania
β
1.55
0.46
2.80
0.67
η
463
5e7
218
178,000
β
11.91
3.06
9.05
4.00
η
275
183
413
128
Line 1
Line 2
Comments:
•
•
Substation transformers are not normally subjected to such heavy loads, so ageing is not
pronounced. Failures due to ageing can be masked by random or external failure reasons and
early replacement of transformers.
Utilities often use replacement strategies, in which old transformers are preferentially replaced,
which do not anymore fulfil the operational requirements (e.g. losses, noises, regulating range).
Thus, potential failures of replaced units do not contribute to the hazard curve.
83
TB 939 - Analysis of AC transformer reliability
•
•
•
•
•
The failure and retirement rate for a given year can look large for a small sample size. For
instance, a utility often will have comparatively fewer old transformers, and so one failure may
look statistically significant.
For Africa, there was not enough data collected, and so when a failure occurs it appears more
statistically significant.
East Asia, Europe and Oceania retire a significant proportion of transformers older than 40
years. This is likely to reduce the failure rate as the units which could fail are removed from
the population.
For Oceania transformers, the failure rate increases along with the retirement rate. This
insinuates that the right transformers are not being retired.
North Americans rarely retire. There may be differences in what they will repair as opposed to
scrapping and purchasing a new unit compared to other utilities.
7.6 Combined Weibull distributions for failure and retirement (due to age
or condition)
The age distribution combining failures (resulting in scrapping) and retirements (age and condition)
was plotted, Figure 76, as in certain instances an organisation may require this for planning.
Retirements due to changing network conditions were excluded. It is noted that there is no one
accepted criterion for replacement, different organisations may have differing policies and acceptance
of risk. The respective hazard rate drawn using the Weibull coefficients is given in Figure 77.
The useful life of these assets ends between twenty and thirty years, as seen by the bend in the trendlines. Line coefficients are given in Table 24.
Figure 76: Age distribution for failures where the unit was subsequently scrapped, and
retirements based on age or condition.
84
TB 939 - Analysis of AC transformer reliability
East Asia
Europe
N. America
Oceania
14%
Hazard rate (%)
12%
10%
8%
6%
4%
2%
0%
0
10
20
30
40
50
60
70
Age (years)
Figure 77: Combined hazard rate for failures and retirements.
Table 24: Weibull distribution parameters for failures and retirements.
East Asia
Europe
NA
Oceania
(North
America)
(Australia and
NZ)
Failures/Censoring
204/5,937
470/11,274
229/7,096
183/2,470
η1
3,471
14,855
3,908
48,297
β1
1.02
0.86
1.33
0.76
η2
75
81
96
68
β2
4.36
5.39
5.00
7.01
7.7 Effect of removing bushings and OLTC on hazard rate
Bushings and tap changers can be replaced or refurbished during the life of the transformer.
Consequently, the analysis was repeated with only the failures caused by other failure location, and
this is shown below in Figure 78 to Figure 80. Line coefficients are given in Table 25.
Higher voltage classes are not shown because the number of failures were too few for the Weibull
modelling programme to converge.
In general, the fraction failing is approximately halved when bushings and OLTCs are removed. This
fits with Figure 26, which shows approximately half of all failures are caused by these two components.
85
TB 939 - Analysis of AC transformer reliability
Figure 78: Bushing and OLTC removed for 100 – 199 kV transformers.
Figure 79: Bushing and OLTC removed for 200 – 299 kV transformers.
86
TB 939 - Analysis of AC transformer reliability
Figure 80: Bushing and OLTC removed for 300 – 499 kV transformers.
Table 25: Weibull distribution parameters for failures where bushings and OLTCs were removed
from analysis.
100 – 199 kV
200 – 299 kV
300 – 499 kV
Censoring
119/15,294
58/6,286
42/4,214
η1
432.982
9,801.54
1.48569e+06
β1
1.94935
1.03145
0.52814
η2
152.013
132.026
194.049
β2
20.8685
4.22494
2.66955
7.8 Kaplan-Meier Estimator
A challenge in an analysis is how to correct for units failing or retired before 2010, i.e. left-truncation.
One non-parametric method is the Kaplan-Meier estimator, where its output can be compared with that
of the Weibull distribution analysis. An advantage is that this estimator does not try to fit a statistical
distribution to the data. This uses the number of assets under observation as an input, and is shown in
Equation 7.1 where 𝑃̂ (𝑡) is the product-limit estimate of the proportion of items in the population whose
lifetimes exceed time 𝑡 between the ages of 1 and 𝑘. A disadvantage however is that no extrapolation
can be performed, like when fitting a Weibull distribution and deducing the mean or characteristic life.
𝑘
𝑃̂(𝑡) = ∏
𝑗=1
(𝑛𝑗 − 𝛿𝑗 )
𝑛𝑗
Equation 7.1
87
TB 939 - Analysis of AC transformer reliability
where 𝑛𝑗 is the number under observation just after a time period 𝐿𝑗−1, , and 𝛿𝑗 number of deaths (failed
or retired) observed in the interval (𝐿𝑗−1, 𝐿𝑗 ]. Often, the interval is one year. An output is shown in Figure
81 dependent on the continent.
Many units have not failed yet (and so the survival does not fall to zero). A mean life can be deduced
from when survival has fallen to 0.5. According to this method, the Oceanian population has a mean
life of approximately 60 years, which is consistent with 50 % fraction failing or being retired shown in
Figure 76. Using the gamma function with the Weibull distribution coefficients given in Table 24, 𝜂2 =
68 and 𝛽2 = 7, a consistent mean life of 63 years is estimated. However, for the other regions, there
have been an insufficient number of failures and retirements to calculate the mean life using this
estimator. As shown in Figure 76 there are hardly any failures or retirements occurring above the 50
% fraction failing level. The average retirement rate in Oceania, Table 22, is higher than the other
regions, explaining the lower fraction of transformers surviving in Figure 81 and why it is the only trace
where a mean life was deduced. The 50 % failure of 60 years is also consistent with the values deduced
from the 2018 Australian failure survey [60], which was 60 years for 110 and 132 kV transformers. (In
this previous survey there had been too few failures and retirements for the >200 kV transformer set
to calculate a mean survivability.)
As the Kaplan-Meier estimator is a non-parametric method, it is not possible to fit a curve like the
Weibull distribution and then extrapolate a mean life.
The Oceania, European and East Asian populations are broadly similar to one another up to about forty
years. A design life of forty to fifty years is frequently used in economic calculations. The North American
data in Figure 81 indicates fewer retirements of transformers compared to the other regions, because
the data in Table 22 shows a similar average failure rate but lower retirement rate. The next lowestreaching trace is from Europe, which has the second highest average retirement rate, then East Asia.
It is noted that this analysis only focuses on survivability, not disruption to the community or overall
lifecycle cost when an asset fails. Meyer Kaplan estimator
Oceania
North America
Europe
E Asia
1
0.9
0.8
Survival
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
10
20
30
40
50
60
70
80
Age (years)
Figure 81: Kaplan-Meier estimator, showing that the mean survival is at least sixty years.
7.9 Combining data with the WG A2.37 survey
Data from the WG A2.37 brochure [2] was re-analysed to determine the probability of failure. In this
previous survey the age distribution of the operating transformer fleet was not collected. However,
where the age distribution of the individual utility had been provided for WG A2.62, the data was
combined and a probability of failure calculated. Only the failures which were scrapped were compared.
As the WG A2.37 survey did not include retirements, the failure rate is an estimate where it is assumed
that the retirement strategy of these owners has been constant.
Table 26 gives the respective transformer population statistics. As can be seen, despite the number of
censored units nearly quadrupling from WG A2.37 to WG A2.62, the number of failures has only
88
TB 939 - Analysis of AC transformer reliability
doubled, insinuating that the failure rate has fallen. Fitting the Weibull distribution to this data, Figure
82, also shows that the hazard rate has fallen for the overall population.
The data was then stratified by voltage level and the Weibull distribution coefficients determined by
curve fitting. These coefficients were used to construct hazard rates, Figure 83, and these have indeed
fallen. There was an insufficient number of 500 to 700 kV failures in WG A2.37, and so no distribution
is given. Line coefficients are given in Table 27, along with those for WG A2.62.
Table 26: Transformer population statistics.
WG A2.37
WG A2.62
Censors
7,401
28,119
100 – 199 kV
2,853
15,294
200 – 299 kV
2,389
6,286
300 – 499 kV
1,536
4,214
Failures
162
322
100 – 199 kV
61
158
200 – 299 kV
37
83
300 – 499 kV
64
64
WG62
CI
20
30
40
Age (years)
WG37
1.0%
Hazard rate (%)
0.8%
0.6%
0.4%
0.2%
0.0%
0
10
50
60
Figure 82: Comparison of hazard rates for WG A2.37 and WG A2.62.
89
70
TB 939 - Analysis of AC transformer reliability
1.8%
100 - 199 kV
100 - 199 kV WG37
200 - 299 kV
200 - 299 kV WG37
300 - 499 kV
300 - 499 kV WG37
1.6%
Hazard rate (%)
1.4%
1.2%
1.0%
0.8%
0.6%
0.4%
0.2%
0.0%
0
10
20
30
40
50
Age (years)
Figure 83: Comparison of voltage class hazard rates.
90
60
70
TB 939 - Analysis of AC transformer reliability
Table 27: WG37 line coefficients.
Line 1
Line 2
β
η
β
η
All failures
1.31
1080
2.81
120
100 – 199 kV
3.50
103
200 – 299 kV
3.94
95
300 – 499 kV
2.19
109
7.10 Summary
Remarkably, the failure rates across different continents are very similar. This is likely to be the result
of globalisation and international transformer manufacturing.
The failure rate is seen to rise after around 20 years (Figure 58), demonstrating the end of the useful
life and entering the wear-out phase. This is also seen in the Weibull distribution coefficients in Table
21, where some lines have a gradient of 1, indicating randomness, and the second line has a higher
gradient around 2 implying wear out. This end of useful life is similar to that indicated in the study on
Australian transformers [60], which deduced a value of nearly 20 years.
The hazard rate is approximately halved when bushings and OLTCs are removed. These two
components are known to be dominant causes of failure, while they may be replaceable if the risk of
failure becomes too great.
The mean survival age implied by Kaplan-Meier model is ≥ 60 years. This is consistent with industry
experience.
91
TB 939 - Analysis of AC transformer reliability
8
Analysis of Failures of Transformers Connected
to GIS, Wind Farm Transformers, Transformers
Filled with New Liquids, Shunt Reactors
The working group goals called for the individual analysis of failures of transformers connected to GIS,
wind farm transformers, transformers filled with new liquids and shunt reactors. The focus was still on
transformers with a voltage of at least 100 kV, and so excluded many smaller units such as those
directly connected to the generator.
8.1.1
New liquids
New liquids cover both natural and synthetic type esters. However, no failures were reported. A reason
likely is that the use of these fluids in transformers with a voltage over 100 kV is still relatively
uncommon, and so there have not been enough service-years for a failure to occur.
8.1.2
Wind and PV reliability
This section pertains to power transformers collecting buses from various branches of a wind farm or
solar panels, where the voltage level has already been modified to medium voltage. Despite a thorough
review, only one failure instance of a transformer connected to a wind farm was reported. No
transformers connected to PV were indicated as having failed.
For the wind farm, a one-year-old transformer failed in 2015. An onsite repair was undertaken which
took longer than a week. The transformer exploded but without fire. The transformer was 155 kV, 180
MVA, three phase with ONAN cooling.
Below are reasons why there was not more wind or PV transformers reported:
•
•
•
Many large-scale PV sites are relatively new, for instance less than ten years old, and the
transformers are relatively young compared to the expected design life of fifty years.
There are relatively few wind and PV farms, and so statistically there may not be enough service
years yet to study failure.
Some wind and PV farms are connected by a dedicated power transformer to the MV bus, e.g.
11 or 33 kV. The load of the upstream transformer, higher than 100 kV, becomes aggregated
with other parts of the power system and not just with the wind or solar farm.
Investigations on wind and solar have noted the following aspects:
Current harmonics should be kept with IEC acceptable limits to prevent further heating inside the
transformer, which leads to a shortening of paper insulation. Such limits are given in IEC 60076-7 [17]
which are 5 % current THD at rated load.
•
•
•
If harmonics are expected to be elevated, then a k-factor transformer should be used.
High frequency current harmonics, above 2.5 kHz, have been observed from the inverters
entering the transformers [61], which might not be measured using standard metering.
Surges and transients from VCB restriking have been suspected to damage insulation.
Some operators have requested complex transformers which provide reactive power compensation
using a STATCOM connected to the tertiary winding. This STATCOM adds harmonics which should be
considered during the design and testing stages as previously stated.
Operators have also acquired transformers which have been designed to work at two different primary
side voltages, so the transformer can be adapted for temporary grid connections (i.e. initial temporary
connection at 230 kV voltage level and later, a permanent connection at 400 kV). The design,
manufacturing and testing of these units should be thoroughly reviewed.
The demand of two equal low voltage windings is common for renewable applications, which requires
detailed transferred-voltage calculations, and tests, to avoid issues during operation if one low voltage
delta winding is opened.
92
TB 939 - Analysis of AC transformer reliability
Renewable application transformers in node substations can be subjected to excessive overvoltage
caused by extremely fast reactive power compensation. As a consequence, the change in voltage cannot
be controlled by the OLTC in the transformer. Thus, the voltage can rise above the rated parameters
of the transformer. This overvoltage should be analysed by the purchaser and considered if needed
during the design stage.
In some countries in Europe, the TSO is requesting that renewable generation transformers include an
OLTC to connect to 420-380 kV or 235-205 kV voltage levels. In addition, the allowable frequency range
is 48-51.5 Hz, with a 3 second disconnection time if the frequency falls below 48 Hz. The disconnection
time if the frequency rises above 51.5 Hz is to be agreed with the TSO. Such requirements may impact
the transformer design complexity and should be considered at the early design stages.
8.1.3
Shunt reactor reliability
In total, fifteen failures were reported when the reactor was scrapped. The findings were:
•
•
•
•
•
Seven bushing failures. Failure was independent of voltage, ranging from 145 kV to 735 kV.
One winding to ground insulation.
Two HV lead exits.
Three core and magnetic circuit.
Two winding faults.
In total, fifteen failures were reported when the reactors were scrapped, given in Figure 84 and Table
28.
Figure 84: Age distribution of failed reactors.
In terms of known causes:
•
•
•
•
Three events caused by overvoltage or lightning.
Five instances of materials being the problem.
Two cases where paper ageing was to blame.
One event of loss of cooling.
Detection:
•
•
•
•
•
Eight detections by differential protection.
One by Buchholz relay.
One site inspection.
Two by pressure relief valve (although tank ruptured).
Three instances by online monitoring systems.
93
TB 939 - analysis of AC transformer reliability
Table 28: Shunt reactor failures resulting in the unit being scrapped.
MVA
kV
No.
phases
Cooling
Insulation
20
145
3
ONAN
OIP
100
400
3
OFAF
100
400
3
100
300
50
Age
Outcome
Cause
17
Explosion
w/o fire
HV
Bushings
Dielectric
Material
Differential
protection
relay
OIP
18
Fire
HV
Bushings
Dielectric
Material
Buchholz
relay
OFAF
OIP
19
None
Winding to
Ground
Insulation
Dielectric
Material
Site
Inspection
3
ONAF
OIP
21
Explosion
with fire
HV
Bushings
Dielectric
Overvoltage,
Lightning
Differential
protection
relay
145
3
ONAN
OIP
26
Explosion
with fire
HV
Bushings
Unknown
Unknown
Differential
protection
relay
110
735
1
ONAN
OIP
31
Explosion
w/o fire
HV
Bushings
Mechanical
Material
Differential
protection
relay
-
500
1
ONAF
OIP
32
Explosion
with fire
Bushings
Dielectric
Unknown
Differential
protection
relay
110
735
1
ONAN
OIP
35
None
Core
and
magnetic
circuit
Mechanical
Other
reasons
Differential
protection
relay
(years)
94
Detection
TB 939 - analysis of AC transformer reliability
60
330
3
ONAN
OIP
35
None
Winding
Dielectric
Paper
ageing
Differential
protection
relay
110
735
1
ONAN
OIP
36
Explosion
w/o fire
HV
Bushings
Mechanical
Other
reasons
Differential
protection
relay
Paper
ageing
Online
Monitoring
system
110
735
1
ONAN
OIP
38
Leakage
only
Core
and
magnetic
circuit
Physical
chemistry
(including
oil,
corrosion,
contaminati
on etc)
55
735
1
ONAN
OIP
40
None
Core
and
magnetic
circuit
Dielectric
Material
Online
Monitoring
system
45
512
1
ONAN
Oil + SF6
bushing
44
Explosion
w/o fire
HV
Exit
Lead
Dielectric
Overvoltag
e, Lightning
Pressure
relief device
45
512
1
ONAN
Oil + SF6
bushing
44
Explosion
w/o fire
HV
Exit
Lead
Dielectric
Overvoltag
e, Lightning
Pressure
relief device
150
315
3
OFAF
OIP
45
Leakage
only
HV Winding
Thermal
Loss
cooling
Online
Monitoring
system
95
of
TB 939 - analysis of AC transformer reliability
9
Conclusion and Recommendations
In conclusion, the power transformers have overall become more reliable over the years, which is
unsurprising given the efforts made by the industry to improve technology and management. The
overall hazard rate now was found to be less than half what it was for the last A2.37 survey, in the
order of several tenths of one percent.
Of the 425,000+ transformer-years of operation collected there were 1,204 major failures and 1,916
retirements from 66 utilities in 27 different countries. Overall, the worldwide failure rate has fallen since
the 2015 survey, with the hazard rate now less than half (0.27 %). Given the extensive effort made by
the industry to improve reliability, this is not surprising. The failure rate increases as the voltage class
rises. It is highest in transformers with voltage class ≥ 700 kV (1.46 %) while lowest (0.23 %) in class
100 ≤ U < 200 kV. This too is expected because of the higher voltage stresses. The surveyed population
primarily comprises substation transformers, accounting for 96 % of the total. Shunt reactors and
generator step-up transformers contribute 2.3 % and 1.7 %, respectively. Compared to substation
transformers the failure rate of GSU’s is higher in almost all voltage classes. This may be because GSU
units are usually operated continuously at high load.
Windings related failure (37 %) proved to be the largest contributor to major failures, irrespective of
the transformer voltage class. Previous studies have indicated bushings and OLTCs being leading causes
of failure, however new technologies have reduced this. Substation transformers had higher
contributions of winding failures than GSU transformers and shunt reactors. This may be because
substation transformers are electrically closer to downstream faults which cause winding deformation.
Shunt reactors on the other hand had higher contributions of bushing related failures than GSU and
substation transformers. Electrical and dielectric mode failures were the most prominent irrespective of
the voltage class of transformers. Substation and GSU transformers had higher contributions of
electrical type failures, while shunt reactors had higher contributions of dielectric type failures. In root
cause analysis study, external short circuits appeared to be the major contributor. However, the root
cause of a large portion of the population was unknown and, in some cases, different failure causes
were present. Thus, results of root cause analysis should be evaluated with caution.
Differential protection and Buchholz relay are the most common devices in all voltage classes to detect
major failures. Approximately one quarter of all major failures resulted in an explosion, and of these
there was a fifty-fifty chance of a fire. While this study found that 41 % of transformers were scrapped
as the result of a fault, this may have changed caused by the current global supply chain problems.
In terms of hazard rates, in many cases two Weibull distributions were used to model failure, one for
random events and the other for age-related failures. For all-major failures (both repaired or scrapped),
the hazard curve was fairly random with a slow increase with age. However, when focussing on only
scrapped units the hazard rate started to accelerate after a few decades. One reason could be that
some subcomponents are not designed to last the full operating life of the transformer, so when they
fail they are unlikely to result in the transformer having to be scrapped. There may be economic
considerations whether to repair a transformer after a fault versus scrap, but as these considerations
may be different between countries they were not explored. Across continents the hazard rates were
broadly consistent. However, in Oceania many transformers are retired rather than run to failure, and
so a lower proportion are surviving at an old nameplate age compared to other regions.
As transformers can be retired, as well as run to failure, a hazard rate for retirements was also given.
It is realised that the retirement rate can be self-fulfilling, as owners make decisions when to remove
a transformer. The criteria for when to retire was not explored. For instance, risk can be used based
on the consequence of a failure, the disruption to the customer, and ease of restoration. Regulatory
pressures such as outage fines can also impact decisions.
The potential impact of new generation technology such as wind and solar farms was investigated.
However, there is still an insufficient number of service years of transformer operation. A known
problem is extra heating caused by current harmonics. However, a utility can control this through
connection agreements with the generator and measure the harmonic spectrum. Another option is to
derate the transformer in accordance with a k-factor.
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TB 939 - analysis of AC transformer reliability
Reasons why reliability has improved were also explored. Power transformer reliability and maintenance
strategies have evolved across various regions since the last comprehensive update in 2015. Updates
gathered from ASEAN, Australia, New Zealand, North America, South Africa, and Spain show the diverse
approaches and advancements made to quantify reliability and ensure the resilience and longevity of
transformer fleets. In ASEAN, a shift towards proactive maintenance regimes, adoption of advanced
monitoring technologies, and strategic specification improvements reflect a concerted effort to mitigate
risks and enhance asset performance, particularly in the face of challenging environmental conditions.
Similarly, utilities in Australia and New Zealand have made strides in data collection and analysis,
leveraging collaborative efforts to gain valuable insights into failure trends and lifecycle management
strategies. The emphasis on statistical calculations and comprehensive reporting underscores a
commitment to informed decision-making and regulatory compliance. In North America, a steady
improvement in transformer reliability over the past decade is attributed to a multifaceted approach
encompassing improved maintenance practices, planned interventions, and reduced false trips. The
downward trend in transformer failures underscores the effectiveness of these measures in enhancing
grid resilience and operational efficiency. South Africa's experience highlights the impact of targeted
modifications, such as the transition to resin-impregnated bushings, in mitigating catastrophic failures
and informing future asset management strategies. In Spain, a focus on technical specification updates
and maintenance enhancements underscores a proactive approach to optimising transformer
performance and longevity. The integration of fibre optic temperature sensors, variable frequency
power factor measurements, and advanced monitoring equipment underscores a commitment to
leveraging cutting-edge technologies for real-time asset management and risk mitigation.
Collectively, these insights underscore the importance of continuous innovation, collaboration, and
data-driven decision-making in ensuring the reliability, resilience, and longevity of power transformer
fleets in an ever-evolving energy landscape. As utilities navigate emerging challenges and opportunities,
the exchange of best practices and lessons learned will remain critical in driving ongoing improvements
and advancements in transformer reliability and maintenance strategies.
Technical specifications play a crucial role in defining various aspects, including industry standards,
characteristics, manufacturing requirements, and testing protocols. These specifications significantly
influence transformer reliability and performance. Key areas covered include performance and service
conditions, insulation levels, dielectric tests, thermal performance, and ageing considerations. Emphasis
is placed on managing insulation material condition through measures like controlling moisture ingress
and specifying breathing systems. By adhering to international standards and leveraging resources like
the CIGRE Technical Brochure 528, stakeholders can formulate robust technical specifications tailored
to their needs, ensuring transformer integrity and longevity.
Design reviews, as outlined in the CIGRE Technical Brochure 529, are pivotal for confirming that the
design meets performance requirements and utilises proven materials and methodologies. Recent
advancements in transformer design, particularly in dielectric, mechanical, thermal, bushing, and tap
changer designs, have significantly enhanced reliability. Thermal design advancements employ
simulations and temperature rise tests to optimize cooling and prevent overheating. Bushings and tap
changers have also seen notable improvements, with increased reliability through synthetic insulation
and vacuum-type technology, respectively. Maintenance strategies, including online monitoring, help
detect issues early, reducing the risk of catastrophic failures.
A reliable production of power transformers is intricately linked to safe, clean, and efficient
manufacturing processes, supported by well-trained personnel. Given their physical size, weight, and
cost, power transformers require meticulous attention throughout their design, manufacturing, and
testing phases. To ensure consistent reliability, adherence to audited Quality Management Systems,
such as ISO9001, is essential. Manufacturing technology continues to evolve, offering superior
techniques aimed at reducing production time while enhancing product quality. From core assembly to
winding processes, advancements in automation and precision contribute to improved efficiency and
performance. Moreover, the selection and application of insulating materials play a critical role in
ensuring the longevity and operational integrity of transformers.
The deployment of new inspection and quality control technologies, both in factory assembly and onsite operations, further increase the reliability of power transformers. By leveraging tools such as PD
measurement, FRA and PDC testing, manufacturers can proactively identify potential issues and ensure
compliance with acceptance criteria. On-site manufacturing and testing technologies have emerged as
97
TB 939 - analysis of AC transformer reliability
indispensable components of transformer production, particularly for large-scale installations. These
approaches not only enhance safety but also streamline logistics and mitigate risks associated with
transportation. Digital innovation, such as online monitoring and remote diagnostics, offer insights into
transformer performance, strengthening predictive maintenance strategies and minimizing downtime.
However, these technologies must be carefully integrated and configured to maximize their
effectiveness and security. Looking ahead, the power sector must embrace digital transformation across
the entire lifecycle of transformers, from design and manufacturing to operation and maintenance. By
leveraging cutting-edge technologies and best practices, stakeholders can ensure the resilience and
reliability of power infrastructure in the face of evolving operational challenges and dynamic grid
environments.
Maintenance activities conducted throughout the lifespan of a transformer play a pivotal role in ensuring
its reliability and longevity. By adhering to manufacturer-recommended maintenance schedules and
employing advanced testing methods, potential issues can be detected early and addressed proactively,
thereby averting costly failures. In regions with ageing transformer infrastructures like North America,
there is a growing emphasis on extending transformer life through strategic refurbishments, which offer
a cost-effective alternative to replacement and mitigate the risks associated with potential failures.
Recommendations from this study are as follows.
•
•
•
•
Periodical review of operational data, for instance for new types of renewable generation and
transformer reliability.
For all major failures, whether the transformers were repaired or scrapped, the hazard curve
showed a fairly random pattern with only a slow increase over time. This indicates that TimeBased Maintenance is not effective for substation transformers. Instead, maintenance should
be planned based on the actual condition of the transformers, which can be assessed through
diagnostic measurements and online monitoring.
Provision of guidance when to retire a transformer, not just a technical focus but also linked to
the regulatory environment and community expectations. It is realised that different owners
will perceive risks differently based on the local operating environment.
A substantial proportion of failure causes were attributed to unknown and other reasons.
Although the causes suggested in the questionnaire actually cover most possible causes this
number has increased in comparison with WG A2.37. This high percentage might suggest more
focus on failure investigation procedures is required by utilities, because the primary step
toward enhancing reliability is understanding the root cause of failures.
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TB 939 - analysis of AC transformer reliability
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TB 939 - analysis of AC transformer reliability
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Paris, France, June 2013.
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[26] CIGRE, "Transformer Bushing Reliability, Technical Brochure 755," Cigre, Paris, France, 2019.
[27] IEEE, "Guide for the Dielectric Frequency Response Measurement of Bushings, IEEE Std
C57.12.200," IEEE, USA, 2022.
[28] IEC 60599, "Mineral oil-filled electrical equipment in service - Guidance on the interpretation of
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[29] S. Montenegro, N. Viveiros, Y. Nomi and R. Asano Jr., "Selection of high voltage bushings for
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case," in Cigre Session, Paris, France, Aug. 2022.
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Technical Brochure 530," CIGRE, Paris, France, 2013.
[32] CIGRE WG A2.53, "Advances in the interpretation of transformer Frequency Response Analysis
(FRA), Technical Brochure 812," Cigre, Paris, France, 2020.
[33] T. Nganpitak, P. Nimsanong and S. Maneerot, "Dielectric response analysis of mineral oil
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transformers," in CIGRE Session, Paris, 2018.
[35] T. Kobayashi, "Quality control and site test for site assemble transformers," in CIGRE SC A2
Colloquium, 2017.
[36] Y. Li, "Experiences from transformer onsite refurbishment, A2-317," in CIGRE Session, Paris,
France, 2020.
[37] M. Szrot, J. Plowucha, R. Kubicki and R. Malewski, "On-site replacement of OLTC, drying of
winding insulation, induced voltage test with PD measurement of 250 MVA, 400-110kV, 40 years
old transformer, A2-303," in CIGRE Session, Paris, France, 2016.
[38] E. G. teNyenhuis, "Advancements in transformer site dryouts," in CIGRE Session, Paris, France,
2020.
[39] U. Kaltenborn, S. Bergmann and M. Hensel, "Field testing of power transformers," in Cigre
Colloquium, New Delhi, 2019.
[40] CIGRE WG A2.59, "On-Site Assembly, On-Site Rebuild, and On-Site High Voltage Testing of Power
Transformers, Technical Brochure 857," CIGRE, Paris, France, 2021.
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[41] E. G. teNyenhuis, M. L. Diaby, G. Pajaro and J. Szczechowski, "Benefits of high voltage testing at
site for power transformers," in CIGRE Session, Paris, France, 2018.
[42] CIGRE JWG A2/D1.51, "Improvements to PD measurements for factory and site acceptance tests
of power transformers, Technical Brochure 861," CIGRE, Paris, France, 2022.
[43] IEC 60567:2011, "Oil-filled electrical equipment - Sampling of gases and analysis of free and
dissolved gases - Guidance," IEC, 2011.
[44] IEEE C57.104-2019, "IEEE Guide for the Interpretation of Gases Generated in Mineral OilImmersed Transformers," IEEE, 2019.
[45] C. Y. Perkasa, N. Lelekakis, T. Czaszejko, J. Wijaya and D. Martin, "A comparison of the formation
of bubbles and water droplets in vegetable and mineral oil impregnated transformer paper," IEEE
Transactions on Dielectrics and Electrical Insulation, vol. 21, no. 5, pp. 2111-2118, 2014.
[46] CIGRE, "Economics of transformer management, Technical Brochure 248," Cigre, Paris, France,
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[47] IEC62443-4-2, "Security for industrial automation and control systems - Part 4-2: Technical
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[48] IEEE 1686-2022, "IEEE Standard for Intelligent Electronic Devices Cybersecurity Capabilities,"
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[55] D. Martin, T. K. Saha, G. Buckley, S. Chinnarajan and T. MacArthur, "Analyzing Differences in
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[58] L. J. Bain and M. Engelhardt, "Approximate Distributional Results Based on the Maximum
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[60] D. Martin, J. Marks, T. Saha, O. Krause and N. Mahmoudi, "Investigation into Modelling Power
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11 Appendix
A) Questionnaire – Excel Sheets
Figure 85: Excel questionnaire – Population data.
103
TB 939 - analysis of AC transformer reliability
Figure 86: Excel questionnaire – Age distribution.
Figure 87: Excel questionnaire – Failure and retirement data.
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TB 939 - analysis of AC transformer reliability
B) Questionnaire – Content of Pull-down Menus
1- IDENTIFICATION OF THE UNIT
1.1 Application
Substation
GSU - Thermal
GSU - Hydro
GSU - Wind
GSU - PV
Shunt Reactor
2 - FEATURES OF THE UNIT
2.3 Number of Phases
Single Phase
Three Phase
2.4 Cooling System
ONAN
ONAF
OFAF
ODAF
OFWF
ODWF
2.5 Type of Tap Changer
On-Load Tap-Changer
Off-Circuit Tap-Changer (OCTC)
Without Tap Changer
2.6 Type of HV Bushing
Oil Impregnated Paper (OIP)
Resin Impregnated Paper (RIP)
Resin Impreganted Synthetic (RIS)
Oil SF6 bushing
Cable Box
unknown
3 - DETAIL OF OCCURENCE
3.3 Reason for Disconnection
Retirement due to network requirements
Retirement due to condition/age (w/o tripping)
Major Failure (with tripping)
4 - CONSEQUENCES OF FAILURE
4.1 External Effects
None
Leakage only
Fire
Explosion w/o fire
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TB 939 - analysis of AC transformer reliability
Explosion with fire
4.2 Failure Location
Winding
Tapping Winding
Lead Exit
Tapping Leads
Phase to Phase Insulation
Winding to Ground Insulation
Winding to Winding Insulation
Electrical Screen
Bushings
Core and magnetic circuit incl. clamping
Flux Shunts
Tank, Conservator, Piping
Cooling unit, Pump, Fan, Radiator
Tap Changer
Internal Surge Arresters
CT
Unknown
4.3 Failure Mode
Dielectric
Electrical
Thermal
Physical chemistry (including oil, corrosion, contamination etc)
Mechanical
Unknown
4.4 Failure Cause
Transport, Handling or Storage
Installation on-site
Improper maintenance or repair
Paper ageing
Overload
Overvoltage, Lightning
Overheating
Corrosive Sulphur
Oil Quality
External short-circuit
Geomagnetic Induced Currents
Seismic Disturbances
External Pollution
Loss of cooling
Loss of clamping pressure
Collateral Damage
Vandalism
Material
Other reasons
Unknown
4.5 Action
Onsite Repair < 1 week
Onsite Repair > 1 week
Onsite Repair > 1 month
106
TB 939 - analysis of AC transformer reliability
Repair in workshop
Scrapping
Unknown
4.6 Detection Mode
Site Inspection
Online Monitoringsystem
Laboratory DGA, Oil Quality Test
Other offline Diagnostics
Overcurrent relay
Differential protection relay
Buchholz relay
Pressure relief device
Unknown
107
TB 939 - analysis of AC transformer reliability
C) Determination of Weibull confidence limits
Confidence limits for the Weibull distribution were determined in accordance with [53], which references
[58] and [59].
For the 𝛽 parameter:
𝑟 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑎𝑖𝑙𝑢𝑟𝑒𝑠 𝑎𝑛𝑑 𝑠𝑢𝑠𝑝𝑒𝑛𝑠𝑖𝑜𝑛𝑠
𝑛 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑎𝑖𝑙𝑢𝑟𝑒𝑠, 𝑠𝑢𝑠𝑝𝑒𝑛𝑠𝑖𝑜𝑛𝑠 𝑎𝑛𝑑 𝑐𝑒𝑛𝑠𝑜𝑟𝑠
𝑟
𝑞=
𝑛
𝐶 = 2.14628 − (1.361119 ∙ 𝑞)
The degrees of freedom is calculated, 𝑑𝑓 = (𝑟 − 1) ∙ 𝐶
The chi squared function is used to calculate values, using the degrees of freedom at 0.05 and 0.95
confidence values. In Microsoft Excel, this can be performed using CHISQ.INV(Probability,df), the
probability is 0.05 for the lower confidence range and 0.95 for the upper.
1
𝐶ℎ𝑖𝑆𝑞0.05 1+𝑞2
𝜔1 = (
)
𝑟𝐶
𝛽𝑙𝑜𝑤𝑒𝑟 = 𝜔1 ∙ 𝛽
1
𝐶ℎ𝑖𝑆𝑞0.95 1+𝑞2
𝜔2 = (
)
𝑟𝐶
𝛽𝑢𝑝𝑝𝑒𝑟 = 𝜔2 ∙ 𝛽
For the 𝜂 parameter
𝐴4 = 0.49 ∙ 𝑞 − 0.134 + 0.622 ∙ 𝑞 −1
𝐴5 = 0.2445 ∙ (1.78 ∙ 𝑞) ∙ (2.25 + 𝑞)
𝐴6 = 0.029 − 1.083 ∙ ln(1.325 ∙ 𝑞)
𝑋 is the fractile of the normal distribution. Following the standard, p=0.05, 𝑋 is calculated in Excel for
a normal distribution mean = 0 and standard deviation = 1, using NORM.INV(1-p,0,1).
𝐴3 = −𝐴6 ∙ 𝑋 2
𝑑1 =
𝑑2 =
𝐴3 + 𝑋 ∙ √𝑋 2 ∙ (𝐴62 − 𝐴4 ∙ 𝐴5) + 𝑟 ∙ 𝐴4
𝑟 − 𝐴5 ∙ 𝑋 2
𝐴3 − 𝑋 ∙ √𝑋 2 ∙ (𝐴62 − 𝐴4 ∙ 𝐴5) + 𝑟 ∙ 𝐴4
𝑟 − 𝐴5 ∙ 𝑋 2
𝑑1
𝑎1 = exp −
𝛽
𝑎2 = exp −
𝑑2
𝛽
𝜂𝑙𝑜𝑤𝑒𝑟 = 𝑎1 ∙ 𝜂
𝜂𝑢𝑝𝑝𝑒𝑟 = 𝑎2 ∙ 𝜂
108
ISBN : 978-2-85873-644-7
TECHNICAL BROCHURES
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Reference 939 - September 2024