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Use of pollution monitors with a neural network to predict insulator flashover
ELSEVIER
Electric Power Systems Research 42 (1997) 27-33
|L|¢?RI¢ POIJ,iBR 8W$?ll'fl$ R||BflRCH
Use of pollution monitors with a neural network to predict insulator flashover Paul Cline *, Will Lannes, Gill Richards Department of Electrical Engineering, University of New Orleans, New Orleans, LA 70148, USA
Received 14 February 1995
Abstract
Insulators on power distribution lines in industrial zones become contaminated from particulates in the air, causing flashovers. Insulators may be cleaned occasionally, but this is a costly process. In this paper, a neural network is trained to interpret data from two pollution-related monitoring devices to estimate the imminence of ftashover on substation insulators. The monitoring devices are the UE-386 Ultrasound monitor and the CAT-ILD leakage current monitor. Data is taken from flashover tests conducted at the Electric Power Research Institute (EPRI) High Voltage Testing Research Center (HVTRC). The neural network predictions are validated with a test set of flashover experiments. © 1997 Published by Elsevier Science S.A. Keywords: Insulator pollution; Leakage current; Neural networks; High voltage testing
1. Introduction
Insulator contamination in electric distribution systems and substations is caused by airborne particles which are deposited on insulator surfaces. When wetted, the particles may combine to form a conductive layer which may then initiate a flashover, an intense current discharge. The flashover necessitates deactivating the power line, causing an interruption in service. Service interruptions are especially troublesome to industrial customers who have critical loads. For them power interruptions, even when reclosing is successful, may precipitate expensive plant or process shutdowns. Yet it is these customers who are most often afflicted with pollution caused flashovers, because of their location in industrial zones where valve, smokestack, and cooling tower substance discharges are common. To reduce the problem, distribution engineers in affected locations clean insulators on a regular basis. The cleaning process is expensive, and may itself involve deactivation of the circuit involved. Thus the timing and location of insulator cleaning is a subject of interest to distribution engineers. Timely recognition of
* Corresponding author. Tel.: + 1 504 2866650.
the need for insulator cleaning is important, but it is also important not to clean too often or too soon. This paper examines use of two monitoring devices, the UE-386 Ultrasound (high frequency sound) monitor and the C A T - I L D insulator leakage current monitor, combined with a local indication of relative humidity, to predict imminent flashover on contaminated insulators. Although high indications from all these devices might be assumed to coincide roughly with flashover conditions, information contained in their magnitude relative to each other, as well the scaling factor, is not intuitively obvious. F o r this reason an explicit functional relationship between data and flashover m a y not be found, but a neural network can be used instead. This approach to flashover prediction has been tested previously, at an operating substation in Louisiana, using weather data instead of ultrasound and leakage current to provide input data [1]. The results in those tests indicated that weather was a fair indicator of pollution, but further validation was not possible because of the scarcity of naturally occurring flashover events at the substation. For this reason, it was concluded that actual high voltage tests, with leakage current and ultrasound as training inputs, would be a more appropriate approach.
0378-7796/97/$17.00 © 1997 Published by Elsevier Science S.A. All rights reserved. PII S0378-7796(96)01 173-X
28
P. Cline et al./Electric Power Systems Research 42 (1997) 27 33
2. Neural network The procedure was to train a neural network on data taken from high voltage tests, which were conducted at the High Voltage Testing Research Center (HVTRC) in Lenox, Massachusetts. The trained network was then validated (as to its accuracy in predicting flashovers) with further H V T R C tests. Both the standard backpropagation network (BPN) and a new radial basis function network (RBFN) were used for the tests. RBFNs are feedforward, multilayer neural networks which employ two different types of neurons, local basis neurons implementing the Gaussian distance function of Eq. (1), and traditional neurons with appropriate activation functions. If Uj is the response of the Gaussian neuron to a single input vector x, then: Uj = e x p [ - (x - Uj)T(X-- Uj)/2S 2]
(1)
where the uj term is the vector representing the cluster center of this Gaussian neuron. The variance term in the denominator, s, is calculated by summing the distances of those n nearest neighbour vectors which are associated with the cluster center. A trainable weight matrix lies between the two sets of neurons, as shown in Fig. 1. These weights are adjusted for minimum training error in the back propagation process. It is necessary to select the number of cluster centres to be uses a priori. A practical approach, which is used here, is to select cluster centres from observation of the training data. The number of natural cluster centres present in the n-dimensional input space determines the number of Gaussian neurons.
3. Testing A series of high voltage flashover tests were conducted at H V T R C to establish the relationship between
~ Output
I'~ crons
S upew',¢~ed
Ur~upe~I~ed
Fig. 1. Radial basis function network.
leakage current, humidity conditions, ultrasound, and flashover. One of the purposes was to record and validate a neural network training set for conditions leading to flashover. Insulators from Louisiana substations were subjected to voltages, constant or increased in steps, in a controlled chamber with high humidity in what is known as a clean fog test. The insulators were initially tested with natural contamination intact from the Louisiana environment. After natural tests, the insulators were artificially contaminated with a salt/clay slurry and again subjected to either step-increased voltage or a constant voltage level. Four different insulators were tested in the chamber, two cap and pin units and two station post units. A summary of the test results is given in Tables 1 and 2. These tables show the various flashover voltages and information on cautionary activity before a flashover; relative humidity levels are also given. The leakage current was measured with a three channel data acquisition system which samples the leakage current every minute for a 10 s interval at the end of the minute and provides an average output divided into 5 ranges, or bins. This device is available commercially under the name CAT-ILD. The number in each bin (1-5 ma, 5 - 1 0 ma, 10-20 ma, 20-50 ma, and > 50 ma) is the portion of leakage current which falls in the bin's range during the sample period. Fig. 2 shows a typical leakage current reading for Test 1, showing leakage currents for Cap and Pin Unit 1. The five different traces (three are visible) represent the five bin values for each minute of the test. The leakage current was also measured by oscilloscope through a shunt resistor measuring circuit. Relative humidity was measured by a mechanical psychrometer in the test chamber at irregular intervals. The humidity was recorded at the ambient level and then every few minutes until the chamber humidity was established at 100% relative humidity. Chamber temperature was measured simultaneously with humidity. The ultrasonic noise was detected with a UE 386 detector made by UE Systems. This instrument detects noise levels centred around 40 kHz. The U E 386, a highly directional detector, was pointed at Cap and Pin Unit 1 during all the tests. The U E 386 provides both a current loop output and a proportional 0 - 1 0 V output which is a rectified, smoothed indication of the noise level. The proportional voltage output was used in conjunction with a strip chart recorder to record output levels. An example of the ultrasound output is shown in Fig. 3. Test 1 (3/2/94) represents the natural response of the insulators to a decreasing flashover margin, which is the difference between an applied voltage and flashover voltage for a given insulator. The level of contamination present, as measured by Equivalent Salt Density Deposit (ESDD), was found by a sponge removal
29
P. Cline et al. ,,/Electric Power Systems Research 42 (1997) 27-33
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P. Cline et al./ Electric Power Systems Research 42 (1997) 27-33
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Fig. 2. CAT-ILD results, Test 1.
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Ultrasound
Measurement
Fig. 3. Ultrasound measurement, Test 1.
procedure applied to a small part of the insulator tested. During Test 1, the insulators were subjected to step increases in voltage with a 10-15 rain pause at each level, until flashover occurred. The regularity of the step process and the relative completeness of data for Tes~ 1 make it attractive as a basis for a neural network training set. The data set, including humidity, leakage current, ultrasound, and voltage margin, for Test I was scaled using the z-scoring method and used to train both an R B F N and a conventional backpropagation network, or BPN. The output for the neural network is voltage
Table 3 BPN results for Test 3 flashover margin prediction No. of hidden neurons
MSE training
MSE test
10 7 4 3 2
13 13 18 22 73
522 397 285 84 155
31
margin measured in kV. A small voltage margin will indicate the need for insulator cleaning. Both neural networks were trained using the data from H V T R C Test 1. The networks were then tested on the same test set, which was the data from insulator number 1 in Test 3. Independence of all tests from each other is assumed. In the test set, 76 patterns are present, one for each minute of the test. Table 3 shows the training and testing results of the Back Propagation Network run made on H V T R C Test 3 (3/7/94). Fig. 4 shows the convergence of the BPN for this same test. The horizontal axis counts the number of training epochs, complete runs through all 76 patterns. MSE is the value on the vertical axis. After 20 epochs, the test error reaches its lowest value at 84 and then oscillates between nearby values for the rest of the test run. The 'ringing' effect in the first eight epochs is a common occurrence in network testing. Table 3 also serves to point out the effects of underfitting and overfitting the neural network training data. With the number of hidden neurons set at 10, the MSE for the training set is low. The neural network is in effect learning the training set so well that it cannot generalize to the test set. This phenomenon has its reverse when the number of hidden neurons is set at 2. Here, the network does not have enough variables to successfully model the process. The appropriate number of hidden neurons for this test appears to be three. With three hidden neurons, and nineteen trainable weights, the network can train to a high degree and still generalize the process for the test set. Table 4 shows the results of the R B F N on Test 3 test set for different numbers of cluster centres and learning constants• As the number of cluster centres increases, the training and testing error appears to diminish. This result agrees with a basic tenet of approximation networks; as the number o f cluster centres approaches the number of training patterns the functional approximation becomes more and more exact• The MSE of the two networks can be exactly compared. In the case of Test 3, the BPN error is roughly four time less than the comparable R B F N result. Another way of looking at the prediction success is to examine the success/failure rates where success is defined as a warning of nearness to flashover when such a distinction is warranted and prediction of safe operation when the insulator is operating within constraints. Failure is defined as a warning of flashover hazard when such a warning is not needed, a false alarm, and prediction of safe operation when the insulator is in a danger zone, a failure to alarm. The following four tables show success/failure rates for both networks for Test 3. All of the tables were developed from results of the H V T R C Test 3 neural network prediction. Table 5 shows the results for the training case. The neural
32
P. Cline et al./Electric Power Systems Research 42 (1997) 27-33
2~
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,o
Fig. 4. Training convergence vs testing for Test 3.
network correctly identified 91.53% of the safe operation cases to be safe. During safe operation, the neural network incorrectly judged several patterns to be unsafe, 8.47%. The high percentage of false alarms during hazardous operation stems from the low number of training cases in the hazardous region. During the tests, the insulator spent less time operating at dangerous voltage levels, the levels just before a flashover, than during normal operating levels. This could be remedied Table 4 RBFN results for Test 3 flashover margin prediction Cluster centres
Learning constant
MSE training
MSE test
30
0.00051 0.51 0.00051 0.51 0.00051 0.51
42 32 100 59 145 144
239 855 223 648 335 332
15 5
Table 5 BPN success/failure rates (training)
by training the network on a greater number of patterns. Table 6 exhibits the test run results for the back propagation network. Just as the PSE is expected to rise from the training MSE in accordance with Eq. (4.1), so will the testing false alarm rate increase over the training rate. Tables 7 and 8 show the alarm rates for the Radial Basis Function network. The false alarm rates for both training and testing are 50% or greater, an unacceptable rate for practical reasons. In these Tables, a failure determination is made if the prediction is outside 10% of the true value. The 10% range was chosen to coincide with the step sizes of voltage increase made during the high voltage tests. Hazardous operation is defined as operating within 25% of flashover voltage.
Table 7 RBFN success/failure rates (training)
%Correct prediction %False prediction Hazardous operation Safe operation
58.83
41.17
91.53
8.47
Table 6 BPN success/failure rates (testing)
%Correct prediction %False prediction Hazardous operation Safe operation
70.59
86.11
13.89
Table 8 RBFN success/failure rates (testing)
%Correct prediction %False prediction Hazardous operation Safe operation
29.41
75
25
88.89
11.11
%Correct prediction %False prediction Hazardous operation Safe operation
50
50
33.33
66.67
P. Cline et al./ Electric Power Systems Research 42 (1997) 27-33
33
4. Conclusions
Acknowledgements
A neural network trained on information from a single high voltage insulation test can be used to predict the nearness of pollution flashovers in other unrelated tests. Table 6 shows a false alarm rate of 11.11% during safe operation and 25% during hazardous operation. This fact must take into account the low number of four hazardous operation training points. Improvement could be made by training the network with a training set of expanded size. By training the neural net with data from all of the high voltage tests and testing on future tests, significant improvement in the false alarm rate could be attained.
The work described was supported by the Electric Power Research Institute and Energy Corporation. The authors appreciate help in the testing process given by EPRI and Entergy engineers and technicians.
References [1] P. Cline and G.G. Richards, Insulator contamination estimation using a neural network, Proc. Third Int. Conf. Fuzzy Logic, Duke University, October 18-22, 1993. (Invited Paper).
Electric Power Systems Research 42 (1997) 27-33
|L|¢?RI¢ POIJ,iBR 8W$?ll'fl$ R||BflRCH
Use of pollution monitors with a neural network to predict insulator flashover Paul Cline *, Will Lannes, Gill Richards Department of Electrical Engineering, University of New Orleans, New Orleans, LA 70148, USA
Received 14 February 1995
Abstract
Insulators on power distribution lines in industrial zones become contaminated from particulates in the air, causing flashovers. Insulators may be cleaned occasionally, but this is a costly process. In this paper, a neural network is trained to interpret data from two pollution-related monitoring devices to estimate the imminence of ftashover on substation insulators. The monitoring devices are the UE-386 Ultrasound monitor and the CAT-ILD leakage current monitor. Data is taken from flashover tests conducted at the Electric Power Research Institute (EPRI) High Voltage Testing Research Center (HVTRC). The neural network predictions are validated with a test set of flashover experiments. © 1997 Published by Elsevier Science S.A. Keywords: Insulator pollution; Leakage current; Neural networks; High voltage testing
1. Introduction
Insulator contamination in electric distribution systems and substations is caused by airborne particles which are deposited on insulator surfaces. When wetted, the particles may combine to form a conductive layer which may then initiate a flashover, an intense current discharge. The flashover necessitates deactivating the power line, causing an interruption in service. Service interruptions are especially troublesome to industrial customers who have critical loads. For them power interruptions, even when reclosing is successful, may precipitate expensive plant or process shutdowns. Yet it is these customers who are most often afflicted with pollution caused flashovers, because of their location in industrial zones where valve, smokestack, and cooling tower substance discharges are common. To reduce the problem, distribution engineers in affected locations clean insulators on a regular basis. The cleaning process is expensive, and may itself involve deactivation of the circuit involved. Thus the timing and location of insulator cleaning is a subject of interest to distribution engineers. Timely recognition of
* Corresponding author. Tel.: + 1 504 2866650.
the need for insulator cleaning is important, but it is also important not to clean too often or too soon. This paper examines use of two monitoring devices, the UE-386 Ultrasound (high frequency sound) monitor and the C A T - I L D insulator leakage current monitor, combined with a local indication of relative humidity, to predict imminent flashover on contaminated insulators. Although high indications from all these devices might be assumed to coincide roughly with flashover conditions, information contained in their magnitude relative to each other, as well the scaling factor, is not intuitively obvious. F o r this reason an explicit functional relationship between data and flashover m a y not be found, but a neural network can be used instead. This approach to flashover prediction has been tested previously, at an operating substation in Louisiana, using weather data instead of ultrasound and leakage current to provide input data [1]. The results in those tests indicated that weather was a fair indicator of pollution, but further validation was not possible because of the scarcity of naturally occurring flashover events at the substation. For this reason, it was concluded that actual high voltage tests, with leakage current and ultrasound as training inputs, would be a more appropriate approach.
0378-7796/97/$17.00 © 1997 Published by Elsevier Science S.A. All rights reserved. PII S0378-7796(96)01 173-X
28
P. Cline et al./Electric Power Systems Research 42 (1997) 27 33
2. Neural network The procedure was to train a neural network on data taken from high voltage tests, which were conducted at the High Voltage Testing Research Center (HVTRC) in Lenox, Massachusetts. The trained network was then validated (as to its accuracy in predicting flashovers) with further H V T R C tests. Both the standard backpropagation network (BPN) and a new radial basis function network (RBFN) were used for the tests. RBFNs are feedforward, multilayer neural networks which employ two different types of neurons, local basis neurons implementing the Gaussian distance function of Eq. (1), and traditional neurons with appropriate activation functions. If Uj is the response of the Gaussian neuron to a single input vector x, then: Uj = e x p [ - (x - Uj)T(X-- Uj)/2S 2]
(1)
where the uj term is the vector representing the cluster center of this Gaussian neuron. The variance term in the denominator, s, is calculated by summing the distances of those n nearest neighbour vectors which are associated with the cluster center. A trainable weight matrix lies between the two sets of neurons, as shown in Fig. 1. These weights are adjusted for minimum training error in the back propagation process. It is necessary to select the number of cluster centres to be uses a priori. A practical approach, which is used here, is to select cluster centres from observation of the training data. The number of natural cluster centres present in the n-dimensional input space determines the number of Gaussian neurons.
3. Testing A series of high voltage flashover tests were conducted at H V T R C to establish the relationship between
~ Output
I'~ crons
S upew',¢~ed
Ur~upe~I~ed
Fig. 1. Radial basis function network.
leakage current, humidity conditions, ultrasound, and flashover. One of the purposes was to record and validate a neural network training set for conditions leading to flashover. Insulators from Louisiana substations were subjected to voltages, constant or increased in steps, in a controlled chamber with high humidity in what is known as a clean fog test. The insulators were initially tested with natural contamination intact from the Louisiana environment. After natural tests, the insulators were artificially contaminated with a salt/clay slurry and again subjected to either step-increased voltage or a constant voltage level. Four different insulators were tested in the chamber, two cap and pin units and two station post units. A summary of the test results is given in Tables 1 and 2. These tables show the various flashover voltages and information on cautionary activity before a flashover; relative humidity levels are also given. The leakage current was measured with a three channel data acquisition system which samples the leakage current every minute for a 10 s interval at the end of the minute and provides an average output divided into 5 ranges, or bins. This device is available commercially under the name CAT-ILD. The number in each bin (1-5 ma, 5 - 1 0 ma, 10-20 ma, 20-50 ma, and > 50 ma) is the portion of leakage current which falls in the bin's range during the sample period. Fig. 2 shows a typical leakage current reading for Test 1, showing leakage currents for Cap and Pin Unit 1. The five different traces (three are visible) represent the five bin values for each minute of the test. The leakage current was also measured by oscilloscope through a shunt resistor measuring circuit. Relative humidity was measured by a mechanical psychrometer in the test chamber at irregular intervals. The humidity was recorded at the ambient level and then every few minutes until the chamber humidity was established at 100% relative humidity. Chamber temperature was measured simultaneously with humidity. The ultrasonic noise was detected with a UE 386 detector made by UE Systems. This instrument detects noise levels centred around 40 kHz. The U E 386, a highly directional detector, was pointed at Cap and Pin Unit 1 during all the tests. The U E 386 provides both a current loop output and a proportional 0 - 1 0 V output which is a rectified, smoothed indication of the noise level. The proportional voltage output was used in conjunction with a strip chart recorder to record output levels. An example of the ultrasound output is shown in Fig. 3. Test 1 (3/2/94) represents the natural response of the insulators to a decreasing flashover margin, which is the difference between an applied voltage and flashover voltage for a given insulator. The level of contamination present, as measured by Equivalent Salt Density Deposit (ESDD), was found by a sponge removal
29
P. Cline et al. ,,/Electric Power Systems Research 42 (1997) 27-33
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P. Cline et al./ Electric Power Systems Research 42 (1997) 27-33
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P. Cline et al. / Electric Power Systems Research 42 (1997) 2 7 - 3 3
% TIME AT C U R R E N T LEVEL
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100 80
40
20 o
0
4
8
12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 TIME (MINUTES) - -
~
a 6-10ma 11-20ma 21-50ma >50ma I
Fig. 2. CAT-ILD results, Test 1.
c i.( ![i'.'
i. . . . .
=~
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~
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~
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Ultrasound
Measurement
Fig. 3. Ultrasound measurement, Test 1.
procedure applied to a small part of the insulator tested. During Test 1, the insulators were subjected to step increases in voltage with a 10-15 rain pause at each level, until flashover occurred. The regularity of the step process and the relative completeness of data for Tes~ 1 make it attractive as a basis for a neural network training set. The data set, including humidity, leakage current, ultrasound, and voltage margin, for Test I was scaled using the z-scoring method and used to train both an R B F N and a conventional backpropagation network, or BPN. The output for the neural network is voltage
Table 3 BPN results for Test 3 flashover margin prediction No. of hidden neurons
MSE training
MSE test
10 7 4 3 2
13 13 18 22 73
522 397 285 84 155
31
margin measured in kV. A small voltage margin will indicate the need for insulator cleaning. Both neural networks were trained using the data from H V T R C Test 1. The networks were then tested on the same test set, which was the data from insulator number 1 in Test 3. Independence of all tests from each other is assumed. In the test set, 76 patterns are present, one for each minute of the test. Table 3 shows the training and testing results of the Back Propagation Network run made on H V T R C Test 3 (3/7/94). Fig. 4 shows the convergence of the BPN for this same test. The horizontal axis counts the number of training epochs, complete runs through all 76 patterns. MSE is the value on the vertical axis. After 20 epochs, the test error reaches its lowest value at 84 and then oscillates between nearby values for the rest of the test run. The 'ringing' effect in the first eight epochs is a common occurrence in network testing. Table 3 also serves to point out the effects of underfitting and overfitting the neural network training data. With the number of hidden neurons set at 10, the MSE for the training set is low. The neural network is in effect learning the training set so well that it cannot generalize to the test set. This phenomenon has its reverse when the number of hidden neurons is set at 2. Here, the network does not have enough variables to successfully model the process. The appropriate number of hidden neurons for this test appears to be three. With three hidden neurons, and nineteen trainable weights, the network can train to a high degree and still generalize the process for the test set. Table 4 shows the results of the R B F N on Test 3 test set for different numbers of cluster centres and learning constants• As the number of cluster centres increases, the training and testing error appears to diminish. This result agrees with a basic tenet of approximation networks; as the number o f cluster centres approaches the number of training patterns the functional approximation becomes more and more exact• The MSE of the two networks can be exactly compared. In the case of Test 3, the BPN error is roughly four time less than the comparable R B F N result. Another way of looking at the prediction success is to examine the success/failure rates where success is defined as a warning of nearness to flashover when such a distinction is warranted and prediction of safe operation when the insulator is operating within constraints. Failure is defined as a warning of flashover hazard when such a warning is not needed, a false alarm, and prediction of safe operation when the insulator is in a danger zone, a failure to alarm. The following four tables show success/failure rates for both networks for Test 3. All of the tables were developed from results of the H V T R C Test 3 neural network prediction. Table 5 shows the results for the training case. The neural
32
P. Cline et al./Electric Power Systems Research 42 (1997) 27-33
2~
150
I\
J
• °..
,o
Fig. 4. Training convergence vs testing for Test 3.
network correctly identified 91.53% of the safe operation cases to be safe. During safe operation, the neural network incorrectly judged several patterns to be unsafe, 8.47%. The high percentage of false alarms during hazardous operation stems from the low number of training cases in the hazardous region. During the tests, the insulator spent less time operating at dangerous voltage levels, the levels just before a flashover, than during normal operating levels. This could be remedied Table 4 RBFN results for Test 3 flashover margin prediction Cluster centres
Learning constant
MSE training
MSE test
30
0.00051 0.51 0.00051 0.51 0.00051 0.51
42 32 100 59 145 144
239 855 223 648 335 332
15 5
Table 5 BPN success/failure rates (training)
by training the network on a greater number of patterns. Table 6 exhibits the test run results for the back propagation network. Just as the PSE is expected to rise from the training MSE in accordance with Eq. (4.1), so will the testing false alarm rate increase over the training rate. Tables 7 and 8 show the alarm rates for the Radial Basis Function network. The false alarm rates for both training and testing are 50% or greater, an unacceptable rate for practical reasons. In these Tables, a failure determination is made if the prediction is outside 10% of the true value. The 10% range was chosen to coincide with the step sizes of voltage increase made during the high voltage tests. Hazardous operation is defined as operating within 25% of flashover voltage.
Table 7 RBFN success/failure rates (training)
%Correct prediction %False prediction Hazardous operation Safe operation
58.83
41.17
91.53
8.47
Table 6 BPN success/failure rates (testing)
%Correct prediction %False prediction Hazardous operation Safe operation
70.59
86.11
13.89
Table 8 RBFN success/failure rates (testing)
%Correct prediction %False prediction Hazardous operation Safe operation
29.41
75
25
88.89
11.11
%Correct prediction %False prediction Hazardous operation Safe operation
50
50
33.33
66.67
P. Cline et al./ Electric Power Systems Research 42 (1997) 27-33
33
4. Conclusions
Acknowledgements
A neural network trained on information from a single high voltage insulation test can be used to predict the nearness of pollution flashovers in other unrelated tests. Table 6 shows a false alarm rate of 11.11% during safe operation and 25% during hazardous operation. This fact must take into account the low number of four hazardous operation training points. Improvement could be made by training the network with a training set of expanded size. By training the neural net with data from all of the high voltage tests and testing on future tests, significant improvement in the false alarm rate could be attained.
The work described was supported by the Electric Power Research Institute and Energy Corporation. The authors appreciate help in the testing process given by EPRI and Entergy engineers and technicians.
References [1] P. Cline and G.G. Richards, Insulator contamination estimation using a neural network, Proc. Third Int. Conf. Fuzzy Logic, Duke University, October 18-22, 1993. (Invited Paper).