INNOVATIVE PARTIAL DISCHARGE
MEASUREMENT AND ANALYSIS TECHNIQUE
FOR THE EVALUATION OF THE RELIABILITY OF
INSULATION SYSTEMS
F. Sciocchetti* (TechImp Srl, Italy), A. Caprara (TechImp Srl, Italy), F. Puletti (TechImp Srl, Italy)
G. C. Montanari** (University of Bologna, Italy), A. Cavallini (University of Bologna, Italy)
Summary: The aim of this paper is to present a
novel approach to partial discharge detection and
analysis that is able to enhance the effectiveness of
asset management based on condition assessment of
electrical apparatus and components (i.e. cables,
motors, generators, transformers,
GIS, AIS,
insulators, etc.). Diagnosis of insulation system
degradation in-factory quality control, on-site afterinstallation tests, and on-line partial discharge
monitoring of electrical apparatus are examples of
applications of this methodology. The PD analysis
tool described here is based on innovative hardware
and software solutions. The broadband PD detector
is designed to record a large number of partial
discharge waveforms and split the acquired pulse
dataset into homogeneous sub-groups, characterized
by similar pulse shapes. The different sub-groups
result to be associated to different PD sources or
noise. Artificial intelligence techniques enable noise
rejection and identification of the defects generating
PD for each sub-group of data. A localization of the
defect is achievable as well and this enhances the
effectiveness of the condition based maintenance
action.
Keywords: Partial discharge - Insulation system Electrical apparatus - Diagnosis - Maintenance Asset management - Risk assessment.
1. INTRODUCTION
Condition-based maintenance (CBM) of electrical
apparatus can be supported by partial discharges (PD)
analysis, as PD can be at the same time cause and effect
of electrical insulation degradation. In fact, an abnormal
presence of PD may indicate that degradation
phenomena are occurring. Likewise, organic insulation
systems can be significantly affected by PD, which can
become the prevailing degradation phenomenon leading
to insulation breakdown. In many cases, an outage
caused by breakdown of an electrical apparatus might
cause serious technical and financial consequences, as it
can happen, e.g., for a power transformer in a
transmission line or an oil pump in a refinery. The
assessment of the state of the insulation system of such
equipment helps in preventing fault occurrence.
Service experience on electrical apparatus reveals
that deep and critical investigation on PD phenomena is
necessary. Sometimes, in fact, failures can occur
without any apparent change in PD activity, while other
times large PD readings are observed, but they are not
associated to incoming breakdown. This can be
addressed to an inappropriate way of recording PD,
rather than to anomalous behaviour of PD phenomena.
Ultimately, misinterpretations of PD records lead to
unsatisfactory evaluation of the state of the apparatus or
component under investigation.
Another problem is that PD detectors that provide the
apparent charge and the occurrence phase for each PD
event may be not able to sort out noise from PD pulses
[1,2]. In fact, voltage correlated disturbances and noise
(as corona discharges, arc welders, electronic switch
commutation) or uncorrelated noise (as broadcasting
signals) can propagate into the measurement circuit and
affect the PD readings. A misinterpretation of such
phenomena as real PD activity in the object under test
can lead to stop incorrectly electrical system operation.
Further problems may come from the acquisition
band of PD detectors, which may be not suited for the
frequency content of discharge pulses so that PD
__________________________________________________________________________________________________________________________________________________________
*
fsciocchetti@techimp.com - Techimp Srl, Via Toscana 11/C, 40069, Bologna.
giancarlo.montanari@mail.ing.unibo.it - Università di Bologna, Facoltà di Ingegneria, Viale Risorgimento 2, 40123, Bologna.
**
Page 2 of 7
activity may be underestimated or missed. Moreover an
overlapping of PD patterns relevant to multiple PD
sources can occur. When multiple discharge activities
are present, some phenomena can be hidden and not
always the prevailing activities are the more harmful. In
rotating machines, for instance, surface discharges, not
particularly harmful, may hide slot discharges,
potentially more dangerous for insulation degradation
[3]. As a result, ageing can progress up to breakdown
virtually unnoticed.
Therefore, in many cases, the operator that has to
take the responsibility to interpret what is really
happening inside the tested apparatus must base his
evaluations on insufficient or misleading PD data.
Information about PD phenomena can be increased
following an innovative approach, presented here,
which is based on the sampling of the complete PD
waveform for a large number of PD pulses (not only of
peak and phase, as it is done by common PD detectors),
the separation of the different contributions and the
enhanced processing based on statistical processing of
PD data and artificial intelligence (AI) techniques able
to provide an automatic identification of the defect
generating PD.
In the following, besides a brief explanation of the of
the innovative PD analysis tool, examples relevant to its
application on field to different electrical apparatus are
presented.
(W). These numbers, defined, e.g., in [4] and proposed
in [5] as a tool for PD analysis, are sufficient, in most
cases, to put in evidence differences between noise and
PD pulses, as well as between pulses due to different
PD sources and/or PD locations. Thus, they can be
successfully employed for achieving noise rejection and
source separation. Yet, for particular applications,
additional features can be implemented in the DSP
firmware.
Fig. 1. Acquisition on a pulse-per-pulse basis. TW is the acquisition
window length, TP is the pre-trigger time.
2. THE MEASUREMENT SYSTEM
The PD analysis tool described in this paper records
and stores PD on pulse-per-pulse basis as it is
schematized in Fig. 1. The amount of acquired samples
is limited by means of a trigger condition that avoids
recoding useless signals. A trigger threshold is set and
only when the input signal (that may come from any
kind of sensor e.g. coupling capacitors, radio frequency
current transformers, Rogowsky coils) exceeds it, the
system acquires the pulse signal. Each pulse is acquired
for a total time TW (window length). A pre-trigger time,
TP, is also used to observe the behaviour of the PD pulse
before the trigger level has been exceeded. Both TW and
Tp can be set by the user.
The measurement system is schematized in the block
diagram of Fig. 2. A fast analog/digital (A/D) converter
and floating point digital signal processor (DSP) allows
the system to:

Digitize the whole PD pulse shape at a 100 Ms/s
rate (the instrument bandwidth has upper limit at
around 40 MHz).
 Evaluate PD pulse apparent charge via digital
filters.
 Extract information about the PD pulse shape.
 Store the data into 16 Mb memory.
Each acquired pulse waveform is processed by the
DSP and some information is extracted, such as
equivalent time length (T) and equivalent bandwidth
A/D: Analog to Digital converter
S/H: Sample and Hold
Trigger: Trigger system
DSP: Digital Signal Processor
Fig. 2. Layout of the measurement system. 1: PD pulse signal, 2:
digitized PD pulse signal, 3: pulse features, 4: array of PD pulse
features, 5: PRPD sub-pattern relevant to one class-homogeneous
subset.
Once a number of PD pulses sufficient to achieve
suitable statistics of PD-associated quantities has been
collected, the recorded data are sent to a remote host
computer for post-processing. Post-processing consists
of:
1.
2.
3.
Separation: the original Phase Resolved Partial
Discharge (PRPD) pattern is divided into
subgroups, each one relevant to one type of impulse
signal. The separation is accomplished through
clustering of the TW map build with pulse features
[6]
Noise rejection: it is achieved by analyzing some
stochastic characteristics of PRPD sub-pattern [7].
Identification: each subgroup that is not recognized
as noise is associated to one or more defect
Page 3 of 7
macrocategory analyzing some quantities extracted
from the PRPD sub-pattern [7].
Separation
Separation is performed by the Unsupervised
clustering block in Fig. 2. Thanks to a fuzzy classifier,
the block splits the measured dataset grouping together
PD pulses having similar equivalent time length and
equivalent bandwidth (thus, similar shape).
Noise rejection
Noise rejection is performed through different
algorithms addressing the recognition of different kind
of noises. For instance 6-pulse rectifiers are
recognizable since the PRPD pattern produced by these
devices is given by six groups of pulses having phase
angles separated by one sixth of period. A thorough
discussion of noise rejection can be found on [6].
Identification
Identification is performed by a three level fuzzy
inference engine (FIE) [7]. At the first level, PD
activities are categorized among three broad categories
that are related with the physics associated with PD.
This response provides a first indication of the defect
harmfulness, then more and more specific information
on the phenomena occurring to the considered test
object is provided by the second and third level of
identification (devised with the purpose of refining risk
assessment).
a- First level identification
The starting point of identification is a group of
routines that extracts statistical markers from the PRPD
sub-patterns. These markers were selected carefully,
trying to reduce ambiguities in defect identification [7],
as well as robustness regarding defect location in the
apparatus under test.
At the first level, defects are addressed to three
macro-categories, that is:



Internal PD: discharges occurring in air gaps
surrounded by solid dielectric or solid dielectric and
metallic
electrodes,
involving
significant
components of electric field orthogonal to electrode
surfaces.
Surface PD: discharges that develop on air/solid
insulator interfaces or liquid/solid insulator
interfaces, involving significant field component
tangential to the surface.
Corona PD: discharges produced by metal electrode
in open air (gas).
b- Second level identification
The second level identification becomes accessible if
the defect has been tagged as predominantly internal.
This level provides indication about the closeness of
internal defects to either HV or LV electrodes, as well
as on the possible presence of electrical trees, which is
particularly useful for assessing fault severity in
polymeric insulation systems [8].
c- Third level identification
In order to provide a more informative output
regarding PD source typology, the first and second level
identification results are specialized for a given type of
apparatus, such as cables, rotating machines or
transformers. As an example, in the case of rotating
machines, PD relevant to delamination, slot, endwindings defects can be assessed [9].
3. APPLICATION TO CABLE SYSTEMS
In this section the results relevant to MV polymeric
cable testing are presented. The cable was supplied off
line by a resonant test set. An example of phaseresolved PD (PRPD) pattern recorded at a joint is
reported in Fig. 3.
Fig. 3. PRPD pattern due to PD activity inside a HV cable joint plus
pulsed interference due to the resonant test set supplying te cable for
the off line test..
This pattern shows different contributions due to
pulses generated by the electronic components of the
power supply system and PD activity. It is noteworthy
that electronics interference has a so large height that it
can hide the real PD activity. Therefore, when using PD
detectors that report only the maximum PD amplitude
per phase channel (an option available on some
commercial apparatus), the PD contribution shown in
the pattern reported in Fig. 3 might appear as
completely undetectable.
The measurement approach here described, on the
contrary, puts in evidence the difference in the recorded
pulse shapes by analyzing the relevant T-W map. Figure
4 shows that PD pulses have larger frequency content
(therefore larger equivalent bandwidth) than power
electronics pulses. It is thus easy to separate power
electronic pulses in order to achieve only the subpatterns characteristic of the PD contribution, as shown
in Fig. 5. As the last step, the sub-pattern shown in Fig.
5 was processed by the fuzzy identification system,
which provided 100% likelihood of internal discharges
(see Fig. 6). Intensity and nature of discharges
suggested substitution of the defective joint (risk
assessment). After joint replacement, PD tests on all the
Page 4 of 7
cable system did not provide anymore evidence of PD.
Further joint analysis carried out in laboratory
confirmed the internal nature of the recorded PD
activity.
signals originated within other machines connected to
the same busbars in the plant. Moreover, cross talk
phenomena make acquired data often difficult to be
analysed. By means of the PD diagnostic system here
presented these issues can be successfully addressed. In
the following, the results of an on-line PD test carried
out on an hydraulic generator running close to full
power are presented. An example of phase resolved PD
pattern obtained from one phase is presented in Fig. 7.
Fig. 4. Equivalent time length and equivalent bandwidth (T-W map)
for the pulses that generate the pattern shown in Fig. 3. The
contribution of PD is indicated (the remaining data are relevant to
noise).
Fig. 7. PRPD pattern recorded at the coupler of an hydraulic generator
The pattern reported in Fig. 7 exhibits an interesting
behaviour. By analysing the PD pulse shapes, it was
possible to realize that three different kind of pulses (A,
B and C) were detected, see Fig. 8.
Fig. 5. Sub-pattern derived by the T-W map of Fig. 4 showing the PD
contribution to the PRPD pattern of Fig. 3.
A
B
C
Fig. 8. Different categories of pulses observed in a hydraulic generator
while running close to full power (see Fig. 7).
Fig. 6. Identification of the sub-pattern shown in Fig. 5.
4. APPLICATION TO GENERATORS
Generator condition assessment through PD testing is
a well-established practice [10]. However, several issues
are still to be faced as regards PD detection and
analysis, particularly as far as on line testing is
concerned.
In fact, often multiple source of PD are active within
the machine under test and several external phenomena
are coupled into the machine trough the busbars. As a
matter of fact, not only can the busbars be source of PD,
but they are also a preferred transmission path for
The PRPD sub-pattern relevant to the pulses (A) and
(B) are shown in Fig. 9. The first sub-pattern is caused
by a delamination phenomenon at the conductor site.
The corresponding pulse A of Fig. 8 shows a smooth
behaviour indicating that it has been somehow filtered
while travelling from the defect to the coupler site
[11,12]. The identification for phenomenon (A) was
performed using the 3rd level identification routines for
rotating machines. The response, shown in Fig. 10, was
Conductor delamination. The second sub-pattern (B)
closely recalls the classic “rabbit ear-like” pattern,
typical
of
insulation-bounded
cavities.
The
Page 5 of 7
corresponding pulse is characterized by high frequency
components (pulse B of Fig. 8). Since high frequency
components are quickly attenuated while travelling on
the stator windings, it can be argued that the defect site
is quite close to the coupler [11,12]. By joining these
considerations, it was speculated that the high voltage
wire connecting the machine bus bars to the coupler had
some internal defect. This hypothesis was confirmed by
off-line inspections.
be used to remove the noise inside the plant. As an
example, Fig. 11 shows a PRPD pattern with PD
activity partially or completely hidden by noise. The
corresponding T-W plot is shown in Fig. 12. In this case,
PD pulses have a smaller frequency content than noise
pulses and noise can be rejected straightforwardly after
separation. In Figure 13 the contribution to the PRPD
pattern of PD, without the influence of noise is shown.
A
Fig. 11. PRPD sub-pattern recorded at high voltage transformer
terminations.
Noise
B
PD
Fig. 9. Sub-patterns (of pattern reported in Fig. 7) relevant to the
pulses groups A and B of Fig. 8.
The pattern relevant to pulse (C), populated by very
few discharges and for this reason not reported here,
was recognized as noise. In fact it displayed a series of
six dots separated by one sixth of the period of the
fundamental supply. This regular behaviour was
ascribed by software routines to pulses associated to the
commutation of the AC/DC converter used to supply the
rotor of the machine [6].
Fig. 12. T-W map for the pulses related to the pattern of Fig. 11.
Fig. 13. Sub-pattern with the PD contribution to the PRPD pattern
shown in Fig. 11.
rd
Fig. 10. 3 level identification of the sub-pattern reported in Fig. 9(A)
5. APPLICATION TO CAST-RESIN TRANSFORMERS
The apparatus here considered is a high-frequency
HV transformer, resin insulated. The goal is to establish
a methodology for quality control, meeting the stringent
requirements associated to the application of this kind of
transformers.
At a first level, the innovative PD analysis tool can
The most interesting aspect is, perhaps, the information
that can be derived from the extracted sub-pattern
through the identification algorithm. The sub-pattern
reported in Fig. 13 was first ascribed to a predominantly
internal defect.
The second level of identification was, therefore
activated; see Fig. 14, obtaining the indication that the
defect was closer to the HV electrode than to the LV
electrode.
Page 6 of 7
Fig. 14. Result of second level identification showing that the defect
that gives rise to the sub-pattern shown in Fig. 13 is located close to
the HV (High Voltage) electrode.
This diagnosis was successively confirmed by forensics
investigations.
6. APPLICATION TO OIL FILLED POWER TRANSFORMER
The electrical apparatus considered here is a HV selftransformer in service on a transmission line for 7 years
and subjected, therefore, to daily cycling according to
load requirements. Both off-line PD testing and on-line
monitoring were performed detecting the HF signal, as
depicted in Fig.15, by means of High Frequency Current
Transformers (HFCT) placed on the ground connection
of the capacitive taps installed in the high voltage (400
kV, 300 pF) and medium voltage (130 kV, 200 pF)
bushings.
Monitoring system
The monitoring system consists of a PD detection
tool (the acquisition unit, located in proximity of the
transformer) and a data logger unit connected through
fiber optic. The data logger unit is, basically, an
embedded computer which controls the acquisition unit
and collects PD data coming from the equipment under
test and allows the remote control of the monitoring
system. The data thus collected is saved in the unit hard
disk and can be retrieved in several ways using, e.g.,
LAN or Internet connections, GPRS, GSM or
conventional modems. The data logger unit also
evaluates simple statistics on PD activity (e.g., mean
and maximum amplitude and repetition rate) and saves
them into a log file and carries out trend analysis.
Warning messages may be issued immediately when
selected PD statistics exceed critical thresholds.
Rough pulse localization can be achieved taking
profit of distortion and attenuation of high-frequency
components of travelling PD pulses. In fact, pulses
coming from defects close to or far from the coupler are
characterized by large or small equivalent bandwidth,
respectively.
As an example, during offline tests performed on a
different transformer, it was observed that PD generated
in proximity of the 400 kV bushings had an average
equivalent bandwidth of 10 MHz when detected at the
capacitive taps of these bushings. From the capacitive
taps of the 130 kV bushings, the same pulses had an
average equivalent bandwidth of roughly 200 kHz.
Fig. 15. Detection circuit used for PD measurements.
The PD measurements were requested because the
oil analysis, carried out 5 months before PD testing,
indicated a water content (calculated at 20°C) equal to
3%. Gas content in ppm, obtained by Dissolved Gas
Analysis (DGA) is reported in Tab. I.
Tab. I: Dissolved Gas Analysis (ppm) on a HV self-transformer.
H2
CH4
C 2H 4
246
721
729
O2
CO
C2H6
195
268
224
N2
CO2
C2H2
47458
5862
8
According to [13], these levels are compatible with
thermal faults exceeding 700° Celsius degrees and do
not indicate the presence of partial discharges. Although
it is known that units suffering high dissolved gas levels
may operate safely throughout their design life [14], it
was decided to monitor the transformer status through
partial discharge analysis in order to achieve deeper
information on the status of the insulation system of the
transformer and evaluate DGA analysis results.
Fig. 16: Behaviour of mean positive and negative PD pulse amplitude
(in mV) during one week of HV, oil-paper insulated self-transformer..
The weekly behaviour of the PD amplitude levels in
mV (being the transformer a prevailingly inductive
system calibration in pC was not considered
appropriate) is reported in Fig. 16. From this figure it
can be derived that the PD activity is affected by a
considerable variability. As a matter of fact,
environmental conditions, such as temperature and
humidity, have been observed to influence noticeably
PD readings [15], particularly because of the presence
of corona and surface (arcing) discharges on connection
leads and on bushings triggered by conductive and inert
pollution generated by a nearby industrial district.
Indeed, also internal discharges can show a fluctuating
behaviour (thus supporting the use of monitoring
techniques) due to, e.g., load and ambient temperature
cycling, bubbling, etc..
By looking carefully to the acquired pattern and
classification map, it was observed that two concurrent
Page 7 of 7
phenomena are active in the transformer. A small
internal phenomenon and a large surface phenomenon.
The pattern, classification map and sub-patterns relevant
to an acquisition are reported in Fig. 17 and 18.
The first activity (A) is characterized by pulses with
an average equivalent bandwidth exceeding 9 MHz
while the second activity is characterized by an average
equivalent bandwidth of roughly 3 MHz.
7. CONCLUSIONS
The results here presented show interesting
applications of an innovative methodology for PD
analysis, which can become a viable way to achieve
consistent and robust diagnostic indications on electrical
apparatus, based on the enhanced capability of PD
source identification provided by the proposed
approach. This can improve considerably risk
assessment, condition-based maintenance programs and
life extension evaluation of electrical systems.
8. REFERENCES
[1]
[2]
(a)
(b)
Fig. 17: Example of large-equivalent bandwidth PD phenomenon
recorded during offline testing (at 1.0 p.u) on a oil-paper MV
transformer. Fig. 19a: PD pattern. Fig. 19b: T-W map.
[3]
[4]
[5]
[6]
[7]
A
B
Fig. 18: Sub-patterns relevant to two different PD phenomena
overlapped in the pattern of Fig. 18a and singled out by the map of
Fig. 18b.
The response prompted by the automatic
identification tool for the two different activities
indicated that phenomenon (A) was to be traced back to
an internal phenomenon, while the phenomenon (B)
consisted of surface discharges. Furthermore by
analysing the acquired patterns, it was observed that the
seasonal fluctuations registered by the monitoring
systems were to be attributed mainly to the surface
discharges associated to polluted insulators [15].
Internal discharges were disappearing and reappearing,
but with magnitudes fairly below that of surface
discharges and approximately constant for all the
monitoring period.
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
IEC 60270, Partial Discharge Measurements, 3rd edition, March
2001.
R. Bartnikas, and J. H. Levi, “A Simple Pulse Height Analyzer
for Partial Discharge Rate Measurements” (IEEE Trans. on
Instrumentation and Measurements, vol.18, n.2, pp.403-407,
April 1969).
M. Belec, C. Hudon and S. Lamothe, “Recognizing partial
discharge activity using PRPD analysis” (2001 Int. Conf. Of
Doble Clients, April 1-6, 2001 Boston, P-7G).
L. E. Franks, Signal Theory, Prentice-Hall, 1975.
Contin, A. Cavallini, G. C. Montanari, G. Pasini, F. Puletti,
"Digital Detection and Fuzzy Classification of Partial Discharge
Signals” (IEEE Trans. on Dielectrics and Electrical Insulation,
Vol.9, N.3, pp.335-348, June 2002).
Cavallini, A. Contin, G. C. Montanari, F. Puletti, "Advanced PD
Inference in On-Field Measurements. Part.1: Noise Rejection"
(IEEE Trans. on Dielectrics and Electrical Insulation, 2003).
A. Cavallini, M. Conti, A. Contin and G. C. Montanari,
“Advanced PD Inference in On-Field Measurements. Part 2:
Identification of Defects in Solid Insulation Systems” (IEEE
Trans. on Dielectrics and Electrical Insulation, 2003).
A. Cavallini, G. C. Montanari, F. Puletti, “A Fuzzy Logic
Algorithm to Detect Electrical Trees in Polymeric Insulation
Systems” (accepted for publication on IEEE Trans. on DEI, Vol.
12, April 2005).
A. Cavallini, G.C. Montanari, D. Fabiani and E. Vellucci,
“Experience on partial discharge monitoring of power
transformers” (IEEE ISEI, Indianapolis, Indiana (USA), pp. 174177, September 2004).
G. C. Stone, "Tutorial on Rotating Machine Off-Line and Online Partial Discharge Testing" (CIGRE/EPRI Colloquium on
Maintenance of Motors and Generators, Vol. 3, Florence, Italy
1997).
R. Miller, W. K. Hogg, “Pulse Propagation of Slot and Internal
Discharges in Stator Windings of Electrical Machines” (ISH,
paper 63.05, Athens, Greece, August 1983).
H. Shu, I. J. Kemp, “Pulse Propagation in Rotating Machines
and its Relationship to PD Measurements” (IEEE ISEI, pp.411414, Baltimore, USA, June 1992).
IEC 60567, Guide for the sampling of gases and of oil from oilfilled electrical equipment and for the analysis of free and
dissolved gases 1992.
IEEE Guide for the interpretation of gases generated in oilimmersed transformers, (IEEE Std. C57.104, 1991).
CIGRÉ Task force 33.13.01 (Polluted insulators: a review of
current knowledge, June 2000).