Abnormality Detection for Gas Insulated Switchgear
using Self-Organizing Neural Networks
Hiromi OGI, Hideo TANAKA, Yoshiakira AKIM OTO
Tokyo Electric Power Company
Computer & Communication Research Center
1-4-10, Irifune, Chuo-ku, Tokyo 104 Japan
Phone: +81-3-3501-8111 ext.5046
Fax: +81-3-3297-1649
Email: [email protected]
Keywords: Neural networks, Diagnosis, GIS
Abstract: This paper presents an Artificial Neural Network
(ANN) approach to diagnostic methods for abnormality detection
for a Gas Insulated Switchgear (GIS). An outline of the current
technologies of power equipment diagnosis is initially presented
followed by the proposed application of the Self-Organizing
Neural Network (SONN) to abnormality diagnosis of GIS. Several
tentative experiments through laboratory simulations for a small
sized GIS are finally presented.
Introductjon
Fault detection is an important task for the reliable operation of a
power system. Recent survey of the diagnostic technologies and
power equipment reliability [I] has shown that the majority of
faults are caused by the maloperation of circuit breakers or
switchgears. Gas Insulated Switchgear or Gas Insulated Circuit
Breakers (GCBs) are being widely used to increase the reliability
of power system operation. In order to maintain the high reliability
of operation, it is necessary to detect in advance the abnormal
operation of the GIS before its propagation to a major fault.
Gas Insulated Switchgear or Gas Insulated Circuit Breakers are
originally designed as maintenance free equipment. The main
components are concealed in SF6 insulation gas and it is difficult
to examine the internal status of operation. This invisibility has
necessitated the development of the Predictive Maintenance
Technology (PMT), concerned mainly with the examination of the
internal status or abnormality of operation by the external
application of sensors. One of the major drawbacks of PMT is the
ineffectiveness of the currently available diagnostic algorithms in
obtaining precise mathematical models to simulate the physical
process of the internal abnormality.
Artificial Neural Networks (ANNs), that mimic the nervous
system, are finding wide applications as potential tools for the
solution of problems where conventional approaches either fail to
Yoshio IZUI
Mitsubishi Electric Corporation
Industrial Systems Laboratory
8-1-1, Tsukaguchi-Honmachi, Amagasaki, Hyogo 661 Japan
Phone: +81-6-497-7642
Fax: +81-6-497-7727
Email : [email protected]
arrive at acceptable solutions or are provide unsatisfactory
performance[2]. ANNs have been successfully applied to many
problems in power systems and have promising applications to
several other related areas[3-9]. The characteristics of ANNs such
as learning, self organization, adaptation and non-linear
classification provide important tasks of category formation and
classification that are required to accomplish the diagnostic
algorithms for PMT.
The objective of this paper is to study Kohonen's Self-Organizing
Neural Network (SONN)[ 10-11] for internal abnormality
detection for GIS using sensor signal attached outside the tank.
The neural network self-organizes its internal weights based on
the probability distribution of spectrum of sensor signal. The label
of abnormality category is assigned to each neuron after self
organization. This process is similar to the LVQ (Leaming Vector
Quantization) algorithm. At the abnormality detection stage,
unknown spectrum is classified as normal or abnormal status
according to the label of the nearest neuron. That is, while
category formation is conducted by unsupervised manner,
classification criterion is conducted by supervised manner.
In the next section, several diagnostic techniques for GIS are
briefly reviewed. In the section three, a short introduction to
Kohonen's SONN is given. Application of SONN to abnormality
detection for GIS and experimental results are presented in the
section thereafter.
Diagnostic Technjques for GIS
In this section, GIS is used as the abbreviation for gas insulated
equipment. Investigation shows that breakdown due to insulation
occupies a high probability of faults in the abnormal oper~tion of
the GIS. The reason can be attributed to the fact that, while GIS
using SF6 are designed to be compact with high insulation
capability, small particles or mechanical un-adjustments while
operation result in the gradual loss of dielectric strength and
finally resulting in insulation breakdown[ I].
1171
A recent report on the technology of insulation diagnosis of
electlic power equipment in utilities has shown that more than
40% of the current problems are accountable for the difficulty in
the detection of abnmmal operation of GIS which are mainly due
to the inability of assessing the internal status of a GIS[l2]. PMT
plays an important role in alleviating this problem.
The PMT detects small internal partial discharges, that provide
signs of final insulation breakdown, with the help of sensors
attached outside to tank. This is done so as to detect the
abnollTlality at an early stage in order to avoid its development to
a major fault. Abnormality detection using PMT can be divided
into two major tasks, namely the development of sensors and the
development of the diagnostic algorithm.
Many developments have been reported for the detection of
partial discharges using sensors[l 2]. These can be classified into
the following major categories of I) Vibration or acoustics
detection. 2) Electronic detection. 3) Optical or heat detection,
and 4) Gas analysis detection. Examples of the first category are
acceleration sensor, detecting the mechanical vibration of the tank
and the ultrasonic sensor detecting the waves propagating inside
the tank. Examples of the second category are the monitoring of
the voltage for the detection of dielectric strength, and the use of
electrode built in the insulation spacer. Examples of the third
category are IRTV, detecting heat produced in the tank and
photosensor method, detecting the radiation of light. The last
categories to detect dissolved gases such as SF4, SOFz and HF
caused by partial discharge. It can be concluded from the above
examples that there exists several techniques for developing and
selecting a suitable sensor for efficient detection of partial
discharge.
During the past few years, there has been few developments in the
diagnostic algorithm and process using sensors. Most of the
currently employed diagnostic algorithms use simple "threshold
systems" . The basic principle of the threshold system is as
follows . "If the output of the sensor signal is less than the
predefined threshold, then the GIS system is of nollTlal status,
else if the output is greater than the threshold, then the system is
of abnollTlal status and the equipment needs to be investigated
before the abnormality develops into a major disturbance".
Even though the simple threshold system is easy to implement, it
is associated with two major drawbacks. The first drawback deals
with the difficulty in detection of the details of abnormality, such
as the kinds of causes or location. The threshold system can only
provide information between the normal and abnormal statuses of
the equipment. The second drawback deals with the influence of
the environmental noise on the response of the threshold system.
It usually inferred that in most cases, if the output of the sensor
signal is large then the GIS is in abnormal status. · However the
simple threshold system is capable of misclassifying the normal
Topological Neighborhood
·.
Label
Neuron
Input Data
Fig. I A Self-Organizing Neural Network
Neuron
• Category-1
Category-4
Feature Space
Fig.2 Self-Organization and Learning Vector Quantization
status in the presence of noise as that on the abnormal status. In
order to overcome these drawbacks, detection techniques which
are unaffected by environmental noise need to be employed for
accurate and efficient classification. Self Organizing Neural
Networks (SONN), which is described in the following section,
promise to achieve the above mentioned tasks.
Self-Organizing Neural Networks
A brief review of SONN and Learning Vector Quantization (LVQ)
is presented in this section. Kohonen's research for the
development of SONN and LVQ has been motivated by the
following experiment. In the experiment using cat visual systems,
the fact was found that the order of arrangement of visual cell on
retina is approximately same as the order of neuron excited by
1172
corresponding visual cell on visual regions on the brain. This kind
of correspondence between the arrangement of input and the
arrangement of neuron is called topological mapping.
Kohonen developed a solution to the above problem by expanding
it to competitive learning. His idea was that the neuron
topologically nearest to the most receptive neuron also responded
to the presentation of an similar input feature. In the following
discussion, it will be assumed that neurons are arranged in a 2
dimensional pattern depicted in figure I. The learning rule given
in [10,11] is as follows.
W(t+I)= IJ p IJ {
W.(t) + a(t)[x - W.(t)]
IJ W;j(t)
where,
ifi,jeN1it) if i,j ~ N1it)
(1)
W;i(t) weight vector of neuron at (i,j) position at epoch t
xP pth input feature vector
a(t) adaptation gain at epoch t
N1it) neighborhood set of neuron at (l,J) at epoch t
The most receptive neuron, i.e., the neuron most near to the input
feature is defined by equation (2).
(2)
The neighbourhood parameter is usually defined as the Euclidean
distance. Several other distance measures such as the inner
product can be employed since all components of the feature
vector considered as positive in this paper. As the learning
proceeds, the value of the adaptation gain and the neighborhood
reduce to zero in order to obtain stabilized convergence. In the
simulation, the initial value of adaptation gain a1 o was set to 0.9.
1 a(t) = a 0 t
1 N1J(t) = N0 -
t
(3) (4)
The initial value of neighborhood No was set to half of neuron
grid size and that also reduces to zero by the following equations.
In each epoch, learning rule (I) is applied to every presentation of
the input feature vector and the error, defined in equation (5), is
calculated. In the simulation, the convergence criterion is defined ·
by the maximum epoch number instead of error value, that is set
as a value of 20.
E = .!. L,~xP - wJJf 2 p
(5)
Actually speaking, learning rule (1) is the rule that reduces the
error defined by (5). The gradient by weight vector (5) is given in
equation (6). Equation (6) is the equivalent to the differential form
of learning rule ( 1 ).
~W.(t) =-VwE IJ ~
(6)
The essence of self-organization is equivalent to the clustering
algorithm shown in Figure 2 used for abnormality detection. In the
above figure, the data in the feature space are indicated as small
circles. The data with the same pattern are derived from the same
causes. The larger circles indicate the neuron of SONN. SONN
categorizes the data into four different types. Thus, each category
of data indicates the cause of abnormality. The learning algorithm
assigns the neurons in the feature space according to the
probability distribution of input data while preserving the network
topology. Each neuron has his own neighborhood region in the
feature space. In other words, feature space (or vector space) is
quantized into regions. The name LVQ is derived from this
characteristic. The neuron is the representative of each region and
is assigned the label of one of the abnormal categories. Kohonen's
LVQ algorithm is used for the fine adjustment of the boundary of
each region.
We guess, however, that strict LVQ algorithm is unnecessarily
and self-organization and assignment of the label to each neuron is
enough for abnormality detection of this case. This is because
strict LVQ is devised for fine adjustment of the boundary of each
category. This is effective for the case where the boundary is
complex. The experiments comparing the performance of SONN
for 5x5 and IOx I 0 neurons (Fig. IQ and II) show similar results.
This indicates there exists no-complex boundary and thus strict
LVQ has little effect for increasing the classification performance.
SONN has examples applied to security field[8,9] in the power
system.
Application of Self-Oreanjzine Neural Networks to
Abnormality Detection for GIS
Large quantity of experimental data is usually needed for
evaluating the performance of the neural network. However. it is
very difficult to obtain enough data from GIS operating the field.
Therefore experiments were conducted in the factory to simulate
actual fault conditions that would be expected to occur in the GIS
while in operation.
Figure 3 shows the simulated normal (Figure3. I) and various
possible abnormalities that occur in a GIS. In the above figure, the
junk is a small thin alurninum wire that simulates the presence of
the abnormal particle. Three practical conditions are simulated
corresponding to the relative location of the junk in the GIS tank,
which are classified as I) particle sticking to central conductor
(Figure 3.2), 2) particle sticking on the bottom of the tank (Figure
3.3), and 3) particle floating in the tank (Figure 3.4). One type of
abnormality is the contact failure between conductors(Figure 3.5).
The gap length is selected as a small value of less than 0.1 mm.
Another type of abnormality is contact failure between metal
fittings (Figure 3.6), which can be either due to loose fittings or
due to ageing and vibration of the tank.
1173
Tank of GIS y
Central conductor ....
l Normal
2 Conductor fixed junk
3 Tank fixed junk
4 Floating junk
5 Small gap between conductors
Bad contact ---I I L.J
6 Insufficiently fixed metal fitting
Fig.3 Examples of Experimental Abnonnalites
I Time Average I
+ Amplitude Normalization
i Input to Neural Network
60HZ
Fig. 4 Sampling of Accelaration Sensor and Preprocessing
These abnormalities cause partial discharge inside the tank, that
cause vibrations in the insulated gas. These vibrations cause the
mechanical vibration of the tank, that can be detected as the
change in acceleration with the help of the acceleration sensor
attached outside the tank. Acceleration sensors are employed in
this experiment due to their compact size, light weight and
portability in comparison with other sensors .
Figure 4 shows the two stages of sampling of the sensor signal
and preprocessing. The sensor signal is sampled at a rate of 853
samples per cycle (1/60 second for 60Hz). Although 1024 points
1 Normal
2 Conductor fixed junk
3 Tank fixed junk
!OO("GJ~ OCuGJ
-100{UG:Jd1 Ds
4 Floating junk
5 Small gap between conductors
6 Insufficiently fixed metal fitting
Fig. 5 Examples of Original Sensor Signal
1 Normal 4 Floating junk
51:Ji 1~
8~.1 2 Conductor
fixed junk
J Hi
3Tank fixed junk
5 Small gap between conductors
6 Insufficiently fixed metal fitting
Fig.6 Examples of Time Averaged Spectrum of the Sensor Signal
of samples is best for the experiments, it has been limited to 853
sample points due to hardware limitations. However, thi s
sampling rate covers the whole frequency limitation of the sensor
and creates no problem for simulations conducted later. Sampled
data for one cycle is transformed to 1024 points spectrum by Fast
Fourier Transform (FFT). Initially these spectrums are averaged
for one cycle to cancel the noise. Next, normalization is performed
to reduce 1024 dimensional vector to a 64 dimensional vector and
make the spectrum suitable as an input feature vector of unit
length . Manipulating I 024 dimensional vector is both
computationally cumbersome and time consuming and cannot be
used for fast abnormality detection. It was concluded that 64
vector dimension is enough for the present objective of
abnormality detection. It is possible to improve the perfonnance
1174
Gas Insulated Switchgear
FFT and other normalization
Self-Organizing Neural Network
Metal junk
Accelaration sensor
1 Normal 2 Conductor fixed junk 3 Tank fixed junk 4 Floating junk 5Small gap 6 Metall fitting
Fig. 7 The Architecture of Self-Organizing Neural Network
for larger dimensions, however, this require more computation
time and needs further study.
Figure 5 shows the examples of original sensor signal for both the
normal and abnormal status. In the above figure horizontal axis
indicates time scale of 1/60 second interval and vertical axis
indicates amplitude of sensor signal. The amplitude for the cases
of 'normal', 'conductor fixed junk', 'tank fixed junk' and 'floating
junk' is of the order of hundreds, while that of 'small gap between
conductors' is of the order of ten thousand and amplitude for
'insufficiently~fixed metal fitting' is of the order of thousands.
These examples show that the order of amplitude does not always
correspond to an abnormality status. This observation implies that
though the simple threshold system works well for most of the
cases, it is also capable of performing misclassification. In other
words, observation of amplitude or its corresponding value is not
enough for accurate abnormality detection. It is necessary to
obtain additional features in addition to the amplitude.
Figure 6 shows the examples of spectrum for both the normal and
abnormal status. Each status corresponds to the sensor signal
described in Figure 5. These spectrums are obtained after time
average but before normalization of dimension and vector length.
It is easier to distinguish between the normal and abnormal status
from the spectrum patterns rather than the original sensor signals.
This is the reason why the spectrum patterns are employed as
input feature vectors to the SONN. Figure 7 shows the complete
architecture of an abnormality detection system using SONN. The
above figure also illustrates the abnormality due to the case of
metal junk fixed on central conductor.
Simulation experiments were conducted in the following fashion.
The number of experimental data was approximately one hundred.
.................... ··1· .•. •• ·1· •• A A A A A A "" ......... ... ... ... .. " ... 4 ... " ......... " ... " ... ... A A A A A A
......... ... " " ... ... ... ... ... ... ... ... ·1·······1···· ........... ...
A A A A A A ... ..... " ... A A A A A A A ... " ...... ... ... ... A A A A ... A A ... ... ......... " ...
··1·······1 •.• ~ " ... " ...... .. ... ... " " .. : ... : ... : .. :..:"" ... :
Fig. 8 An Example of Learned 2 Dimensional Self-Organizing
Neural Network ( 5 x 5 neurons )
Fig. 9 An Example of Learned 2 Dimensional Self-Organizing
Neural Network ( 10 x 10 neurons )
Six data for normal status, 55 data for conductor fixed junk and so
on. Each data is transformed into an input feature vector of 64
dimensional unit vector. During learning, all of the learning data
were presented to the network for each epoch and learning rule of
equation (l) was applied for each learning data. After convergence,
all of the learning data were again presented to the network together
with the assignment of the label of each neuron. Each neuron
searches the nearest learning data according to equation (2) and is
assigned the abnormal status corresponding to the label of the
learning data as a label.
Figures 8 and 9 shows the examples of labeled 5x5 and lOxlO
SONN respecti vely. The number of causes corresponds to the
number in the Figure 3. Because of the competitive learning with
1175
-~ 0 ...__.. Q) +-' co a: c 0 :e c C> 0 (.) Q)
a:
100.00
80.00
60.00
40.00
20.00
SONN(5x5)
I ...................... + ..........
10 20 30 40 50 60 70 80 90 0/o of Training Data
Fig. 10 Recognition Rate of Self-Organizing Neural Network
of 5 x 5 Neurons
-~ 0 ...__.. Q)
-rn a: c 0 :e c C> 0 U. Q)
a:
SONN(1Ox10) 100.00 --.-- .....---.--.....--.-- ,.--.--,..--,
0. 00 - ........ -+--i---+....-...1----1""~1'--"...,.....--"1
10 20 30 40 50 60 70 80 90
0/o of Training Data
Fig. 11 Recognition Rate of Self-Organizing Neural Network
of 10 x 10 Neurons
neighborhood, the same causes are assigned to the nearby neuron
in the 2 dimensional topology. This result can also be
contemplated from the Figure 2. Identical causes. result in the
formation of similar spectrum patterns. In the feature space (also
termed as spectrum space) the identical causes result in the
approximate distribution of neurons in the neighborhood with the
weights of the neuron corresponding to the cluster centers while
preserving the 2 dimensional network topology. Comparison of
Figures 8 and 9 shows the similarity of class distribution. Classes
I and 6 are widely separated and occupy diagonal locations, while
classes 1 and 4 are closely located. Classes 4 and 6 are also
closely located. However, further study is needed to understand
the distribution of the topology and the positions of each class.
Learning time of the SONN was of the order of a few minutes
simulated on SUN Spare Station 370 (l 7MIPS) that is
comparatively faster than Back Propagation (BP) learning. In the
previous work presented in[6,7] BP was employed that required
more than 1 hour for learning.
Figure 10 and 11 show results of the performance evaluation of
the SONN. The network size considered is of 5x5 for Figure JO
and !Ox 10 for Figure 11. The horizontal axes represents the
percentage of data used for learning from the whole experimental
data. For instance, the number '80' on the horizontal axes implies
that 80% of experimental · data was used for learning while the
remaining 20% of data were used for evaluation.
The definition of the terms 'correct' and 'incorrect' occurring in the
above figures are defined as follows . If the label of the nearest
neuron to the evaluating data is the same as that of learning data, it
is classified as correct, i.e ., after learning, each neuron is
associated with the label corresponding to either 'normal' or
'abnormal' status as shown in Figure 8 and Figure 9. The nearest
neuron is obtained from equation (2). If the label of this nearesl
neuron is as same as the cause of the evaluating data, then the
classification is defined as 'correct'. On the other hand, if the label
of the nearest neuron is different from that of evaluating data, it is
classified as 'incorrect'.
Investigation on above figures explains that approximately 80%
correction rate can be achieved with the present experimental data.
and slightly higher correction rate can be obtained if more learning
data is available . Comparison between Figure 10 and Figure 1.1
shows that similar results can be obtained for different network
sizes. The number of neurons for the. 1Ox10 network is four times
larger than that of the 5x5 network, i.e., SONN with 25 (5x5)
neurons is found to suitable for satisfactory perforrpance compared
to that of the SONN with 100 (1Oxl0) neurons for this
experiment.
The correction rate of 80% needs further study to improve the
classification performance. The factor responsible for decreasing
the correction rate is the data for class four corresponding to the
abnormal case of 'floating junk'. The junk piece , simulated by
means of a small thin aluminium wire, moves around the inside of
the tank. When the junk is near to the conductor, the abnormal
status is classified as that of 'fixed junk' (class 2) and when it is
near the bottom of the tank it is classified as 'tank fixed junk' (class
3). The classification rate is mainly inherent in the data other than
neural network architecture. This observation also suggests that
the same classification rate can be expected if all of the 100% of
the data is used for training the neural network. Further study
needs to be carried out to improve its performance. One such
possible strategy is the use of sensor fusion that employs more
than two kinds of sensor signals for efficient abnormality
1176
discrimination .
In the Figure 11, BP(MNN) represents the result of neural
network using backpropagation conducted for the same
experimental setup described in [6,7]. If enough learning data is
available, BP achieves similar. correction rate as SONN. BP,
however, achieves poor performance due to the . limited
availability of learning data This difference of capability may be
derived from the difference of mathematical structure of SONN
and BP. In other words, SONN creates cluster centers of each
category in the feature space while BP must learn every boundary
of categories. Creating cluster centers is possible even for a few
number of learning data. Estimation of the exact boundary,
however, is difficult unless enough number of learning data is
The proposed method are still at a preliminary stage. On-line
learning in the real field, validation for other types of GIS and
combinations with other sensors are the next issue. The authors
expect that ANNs including SONN will have large potential for
practical diagnosis in the near future and are currently designing
the portable prototype system. The authors are conducting the
field tests to evaluate the system in the next few years.
References
1) IEE of Japan, "The report on diagnostic technologies and
reliability on switchgears", Vol.290, Jan. 1989.
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Compared to BP, SONN has the advantages of fast learning and Adaptive Pattern Recognition and Self Organizing higher correction rate for small number of learning data. On the Techniques", Proc. of the 5th PSCC, pp.1-16, 1975.
other hand, BP may have the advantages such that BP can learn
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real field. This requirement gives rise to the advantage of SONN
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The proposed SONN structure is valid for all GIS. The learned
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simulations or with similar mechanical structure. However, since
major parts of the GIS are designed in accordance to a standard
specification and consist of a combination of these parts, both of
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similar GIS configurations.
Copclusjop
GIS are widely used power systems due to the main advantages of
compactness, excellent insulation strength and reliability of
operation. The enclosed structure of the GIS has prompted the
requirement of the PMT and related efficient and accurate
diagnostic algorithms other than conventional threshold system.
The ANN, that has the capabilities of category formation and
classification, is one of the potential methodologies to establish
required diagnostic algorithm. In this paper, the SONN is applied
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for small number of learning data that BP can perform only poor
results. (3) The learning time of SONN is a few minutes. It is
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