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Knowledge-based diagnosis of partial discharges in power transformers
Article in IEEE Transactions on Dielectrics and Electrical Insulation · March 2008
DOI: 10.1109/T-DEI.2008.4446759 · Source: IEEE Xplore
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IEEE Transactions on Dielectrics and Electrical Insulation
Vol. 15, No. 1; February 2008
259
Knowledge-Based Diagnosis of Partial Discharges
in Power Transformers
S. M. Strachan, S. Rudd, S. D. J. McArthur, M. D. Judd
University of Strathclyde
Institute for Energy and Environment
Glasgow, UK
and S. Meijer and E. Gulski
Delft University of Technology
HV Components and Power Systems
Delft, The Netherlands
ABSTRACT
The abstraction of meaningful diagnostic information from raw condition monitoring
data in domains where diagnostic expertise and knowledge is limited and constantly
evolving presents a significant research challenge. Expert diagnosis and location of
partial discharges in High Voltage electrical plant is one such domain. This paper
describes the functionality of a knowledge-based decision support system capable of
providing engineers with a comprehensive diagnosis of the defects responsible for
partial discharge activity detected in oil-filled power transformers. Plant data captured
from partial discharge (PD) sensors can be processed to generate phase-resolved
partial discharge (PRPD) patterns. This paper proposes a means of abstracting the
salient features characterizing the observed PRPD patterns. Captured knowledge
describing the visual interpretation of these patterns can be applied for defect diagnosis
and location. The knowledge-based PRPD pattern interpretation system can support
on-line plant condition assessment and defect diagnosis by presenting a comprehensive
diagnosis of PD activity detected and classification of the defect source. The paper also
discusses how the system justifies its diagnosis of the PD activity to offer the expert
greater confidence in the result, a feature generally absent in ‘black-box’ pattern
recognition techniques. The incremental approach exhibited by the system reflects that
of a PD expert’s visual interpretation of the PRPD pattern. The paper describes how
this functional system design has evolved from the approach taken by PD experts to the
visual interpretation of PRPD patterns.
Index Terms — Transformers, partial discharges, knowledge-based system,
classification, diagnosis, condition monitoring, UHF sensors.
1 INTRODUCTION
WITH a design life of around 20-35 years (increasing to
50 years in practice with appropriate maintenance) [1], large
power transformers at transmission voltages are widely
regarded as reliable items of electrical plant. The crucial role
performed by transformers in maintaining a secure and
continuous delivery of electricity to consumers, demands
their safe and reliable operation. While significantly more
reliable in comparison to many other network components, it
is the potential impact of a transformer malfunction that
drives the requirement for effective condition monitoring of
these commercially and technically valuable network assets.
Transformer failures may result in loss of supplies to large
Manuscript received on 21March 2007, in final form 10 August 2007.
populations of customers, while the threat of explosions and
fire causing irreparable damage to adjacent plant and injury
to personnel pose a risk to safety and the environment with
potentially serious legal and regulatory implications.
Therefore, while transformers may have a generally reliable
reputation in the operation of the network, the welfare of
these critical network assets remains at the forefront of
utilities’ asset management responsibilities and obligations.
Condition monitoring technology captures operational plant
data characterizing its ‘vital signs’, enabling subsequent
on/off-line assessment of plant condition and performance.
Numerous on-line and off-line condition monitoring
technologies operating on a variety of observables are
available for the detection of incipient failures within
electrical power transformers [2].
1070-9878/08/$25.00 © 2008 IEEE
260
S. M. Strachan et al.: Knowledge-Based Diagnosis of Partial Discharges in Power Transformers
However, the novel nature of some condition monitoring
and sensor technologies means that plant experts can find
themselves presented with unfamiliar data sets with the
potential to provide fresh insights into previously
uncharacterized areas of plant behavior and associated
phenomena. As this knowledge grows and evolves, a
mechanism is required to harness, store and leverage the
knowledge and understanding required to map these
condition monitoring data sets to distinct observations in the
plant’s behavior and its failure mechanisms. Partial discharge
is one such phenomenon that is the subject of ongoing
research focusing on its behavior, cause and effects, and
methods of detection and diagnosis [3],[4].
Partial discharge (PD) activity is a prominent symptom of
the service ageing and degradation of transformer insulation.
Historically, monitoring of this phenomenon has been limited
and as such a lot remains unknown of its causes, breakdown
mechanisms, and exactly what influences its behavior. The
development of an extensible knowledge based system will
allow the incorporation of new knowledge of previously
unknown PD behavior and new defect types as it is learned
(either through empirical analysis or practical experience).
What is known, is that the dielectric properties of plant
insulation may be severely impaired if subjected to consistent
PD activity over prolonged periods of time (e.g. initiated by
transient over-voltage or incipient weaknesses in insulation,
etc.). This may ultimately lead to complete failure where PD
activity remains undetected and untreated. Insulation integrity
is therefore regarded as a critical aspect of a transformer’s
condition. As such, suitable safeguards are required to
monitor transformer plant for the presence of potentially
damaging PD activity.
Historically, research into partial discharges has focused
on the nature of these discharges occurring within solid and
gas insulation systems [5],[6]. Investigations into the partial
discharge phenomenon in liquid dielectrics (such as the oil
insulation of a transformer) are more difficult and as a
consequence are generally less well understood within the
PD research community as a whole. Limited knowledge of
the nature of partial discharges generated in the oil
insulation of power plant with complex geometry, such as
oil-filled power transformers, prompted some empirical
research within the University of Strathclyde [7]. Partial
discharge data captured using UHF sensor technology was
analyzed by University of Strathclyde researchers in an
attempt to understand the physical process underpinning
dielectric breakdown within large oil-filled power
transformers and the PD behavior arising from particular
defect geometries [7][8]. This research sought to enhance
the general PD community’s “knowledge-base” of PD
behavior associated with this transformer plant and oil
insulating medium.
This paper proposes that as our knowledge base evolves
and matures, and the linkage between the cause and effect
of the PD phenomenon becomes more established and
better understood, this domain knowledge can be retained
and embedded within a knowledge-based system
facilitating future PD defect diagnosis and location. A
knowledge-based decision support system is proposed as a
means of providing a practical diagnostic explanation of
PD behavior and defect geometry to end-users (e.g.
maintenance personnel, asset managers, PD experts, plant
manufacturers).
The knowledge-based system proposed in this paper
mimics a partial discharge expert’s approach to the
interpretation of phase-resolved partial discharge (PRPD)
patterns for the purpose of diagnosing the nature, severity
and cause of detected PD activity. The system uses
statistical parameters to characterize the key feature (or
descriptors) of the PRPD pattern, which a PD expert would
typically look for when performing their diagnosis. These
descriptors are then used to explain in physical terms the
behavior associated with the observed partial discharge
activity. Understanding the PD behavior enables distinct
aspects of the physical geometry and properties of the
responsible defect source to be deduced and subsequently
matched to the diagnostic rules of a knowledge base.
Ultimately, knowledge of the defect site geometry can be
used to classify and subsequently locate the PD defect
source.
This knowledge-based system contains knowledge
derived from multiple experts which readily maintainable
and generally applicable to any H.V. oil filled transformer.
Another benefit of the knowledge-based approach is the
provision of a meaningful diagnostic explanation
accompanying the system’s defect classification. This
increases the user’s confidence in the system output.
The paper describes the structured approach taken to the
knowledge capture required to populate the respective rulebases with the appropriate areas of domain knowledge.
Furthermore, the paper proposes that the modular system
design provides an extensible framework, which can be
readily augmented with fresh knowledge (e.g. concerning
new types of PD, different plant types, different insulating
mediums) as it evolves and matures through continued
research and on-site experience. In addition, this system will
assist experts in the recognition and understanding of new PD
sources or different manifestations of existing ones.
2 PARTIAL DISCHARGE ACTIVITY IN OILFILLED POWER TRANSFORMERS
A partial discharge occurs in a high voltage (HV)
transformer when the electric field strength exceeds the
dielectric breakdown strength of the insulating medium in a
localized area. This results in an electrical discharge that
only partially bridges the insulation between conductors.
High electron energies can break molecular bonds in
adjacent solid or liquid dielectrics resulting in changes to
the chemical properties of the insulating material and its
subsequent ablation.
IEEE Transactions on Dielectrics and Electrical Insulation
Vol. 15, No. 1; February 2008
Partial discharge activity may be categorized by the
defect source responsible and its locale [4]. The range of
defect classes for which the nature of the discharge
behaviour was observed in the programme of research
reported in [7] include:
• Bad Contact – microdischarges between conducting
surfaces separated by a thin layer of oil, e.g. between
the threads of loose nuts and bolts.
• Floating Component – small fixed conducting particle
at floating potential that experiences a localised PD
that does not bridge the gap to the system conductors.
• Suspended Particle – small, free-moving conducting
objects or debris within the insulating oil that can
discharge to the system conductors.
• Rolling Particle – conducting particles lying on a
conductive surface until influenced by the electric field
causing them to roll or bounce around.
• Protrusion – fixed, sharp metallic protrusions on HV
conductors.
• Surface Discharge – at pressboard surface due to
moisture ingress or material damage.
• Floating Electrodes – large components such as stress
shields that have become detached from the chamber,
acquiring a floating potential that results in capacitive
sparking.
These defect classes represent those targeted by the
knowledge-based system described in this paper.
3 UHF DETECTION OF PARTIAL
DISCHARGES AND PHASE-RESOLVED
PATTERN REPRESENTATION
Figure 1. Phase-resolved partial discharge pattern.
Recent research has demonstrated the efficacy of
employing ultra-high frequency (UHF) detection of PD in
the monitoring of transformers [9],[10]. When a PD occurs
inside a transformer, an electromagnetic wave is radiated
that resonates in the tank and can be detected using UHF
sensors. Signals from the sensors are typically filtered,
amplified and converted to a simple pulse by a detector
261
circuit before being digitized. A phase signal derived from
the power frequency waveform provides additional
reference information for the digitized PD data. Each PD
pulse recorded is associated with a particular time and
“point-on-wave”. The amplitude of the displayed pulses is
proportional to the intensity of the UHF signal. A typical
display is shown in Figure 1, in which PD pulses occur in
bursts on the rising and falling portions of the high voltage
sinusoid of the power frequency waveform.
Three main measurements are used to quantitatively
describe the degradation of insulation resulting from partial
discharge activity, these are:
• Discharge pulse magnitude trend - indicative of the
rate of dielectric deterioration.
• Discharge pulse repetition rate/number of pulses –
indicative of defect severity, where periods of
sustained activity are considered more damaging
than periods of intermittent activity.
• Discharge pulse phase angle - indicative of the
defect’s ignition and extinction conditions in relation
to the electric field.
These quantities are inherent in the PRPD pattern and
collectively provide a suitable means of characterizing
partial discharge activity for diagnostic interpretation and
defect classification [11].
A PRPD pattern may be constructed using data derived
from many different types of sensor and measurement
systems. Therefore, using the PRPD pattern as the basis of
the system diagnosis means that the system could in
principle support different monitoring technologies.
4 KNOWLEDGE-BASED SUPPORT
FOR PARTIAL DISCHARGE DEFECT
DIAGNOSIS IN TRANSFORMERS
Historically, PD in transformers has not been monitored
in service. Utilities have relied principally on laboratory
based Dissolved Gas Analysis (DGA) to establish the
extent and nature of the PD occurring within plant. While
this monitoring technology has provided significant benefit
in the detection of PDs within transformer plant, it has its
limitations in diagnosing the nature, cause, severity and
location of detected PD activity. The lack of understanding
of the PD process associated with dielectric breakdown, has
historically prompted the need for a less knowledge
intensive and more data driven approach to PD defect
classification [12]. On the other hand, the power
transformer in general is a complex system due to the
amount of different dielectrics in- and outside the
transformer, all of them having their own failure
mechanisms. In order to detect these failures in advance,
several types of diagnostics are available and should be
applied during the service life of a power transformer. In
particular, as shown in [3] performing PD diagnosis may
contribute to detect 21% of problems related to transformer
vessel and to 14% of problems related to HV bushings.
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S. M. Strachan et al.: Knowledge-Based Diagnosis of Partial Discharges in Power Transformers
However as on-line monitoring of in-service plant has
become more practical and viable, PD experts are presented
with an opportunity to gain new insights into the physics of
the partial discharge process, and subsequently improve
their understanding of this phenomenon.
While previous research has demonstrated the application
of machine learning pattern recognition techniques as a
useful means of defect classification [13]-[15], some
constraints are associated with these approaches, including:
the trained classifiers remain specific to the transformer
from which the initial training data set is derived; historical
data sets are required to train the classifier; a classifier will
only recognize defects on which it has been trained and as
such must be retrained (or train new networks) to recognize
new defects. More significantly however, the nature of
these ‘black-box’ algorithms is such that no practical
explanation may be provided as justification for the output
classification presented by the system. Machine learning
techniques remain a useful means of defect classification,
despite their limited explainability. As such, this paper does
not discount the data driven approach, but instead
recognizes the potential role of a knowledge-based system
in providing decision support to plant experts. Providing a
detailed diagnosis of the detected PD activity reassuringly
offers the expert an explanation of the health and condition
of transformer plant, and provides a degree of confidence in
the system output, generally absent in use of ‘black-box’
pattern recognition techniques.
PD diagnostic knowledge can be captured from empirical
and practical engineering experience, which relates measured
partial discharge quantities to observed defects and the
resulting breakdown processes, taking into account external
influencing conditions such as plant geometry, propagation
effects, attenuation, interference, etc. This knowledge can be
implemented as heuristic rules to support engineers in the
diagnosis of partial discharge activity when assessing the
condition of transformer insulation. A CIGRE Task Force
(15.11/33.03.02) involving some key contributors within the
PD community has recognized the potential for knowledgebased decision support in this area [16].
The expert interpretation of the PRPD patterns suggests
an emerging area of expertise which if suitably harnessed
by means of a knowledge (rule) based expert system, offers
a means of automating PD diagnosis and defect
classification.
5 EXPERT INTERPRETATION OF
PHASE-RESOLVED PARTIAL
DISCHARGE PATTERNS
This paper proposes a knowledge-based approach to the
interpretation of phase-resolved partial discharge patterns
that aims to mimic an expert’s visual interpretation of these
patterns (Figure 2).
By simply observing captured PRPD patterns, experts
are able to intuitively abstract features in the PRPD patterns
(i.e. PRPD descriptors) and associate these features with
key characteristics of the PD behavior and hence the
responsible defect geometry. Knowledge of the physical
attributes of the defect can subsequently inform the
classification and location of the PD defect source.
The Unified Modeling Language (UML) Activity
Diagram [17] of Figure 2 was defined through knowledge
elicitation meetings conducted with experts specializing in
the experimentation and understanding of the partial
discharge phenomenon and specifically the interpretation of
their causes and behavior from PRPD patterns derived from
UHF condition monitoring data. A UML Activity Diagram
can be used for process modeling in order to present
knowledge engineers and domain experts with a clear
understanding of the interaction between process tasks in
terms of the input/output objects handled and delivered by
the task, the task dependencies indicating the sequence of
execution and the flow of information between tasks. This
graphical representation enables fast and accurate
validation of the modeled process by domain experts.
Inputs
Tasks
Figure 2. Process of visual interpretation of PRPD patterns for PD defect
diagnosis.
The Activity Diagram of Figure 2 in turn defined the
functional framework of the knowledge-based expert
system of Figure 3 and the structured approach taken to the
acquisition of domain knowledge suitable for system
implementation.
IEEE Transactions on Dielectrics and Electrical Insulation
Vol. 15, No. 1; February 2008
263
The functional flow of the expert’s PRPD pattern
interpretation process effectively mapped out the
agenda for the knowledge acquisition exercise. When
eliciting the diagnostic rules for the population of the
appropriate rule-bases, aligned to each stage of the
interpretation process, it was more intuitive for the
expert to start with a PRPD pattern of a known defect
class and/or location. It is then easier for the expert to
deconstruct the defect source diagnosis into its physical
characteristics first, before considering how these
characteristics influence the resultant partial discharge
activity and ultimately how this is evident in the
characteristics of the PRPD pattern.
6 KNOWLEDGE-BASED EXPERT SYSTEM
FOR PRPD PATTERN INTERPRETATION
The modular design of the system illustrated in Figure
3 reflects the expert’s incremental approach to the visual
interpretation of PRPD patterns. Each stage of the
interpretation process requires and utilizes knowledge
relating to how the PRPD pattern, the PD activity and the
responsible defect source can be recognized and
characterized. This knowledge can be used to build up a
practical explanation of the system’s PD defect
classification. The diagnostic rules elicited from PD
experts, which enable the interpretation of the PRPD
patterns, form a number of rule-bases. Each rule-base
represents knowledge associated with a specific stage of
the interpretation process illustrated in Figure 2. The
objective of the rules contained in each rule base can be
summarized as follows:
•
Rule-base RB No.1 consists of rules that extract
deduced and statistical features from the PRPD
patterns, forming the basis for further interpretation.
•
Rule-base RB No.2 uses knowledge of how the derived
statistical fingerprint characterizing the PRPD pattern
translates into features (i.e. PRPD descriptors)
describing the observed characteristics of the PRPD
pattern itself in terminology familiar to human experts.
•
Rule-base RB No.3 uses knowledge of how these
PRPD descriptors translate into physical aspects of the
PD behavior.
•
Rule-base RB No.4 uses knowledge of how the
identified PD behavior translates into the physical
characteristics of the responsible defect source (i.e.
defect geometry).
•
Rule-base RB No.5 uses knowledge of how the defect
geometry can be used to identify suspected defect
classes.
•
Rule-base RB No.6 uses knowledge of the defect class
to propose plausible locations for the defect site within
a localized area of the plant item.
Figure 3. Functional description of knowledge-based PRPD pattern
interpretation system.
The nature of the rules in each rule-base is detailed in
Section 8.
This knowledge-based approach provides the user with a
justification for the system’s defect classification. In
addition, the level of detail in the explanation can be
customized to suit the user’s level of expertise and
experience. For example, maintenance engineers may be
more concerned with identifying the type of PD defect
source and its location, while PD experts or plant
manufacturers may have a greater interest in the breakdown
mechanisms associated with the PD activity underpinning
this high-level diagnosis, and the potentially degrading
effect this has on the plant insulation in the long term under
different operating conditions (i.e. the PD behavior and
defect geometry).
The inference engine of an expert system is encoded in
software and is responsible for processing input data to
decide what should be done next, i.e. specifically which
rules in the system rule-base to invoke or ‘fire’. If the rule
conditions are satisfied and it is fired then the rule
concludes that some predefined action to be performed (e.g.
report a diagnosis to the screen, invoke another rule, etc.).
The inference mechanism of the system described in this
paper underpins the incremental approach to the
knowledge-based diagnosis through its application of a
forward-chaining reasoning mechanism across the rulebases. The selection and execution of PRPD pattern
interpretation rules from the respective rule-bases is
controlled by this inference mechanism, using existing
diagnostic facts (determined by the conclusions of
previously fired rules, asserted in the working memory) to
fire relevant rules in the same or subsequent rule-bases
264
S. M. Strachan et al.: Knowledge-Based Diagnosis of Partial Discharges in Power Transformers
[18]. Once fired, further rule conclusions are asserted,
effectively augmenting the system’s diagnosis while
prompting subsequent rule firing in the appropriate rulebase as the system moves towards its diagnostic
conclusion.
7 CAPTURING DIAGNOSTIC
KNOWLEDGE FOR RULE-BASED
IMPLEMENTATION
Knowledge may be considered to be either explicit or
tacit. Explicit knowledge is generally that which is well
defined, represented and understood, while tacit knowledge
is generally experiential knowledge often associated with
tasks performed by a specialist which have effectively
become ‘second-nature’, and as such can be difficult to
articulate. It is therefore tacit knowledge that often sets
experts apart from non-experts and subsequently
distinguishes expert systems from traditional information
systems. Within the context of this paper the term
‘knowledge’ relates to the expertise exhibited by domain
experts in diagnosing and classifying PD defect sources
from observations made on PRPD patterns. This tacit
knowledge can be captured and utilized in an expert system
through a structured process of knowledge engineering.
Knowledge engineering is a structured method of
transferring and transforming the tacit expert knowledge
relating to a specific domain, from the mind of an expert
into a computer-tractable form. The knowledge
engineering process involves the acquisition of expert
knowledge associated with a particular domain and task
using a structured interview process focused on preselected case studies (i.e. PRPD patterns); representation
of expert knowledge in an intelligible format for
validation and utilization (i.e. knowledge transcripts and
causal models); validation of expert knowledge before it
can be confidently utilized (e.g. within a knowledge
(rule)-based system).
As mentioned, the output from each rule-base in this
approach to PRPD pattern interpretation relies upon the
output asserted by its antecedent rule-base. A forward
chaining inference mechanism underpins the interaction
between these rule-bases. This section examines the
execution of the rules within the knowledge-based system
in the construction of a PD diagnosis for a particular case
study.
8 RULE-BASE IMPLEMENTATION –
CASE STUDY
8.1 Rule-base No.1 – Abstraction of
Statistical Fingerprint from PRPD
Pattern
Rule-base No.1 of Figure 3 deduces distributions from
the input PRPD pattern (Figure 4), and subsequently
derives statistical quantities from these distributions.
Figure 4. Input PRPD pattern used in the case study.
8.1.1 DEDUCED DISTRIBUTIONS
The PRPD pattern provides an insight into the nature of
the PD process, its intensity and the geometry of the
responsible defect [11]. Distributions deduced from the
PRPD pattern comprise both time varying and phase
varying features characterizing sustained PD activity over a
number of voltage cycles. Consequently, the following four
distributions deduced from a PRPD pattern representing a
one second snapshot of PD activity (Figure 5) characterize
the changing partial discharge activity over a number of
consecutive cycles (in this case 50 voltage cycles):
• The Discharge Pulse Count distribution Hn(φ).
• The Discharge Maximum Pulse Height distribution
Hqm(φ).
• The Discharge Energy Magnitude distribution Hqs(φ).
• The Discharge Mean Pulse Height distribution Hqn(φ).
Figure 5. Abstraction of deduced discharge mean pulse height distribution
from phase-resolved partial discharge pattern.
8.1.2
STATISTICAL QUANTITIES
Separate H distributions are required to characterize the
PD activity associated with each half of the voltage cycle
(i.e. H+ and H-). The H+ and H- distributions can be
characterized and described in terms of their mean,
variance, skew and kurtosis statistical features. In addition,
comparative statistical features characterize the difference
in the mean discharge magnitude, inception phase angle
and cross-correlation (i.e. shape) between the distribution’s
positive and negative half cycles. Rule-base (RB) No.1 of
Figure 3 calculates and combines these statistical
parameters providing a ‘statistical fingerprint’ of each H
distribution and consequently the PRPD pattern.
8.2 Rule-base No.2 – Abstraction of PRPD
Pattern Descriptors
Rule-base No.2 of Figure 3 employs rules operating on
specific combinations of the statistical parameters derived
IEEE Transactions on Dielectrics and Electrical Insulation
Vol. 15, No. 1; February 2008
via rules in rule-base No.1 in order to characterize the
salient features of the observed PRPD pattern shape (i.e.
PRPD pattern descriptors) (Figure 6).
For example, these quantities can be used to determine
whether: the PRPD pattern representing PD pulse activity,
is symmetrical or asymmetrical; pulses occur regularly
across consecutive voltage cycles or appear intermittently;
bursts of high-energy partial discharges appear; partial
discharge pulses occur at the voltage zeros or peaks, etc.
While still relatively abstract, the interpretation of the
PRPD pattern is progressed, providing the basis for a more
comprehensive and intuitive description of the PD
behavior.
Causal
Knowledge
Models
Pulse magnitude at
0 o, 180o, 360o
greater than zero
Skewness of
distribution in +ve
and –ve half cycles
>> 0
Knowledge
Rules
pulse magnitude at 0 o, 180o, 360o
greater than zero
THEN pulses located on zero crossings
IF positive skewness
THEN Asymmetrical PD activity:
skewing of larger PD pulse
amplitudes to earlier phase
position
AND
Shape
symmetry
indicates
Cross-correlation
factor close to one
indicates
indicates
Pulses located
on zero
crossings
indicates
Knowledge
Rules
Causal
Knowledge
Model derived
from knowledge
extract No.2 & No.3
Scale symmetry i.e.
same amount of PD
activity associated with
+ve and –ve half cycles
Scale
symmetry
Causal
Knowledge
Models
Rule-base No.3 of Figure 3 interprets the PRPD pattern
descriptors to provide an insight into the physical nature of
the partial discharge activity generated by one or more
defects (i.e. PD behavior). Experimental studies of the PD
behavior of various defects in transformer oil led to the
capture of the following extracts of knowledge from the
compiled transcripts.
Asymmetrical PD
activity: skewing of
larger PD pulse
amplitudes to
earlier phase
position
indicates
indicates
8.3 Rule-base No.3 – Explanation of PD
Behavior
Pulses located
on zero
crossings
indicates
IF
Ratio of mean
amplitude in +ve
and –ve half cycles
close to one
High correlation
between activity
associated with +ve
and –ve half cycles
Knowledge
Rules
1.
Shape symmetry: i.e.
same distribution shape
of PD activity
associated with +ve
and –ve half cycles
IF
scale symmetry
AND
shape symmetry
THEN high correlation between activity
associated with +ve and –ve half
Figure 6. Knowledge rules for the abstraction of PRPD pattern descriptors.
Dependent on
rate of change
of voltage
Issue of space
charge
IF
pulses located on zero
crossings
THEN PD dependent on rate of
change of voltage
AND
issue of space charge
Phase position
symmetry
indicates
Chopped sine
wave
indicates
Conditions are the
same for both
polarities
Low energy
discharge
IF
phase position symmetry
THEN conditions for partial discharge
are same for both polarities
IF
chopped sine wave
THEN low energy discharge
Figure 7. A knowledge interpretation rules explaining the PD activity.
2.
Knowledge
Rules
265
“The discharges would occur around the zero crossing
when they are more dependent on the rate of change of
voltage rather than absolute voltage. A classic example
to illustrate this is a discharge in a void in solid
insulation. Each discharge relieves the electrostatic
field across the void. There is a space charge effect in
that charge is trapped on the inner surfaces of the
void.”
“A truncated sinusoidal pattern implies low energy
discharges are occurring at a sufficient rate to form
the pattern envelope. If such a pattern is centered on
the zero crossings it suggests a void. A void is a
capacitive discharge governed by the combined
electric field of the system voltage and local space
charge. Void geometry can be symmetrical in shape.”
S. M. Strachan et al.: Knowledge-Based Diagnosis of Partial Discharges in Power Transformers
266
3.
4.
“When looking at the PRPD pattern three types of
symmetry can be identified; phase, shape and
magnitude. Phase position symmetry means that field
conditions for partial discharge are similar for both
polarities. Shape and magnitude symmetry suggests
that the defect is physically symmetrical.”
“A PD exhibiting characteristics of a void discharge
could not be located in bulk gaseous or liquid
insulation. Therefore the PD site can be narrowed
down to those regions where solid insulation is present
or gas bubbles might form in the oil. For example, in
the case of a transformer, a bushing would be a
possibility.”
Figure 7 illustrates how the knowledge interpretation
rules translate the PRPD pattern descriptors (output from
RBs 1, 2 and 3), into a diagnosis of the PD behaviour.
8.4 Rule-base No.4 – Diagnosis of Defect
Geometry and Characteristics
The physical characteristics of the defect source (i.e.
defect geometry) can be interpreted from the PD behaviour
(output from RB No.3) using the knowledge retained in
rule-base No.4 of Figure 3.
Figure 8 illustrates how knowledge interpretation rules
translate characteristics of the PD behaviour into
characteristics of the possible defect geometry.
Capacitive
discharge
mechanism
Causal
Knowledge
Model derived
from knowledge
extract No.2 &
No.3
Shape
symmetry
Magnitude
symmetry
Knowledge
Rules
defect site. Figure 8 illustrates how an understanding of
the linkage between the behavior of PD activity
characterizing a defect source and the characteristics of
the defect geometry of the defect make its classification
possible.
Figure 9 illustrates how RB No.5 uses knowledge of
the defect geometry (derived from RB No.4) to classify
the defect type associated with the input PRPD pattern. It
is evident that while on the whole PD’s arising from
different defects may behave differently, they can share
similar characteristics. In addition, more than one class of
defect may exist simultaneously, or a particular class of
defect may morph into another as a result of continued
thermal or mechanical stress, or sustained discharging.
Therefore, providing a diagnostic explanation of the pulse
activity observed from successive PRPD patterns
generated over a period of time supports the engineer in
determining the nature, extent and severity of PD activity
emanating from a defect source as it develops.
Capacitive
discharge
mechanism
Causal
Knowledge
Model derived
from knowledge
bulletin No.2
indicates
AND
Void
defect
Low energy
discharge
Dependent on
rate of change
of voltage
indicates
indicates
Geometrically
symmetrical
Defect site
geometrically
symmetrical
indicates
IF
PD dependent on rate of change of
voltage
THEN capacitive discharge mechanism
IF shape symmetry
THEN geometrically symmetrical
IF
magnitude symmetry
THEN geometrically symmetrical
Figure 8. Knowledge interpretation rules explaining the PD defect.
8.5 Rule-base No.5 – Classification of Partial
Discharge Defect
Specific defects exhibit certain PD behaviors that, while
influenced by the dielectric properties of the insulation and
external conditions affecting the sensor signals, (e.g.
propagation, attenuation, interference), are generally
symptomatic of the physical nature and geometry of the
Knowledge
Rules
IF
AND
AND
AND
capacitive discharge mechanism
geometrically symmetrical
low energy discharge
dependent on rate of change of
voltage
THEN void defect
Figure 9. Knowledge interpretation rules supporting the classification and
location of partial discharge defects.
8.6 Rule-base No.6 – Description of
Plausible Partial Discharge Defect
Source Location
Knowledge of the defect class alone will not provide an
indication of the precise location of the defect source.
However, considered in conjunction with other techniques
(e.g. time of flight) 0, which can be used to identify the
general locale of the defect, knowledge of the defect class
can be a useful means of homing in on potential defect sites
within the identified area of interest.
Figure 10 illustrates how RB No.6 uses knowledge of the
defect type classification (derived from RB No.5) to
propose possible defect sites.
IEEE Transactions on Dielectrics and Electrical Insulation
Causal
Knowledge
Model derived
from knowledge
extract No.4
Vol. 15, No. 1; February 2008
Void
Defect
indicates
Time of Flight
Output is top of A
phase LV winding
Knowledge
Rules
Possible location of
defect site is LV
bushing A Phase
IF
AND
Void Defect classification
Time of Flight location is top of A
phase LV winding
THEN Possible location of defect site is LV
bushing A Phase
Figure 10 A knowledge interpretation rules suggesting sites of PD activity
A summary of the diagnostic explanation output from
each of the system rule-bases, for the case study discussed,
is provided below:
ƒ PRPD Pattern Descriptors (RB No.2)
- Asymmetric PD activity
- Scale symmetry
- Shape symmetry
- High correlation between activity in positive and
negative half cycles.
ƒ PD behaviour (RB No.3)
- PD dependant on rate of change of voltage
- Space charge present
- Low energy discharges
ƒ Defect geometry (RB No.4)
- Capacitive discharge mechanism
- Site geometry symmetrical
ƒ Defect classification (RB No.5)
- Void defect
ƒ Defect location (RB No.6)
- LV bushing
9
CONCLUSION
This paper proposes a knowledge-based system as a
means of replicating the visual recognition and
interpretation process of PD experts when classifying the
defect sources using PRPD patterns. This requires an
understanding of the traits commonly associated with, and
relationship between, observed partial discharge activity
and responsible defect sources. This paper has proposed a
means of capturing this currently expanding knowledge
base and subsequently retaining relevant domain
knowledge within a knowledge-based expert system
designed to support plant experts, manufacturers and PD
experts in the classification, and continued understanding,
of the partial discharge phenomenon. It is envisaged that
this knowledge-based approach will offer the advantage of
supporting the classification of PD defects with a practical
diagnostic explanation of the detected PD activity, currently
absent in many of the data-driven and machine learning
approaches advocated in adjacent areas of research.
A more detailed diagnosis of the effects of PD activity
experienced by transformer plant can subsequently be used
267
to better inform plant maintenance and asset management
strategies. This supports more focused maintenance action
(condition-based maintenance), supplying maintenance and
plant engineers with instructive remedial advice.
The knowledge-based approach to the interpretation of
the PRPD patterns supports a modular system design. This
in turn provides a generic framework for the incorporation
of PD knowledge relating to different items of power
system plant. As such, an extensible system design enables
the knowledge-bases to be easily augmented with fresh
knowledge (e.g. different defect types, plant types,
insulation mediums) as knowledge and understanding of
the PD phenomenon improves through ongoing empirical
research and the growth of practical engineering
experience.
ACKNOWLEDGMENT
The UK authors would like to thank the EPSRC for
supporting this research through the Supergen V UK
Energy Infrastructure (AMPerES) grant.
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Scott Strachan received the B.Eng. (Hons.) and Ph.D.
degrees from the University of Strathclyde. He currently
holds the post of Research Fellow within the Institute of
Energy and Environment. His research interests include,
plant monitoring, asset management, data mining,
knowledge management & engineering and intelligent
systems applications in power engineering.
Susan Rudd received the B.Sc. (Hons.) from the
University of Strathclyde in 2004. She is a research
student in the Institute for Energy and Environment. Her
research interests include knowledge engineering,
intelligent system applications in power engineering and
condition monitoring.
Stephen McArthur (M’93-SM’07) received the B.Eng.
(Hons.) and Ph.D. degrees from the University of
Strathclyde. He is a Reader in the Institute for Energy
and Environment. His research interests include
intelligent system applications, condition monitoring and
multi-agent systems.
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Martin D. Judd (M’02-SM’04) graduated from the
University of Hull in 1985 with a B.Sc. (Hons.) degree in
electronic engineering. His employment experience
includes work at Marconi Electronic Devices Ltd and
EEV Ltd, both in Lincoln, England. Martin received a
Ph.D. from the University of Strathclyde in 1996 for his
research into the excitation of UHF signals by partial
discharges in gas-insulated switchgear. From 1999 to 2004 he was an
EPSRC Advanced Research Fellow. His fields of interest include high
frequency electromagnetics, fast transients and their measurement and
partial discharges. Dr Judd is a Senior Lecturer within the Electrical Plant
and Diagnostics group at the University of Strathclyde.
Sander Meijer received the M.Sc. degree in electrical
engineering in the field of electrical power systems in
1995. In 2001 he obtained the Ph.D. degree from the
Technical University of Delft. At present, he is assistant
professor in the department of high-voltage technology
and management. His main research topic is in the field
of high frequency measuring techniques for insulation
diagnosis of gas insulated systems, power transformers
and high-voltage cables and electromagnetic compatibility. He is active in
international working groups and is the secretary of Cigré WG D1.03 (gasinsulated systems).
Edward Gulski received the M.Sc. degree in
information technology in 1982 from Dresden
University of Technology in Germany. In 1991 he
received the Ph.D. degree from Delft University of
Technology in The Netherlands. In 2004 he received
the Doctor Habilitatus degree from Warsaw University
of Technology in Poland. At present, he is an associate
professor involved in education and research in the
field of insulation diagnosis of HV components and Asset Management.
He is member of the Executive Board of KSANDR organisation and
responsible for research in education. He is chairmen of Cigre Working
Group D1.17, ''HV asset condition assessment tools, data quality, and
expert systems'', member of Cigre Study Committee D1 ‘’Materials and
Emerging Technologies’’, convenor of Cigre Task Force D1.33.03 “Partial
Discharges Measurements’’, member of IEEE Working Group 400.3 and
member of the IEEE Insulating Conductors Committee.