Intelligent Agents Embedded with Computational Techniques for
Power Systems
ELENI E. MANGINA
Department of Computer Science
University College Dublin
Belfield, Dublin 4
IRELAND
http://www.cs.ucd.ie/staff/emangina/deafault.htm
Abstract: - Electrical utilities need to use their equipment closer to their design limits and require extending
their operating life through automatic condition monitoring systems. Data interpretation is of high importance
to infer the state of the equipment and is achieved by converting the data into appropriate information. This
paper describes the use of agent-based technology for classification of partial discharge signatures of Gas
Insulated Substations. The objective of this work is to take the next step and promote a decentralised, objectoriented, agent-based approach for data interpretation by making use of different artificial intelligence
techniques embedded within intelligent agents.
Key-Words: - Intelligent Agents, Computational Techniques, Gas Insulated Substations
1 Introduction
By definition condition monitoring is concerned
with detecting and distinguishing faults occurring in
plant that is being monitored [1], therefore the early
diagnosis and identification of faults has a number
of benefits (improvement in the plant economy,
reduction in operational costs, improving the level of
safety etc). In extra high voltage gas insulated
equipment, partial discharge (PD) occurs when a
defect enhances the local electric field. If the limit of
the insulation medium is exceeded, this can lead to
flashover and then breakdown. Various types of
defects, depending on the type of equipment, may
cause partial discharge activity. In Gas Insulated
Substations (GIS), PD can be initiated by free
metallic particles or by sharp protrusions located on
the electrodes, for example. For cost reasons, it is a
necessity to avoid breakdown. Various techniques
can be used to detect impending failures and monitor
the problems on-line.
Continuous monitoring systems based on the
principle of detecting the UHF signals emitted by
partial discharges (PD) in SF6, originally developed
at Strathclyde University [2,3], are now installed at
many substations world-wide and the technique is
established as the most sensitive available for
avoiding breakdowns and outages at critical nodes of
the power transmission network [4,5].
This work introduces the application of agentbased technology in advanced condition monitoring
for data interpretation of a large volume of data from
a Gas Insulated Substation. Interpretation of the
parameters is complex but essential to assess
possible performance deficiencies. By measuring
these parameters on-line, the data can be gathered in
a form that is ideal for the application of an
intelligent agent-based condition monitoring system
which will select the most appropriate interpretation
technique under varying operational conditions.
Within this paper, section 2 discusses the
problem domain of Gas Insulated Substations. The
analysis of recorded signals can be simplified and
automated through the application of classification
tools and the use of intelligent agents which apply
Artificial Neural Networks, K-means clustering and
C5.0 rule induction method is described in section 3.
Finally, section 4 outlines how the latter software
system can enhance the capability of the partial
discharge detection system by adding new intelligent
tools specialising in the recognition of partial
discharge sources.
2 Problem Domain
2.1
Gas Insulated Substations (GIS)
Gas Insulated Substations (GIS) are used in many
power transmissions networks for switching and as
transformation units for the control and management
of electrical power. A GIS substation might be very
large and complex in its design, as it is made of
many chambers connected together through
supporting barriers, corners and circuit breakers. The
GIS chamber is a large coaxial transmission line
with a central part consisting of a high voltage
busbar. The enclosure is earthed and contains the
pressurised SF6 gas. The barriers are made of
insulating epoxy resin; they maintain the positioning
of the busbar, centred in the chamber, and act as gas
tight seals. The chamber is approximately 300
millimetres in radius. The busbar diameter is about
100 millimetres. Due to the proximity of electrodes,
stresses within the chamber are very high and failure
might occur due to small defects. For detection
purposes the GIS has been equipped with sensors
affixed to the chambers. A portable PD detection
unit is connected to the coupler and displays
detected signals as shown in Fig. 1.
GIS chamber
Busbar
Coupler
4bar(g) SF6
Busbar
Supporting barrier
PD monitoring
system
Earthed
enclosure
Fig. 1: GIS chamber and PD monitoring system
According to the method based on UHF
measurement,
which
was
developed
and
implemented in the high voltage laboratory at the
University of Strathclyde, the GIS chamber acts as a
resonating cavity for a large range of VHF and UHF
electromagnetic EM waves. When small defects
emit electric pulses, EM modes are excited into the
wave-guide during a long period of time, with a low
loss factor. An appropriate coupler detects the
signal, which is then sent to the diagnostic
monitoring system. Interpretation of the detected
signals can lead to the evaluation of the cause of the
PD, which currently is achieved based on expertise,
and this introduces the need to look for methods to
automate the process.
2.2
Partial Discharge (PD)
Partial discharge (PD) is the electric phenomenon
where small voltage and current pulses are generated
by fast electrons and ions in electrical insulated
systems. In extra high voltage gas insulated
equipment, PD occurs when a defect (i.e. small
protrusion on the inner conductor or a free metallic
particle) enhances the local electric field. The
electrical and chemical activity associated with the
presence of such defects may lead to significant
degradation of the insulation and sometimes to
complete breakdown [6]. Various types of defects,
depending on the type of equipment, may cause PD
activity. In Gas Insulated Substations (GIS), PD can
be initiated by free metallic particles or by sharp
protrusions located on the electrodes, for example.
There are various types of defects that can
cause Partial discharge. Commonly found defects
fall into the six main categories quoted below:
 Free particles: when detached metallic particles
are liable to the AC voltage cycles, they hop at
the bottom of the chamber and emit very fast
current pulses.
 Busbar protrusion: sharp needles on the high
voltage electrode cause partial discharge with a
corona effect.
 Chamber protrusion: same as above but the
needle is on the enclosure.
 Floating electrode: this is particular to situations
where one of the electrodes has one part which
is not directly connected to the main body.
Sparks cross the gap between the two
components.
 Surface contamination on insulating barrier:
metallic particles that are glued on to the surface
of the spacer may cause surface discharge.
 Cavities in insulating barrier: internal voids
trapped in the insulating material can initiate
partial discharge.
Not only do the standard defects need to be
monitored, but also external sources that can be
detected by the actual system like communications
noise, radar signals and motor noise and any other
external source whose signal can be detected.
The PD monitoring system used displays the
detected signal in three dimensions: the phasecycles-amplitude display as shown in Fig. 2 for two
different defects. The phase axis divides the AC
cycle into equal phase windows, the cycle axis
indicates the cycle number of the AC wave and the
third axis is the actual amplitude of detected PD
pulses. Signal patterns vary with respect to the type
of source that created the discharge. Through
experience, typical patterns are easily recognisable.
The objectives of this work are to investigate
several methods of partial discharge classification
embedded within intelligent agents and study the
feasibility of integrating them into an agent-based
condition monitoring system. The goal is to extract
the meaningful information from the database of the
PD signals supplied by the PD monitoring system, in
order to evaluate the cause of the PD. The resulting
classification program will be derived from
redundant analyses based on computational
intelligence techniques and their combination will
guarantee a high level of confidence in the outcome.
Interpretation of the parameters is complex but
essential to assess possible performance deficiencies.
By measuring these parameters on-line, the data can
be gathered in a form that is ideal for the application
of an intelligent condition monitoring system, which
will select the most appropriate interpretation
technique under varying operational conditions. The
experts’ knowledge of interpreting the detected
signal’s 3D display can be captured and embedded
within a software system. The goal is to extract the
meaningful information from the database of the PD
signals supplied by the PD monitoring system, in
order to evaluate the cause of the PD. The resulting
classification program is based on computational
intelligence techniques and their combination. Next
section describes the application of various
classification techniques through a multi-agent
system in advanced condition monitoring for data
interpretation of a large volume of data from a Gas
Insulated Substation. This multi-agent software
system will be the basis for an integrated approach
of condition monitoring and plant lifetime modelling
of GIS. The final software system will process the
results of the data interpretation from different
groups of intelligent agents, diagnose any faults
occurring and assess the state of the GIS.
3 Multi-Agent System (MAS) for GIS
Fig. 2: Typical 3D displays for (a) free particle & (b)
protrusion
2.3 Current problems
The problems associated with monitoring GIS
for PD signal identification include:
 There are a number of different diagnostic
techniques (PD-detectors, chemical, optical,
acoustic, electrical), which have individual
advantages and disadvantages depending on the
type of defect, where one type of defect
remained totally or almost undetected. With
these conventional PD-detectors no reliable
results could be gained.
 The specialists must interpret the detected
signal’s 3D-display. The number of experts is
limited and it is infeasible to monitor the GIS on
a 24-hour basis.
Following the data preparation and the evaluation of
different classification techniques, there could be
identified cases where one individual method could
not classify the type of defect accurately, or could
identify only certain type of defect. Consequently, a
number of software entities have been developed
and form the hybrid solution for COMMAS-GIS
(COndition Monitoring Multi Agent System for
GIS) [7], the generic framework of which is given in
Fig. 3. The different software agents, which interact
in a dynamic way to support the required data
interpretation functions include:
 Kohonen-map agent: classifies data using
Kohonen maps
 Kmeans agent: classifies data using Kmeans
clustering algorithm
 C5.0_rule_induction agent: classifies data using
rule induction
 Case Based Reasoning (CBR) agent: reasons
based on past cases of the same type of defect
 Engineering Assistant Agent (EAA): informs the
user of the final result with details based on the
users’ profile
For the software development of this case study
there have been developed 5 different intelligent
software agents, while the number of the EAA
depends on the number of users.
MKRA_GIS (final data interpretation)
Kohonen_
map Agent
Kmeans
Agent
C5 rule
CBR
induction
Agent
Agent
Database
Fig. 3: MAS for monitoring PD of GIS
The research within this case study, although a
two-layer multi-agent system, denotes how the
theoretical framework of COMMAS for intelligent
data interpretation has been applied and
implemented in the identification of partial discharge
signal defects of Gas Insulated Substations (GIS).
Monitoring the PD signals and interpretation of the
parameters is complex but essential to assess
possible performance deficiencies. The coupler
within the GIS detects the signal, which is then sent
to the diagnostic monitoring system. The
“fingerprint” representation of the partial discharge
record, as described previously, is based upon
statistical analysis of the raw data (which within
future development can be achieved by ARA
agents). This reduces the amount of data to be
stored, and picks out the salient features within the
data. Within this application there were
approximately 600 different cases in the database
covering 7 distinct classes (types) of defect. The data
provided to the software system are in the form of
text files to be read and processed from the
intelligent agents:
<Casename, Feature 1, Feature 2… Feature 30 >
COMMAS-GIS will identify new cases based
on the most appropriate classification technique by
calling the different classification agents. Currently,
this is a two-layer architecture as opposed to the
COMMAS general three-tier approach. However,
the system will conform to the three-layer
architecture once the implementation of COMMAS
for GIS is complete, where other parameters will be
monitored in a similar way to PD signals. The fully
developed software system will evaluate the state of
the GIS, by collecting the results of all groups of
agents.
COMMAS-GIS has been implemented using
the ZEUS Agent Building Toolkit [8], where each
agent individually interprets and classifies the data
using its embedded technique, and communicates its
results using KQML messages. The training of each
algorithm has been implemented off-line, and the
accuracy of each method has been evaluated from
the agents, which call the external programs
responsible for testing. For each unidentified new
case, the agents execute each method and the final
result is the outcome of their combined
interpretation (based on the “majority voting
system”) through the MKRA_GIS agent.
Within each type of classification agents
(Kohonen_map, K-means, C5.0_rule_induction)
each clustering algorithm has been implemented to
classify the data based on the classification role
model. Although each classification agent is using a
different method (by calling different external
programs), they all belong to the same role model,
because the database has to be accessed and after
(off line) training, the accuracy is calculated. For the
identification of each case the results are sent to the
MKRA_GIS to be processed and the EAA informs
the user of the procedure in detail.
Fig. 4, 5 and 6 show the general execution of
each classification agent (Kohonen_map, K-means
and C5.0 rule induction respectively). Each type of
agent embodies the final vector of weights or rules
from the training executed off line. The testing and
the accuracy evaluation are accomplished on line
from each classification agent.
MKRA_GIS Agent (final data interpretation)
Send the final result of the identified new case
Kohonen_map
Agent
New
data
fingerpt
int
LVQ_PAK
(Supervised
learning,
training off
line)
SOM_PAK
(Unsupervis
ed learning,
training off
line)
Fingerprint
Database
TESTING, Final vector with neurons’
weights, Accuracy evaluation
Fig. 4: Kohonen_map Agent
MKRA_GIS Agent (final data interpretation)
Send the final result of the identified new case
MKRA_GIS Agent (final data interpretation)
Clementine v5.2
Kmeans Agent
Data mining software
system using K-means
clustering algorithm
5
New
Case
1
Fingerprint
Database
CBR Agent
2
Fingerprint
New
data
fingerpt
int
TESTING, Final vector with clusters’
weights, Accuracy evaluation
Fig. 5: K-means Agent
3
Database
3D Display
Database
Send the final result for the
new identified case
4
1. Receive a new case
2. Find the most similar cases from the
fingerprints case memory
3. Given the most similar cases,
retrieve their representative 3D
displays
4. Get feedback from the user
5. Send result to the MKRA, which will
form the final decision in
association with the other
techniques
6.Store the case with its classification,
confidence factor and 3D display
in the case memory
MKRA_GIS Agent (final data interpretation)
Send the final result of the identified new case
C5.0 Rule
Induction
Agent
APPLY_RULES
ON_LINE
New
data
fingerp
tint
Clementine v5.2
Data mining
software system
using C5.0 rule
induction
algorithm
Fingerprint
Database
Final rules for data
classification
Fig. 6: C5.0 Rule Induction Agent
During discussions with the experts it was
identified that there are certain characteristics of
each type of defect that could be seen from the 3D
display provided by the existing monitoring system.
These allowed the expert to come up with a
conclusion on which type of defect a case belonged
to. For example, certain defects tend to appear at
certain times, or phases. The expert would therefore
look at parameters like time and phase dependency.
Any symmetry that existed within the pattern on
both the negative and positive cycle would provide
information about the physical reality of the defect.
To emulate the experts’ reasoning, representative
cases of each defect could be identified, which
would then be provided to the user through the Case
Based Reasoning (CBR) software agent within the
COMMAS-GIS.
Along with the fingerprints for each case, the
3D display from the raw data is stored to be used by
the CBR agent, to display it to the user.
Fig. 7: CBR Agent within COMMAS-GIS
Based on the given images (as shown in Fig.8) the
user will select which one is the most similar and
will give feedback to the CBR agent along with the
confidence factor representing the user’s belief of
the new case being of a certain type of defect. The
result will be sent to the MKRA_GIS and the new
case will be stored to the case memory of the agent
and will be used for testing another new case in the
future. The impact of the CBR agent to the overall
multi-agent system is of high importance, especially
for cases where the software system cannot identify
and there is the need for the experts’ input. The
feedback from the experts is then stored in the case
memory as new cases and the knowledge can be
reused and the accuracy of the system will be
increased over time
Fig. 8: CBR display for user’s feedback
4 Conclusion
This paper has presented the analysis undertaken
upon GIS Partial Discharge monitoring data using
clustering and classification techniques. The
Kohonen map can be used successfully to classify
most of the data classes by assigning a class
identifier to each neuron in the map. The K-means
clustering algorithm had a very good performance as
it could accurately classify the input data according
to which cluster the data is nearest. The C5.0
performance is comparable to that of the Kohonen
map, where again certain classes could not be
differentiated from the other classes and it provided
rules, for future rule-based intelligent system
implementation. Although most of the classes could
be identified using the previous techniques,
problems were encountered due to an uneven
distribution of the data between the classes.
Provided more data the different techniques will be
used and evaluated again.
The next stage of implementation for COMMASGIS is to embed knowledge elicited from experts
who manually analyse the PD signals and to
integrate it with knowledge based systems to
emulate the experts reasoning. This will allow tacit
expertise gained over many years to augment the
existing classification techniques. The results from
the initial discussion with problem domain experts
lead to the following conclusions:
 There is phase dependency associated with most of
the defects. Symmetry in the patterns on both
negative and positive cycle gives information
about the physical reality of the defect (e.g.
floating electrodes cause discharges during the first
and third quarter).
 There are specific differences between some of the
defects (e.g. protrusion on busbar has the exact
reverse pattern of that of a protrusion on
enclosure).
 There are similarities between defects (e.g. floating
electrode has quite a small signal because it is like
a particle discharging).
 Digital interference (noise) can be easily identified
due to the “blanket effect”.
 There might be the case where two or more defects
occur, which makes the identification more
complex.
The agent-based approach offers a flexible condition
monitoring architecture, which can be applied to any
plant item. The distribution of the intelligence
allows for scalability and ease of integration of new
intelligent reasoning modules. Additionally, it
allows the reasoning to be performed across a
number of processors at a number of locations.
Although currently only fingerprints of the PD
signal data can be analysed the software system can
be extended to monitor other parameters in
association with other techniques of intelligent
reasoning. For example, once all the important
parameters are identified and different models of the
GIS specified, there can be included intelligent
agents to embed model based reasoning, or in case
of optimisation problems to use genetic algorithms.
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