Electric Power Systems Research 78 (2008) 16861692

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Electric Power Systems Research

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Prediction of leakage current of non-ceramic insulators in early aging period

Ayman H. El-Haga,, Ali Naderian Jahromib, Majid Sanaye-Pasandc

a Electrical Engineering Department, American University of Sharjah, Sharjah, United Arab Emiratesb Kinectrics Inc., Transmission & Distribution Technologies, Toronto, Canadac Electrical and Computer Engineering Department, University of Tehran, Iran

a r t i c l e i n f o

Article history:Received 10 September 2007Received in revised form 11 February 2008Accepted 24 February 2008Available online 8 April 2008

Keywords:Outdoor insulatorsAgingLeakage currentNeural network

a b s t r a c t

The paper presents a neural network based prediction techniqueceramic insulators during salt-fog test. Nearly 50 distribution classthree different voltage classes have been tested in a salt-fog chamberrecorded for at least 100h. A boundary for early aging period is de

radiasulatin intgingtwor5.3%

2

1. Introduction

One of the main causes of aglators is the development of leaksurface leading to dry-band arcintored to evaluate the insulators sand accelerated aging test condiconducted to understand the reldation of SIR insulators [111]. ItLC low frequency harmonics (mharmonic components) is highlylator surface damage [2,3]. WhenLC exceeds 1mA during salt-fog tface of the SIR [2]. Another studthe rotating wheel dip test as themonitor the early aging period of SIR insulators [6]. It has beenreported that if the peak value of the LC attains 1mA, the insula-tors lose their hydrophobicity and thedamageon the surfacebeginswhen the LCapproaches4mA[6]. Kumagai andYoshimura [11] sep-arated the leakage current duringcomponents: sinusoidal, transitiothat the cumulative charges of ththe hydrophobicity and the contsurfaces.

Corresponding author.E-mail address: aelhag@aus.edu (A.H.

ervers whis pidityminainteroverreviond s

e levce dhas b. Clabee

e LCmagnitude of the fundamental and harmonic components of theLC. Another network has been trained to classify the waveformas sinusoidal, nonlinear, or containing discharge. A feed-forwardback-propagation ANN with two layers has been employed for the

0378-7796/$ see front matter 2008 Edoi:10.1016/j.epsr.2008.02.010salt-fog test into three differentn, and local arc. They have shownese components are sensitive toamination level of the insulating

El-Hag).

classication [3]. Although the classication has been successful,no attempts have been made to predict the level of LC.

The classication of surface conditions for polymeric materi-als, assessed in inclined plane test, has been studied by using ANN[14]. The process of identifying of the surface condition of non-ceramic insulators was automated in this study. An ANN basedclassier was used to categorize the LC measured in the inclinedplane test. Amultilayer feed-forward ANNwith a back-propagationlearning algorithm has been employed. Although the authors have

lsevier B.V. All rights reserved.instead of a xed threshold value. Consequently, the Gaussianpredict the level of LC at the early stage of aging of the SIR innetwork. The initial values of LC and its rate of change at 10mthe input to the network, and the nal value of LC of the early athe network. It is found that Gaussian radial basis function nemethod is an appropriate network to predict the LC with a 3.5testing data are selected from the same type of SIR insulators.

ing of silicone rubber (SIR) insu-age current (LC) on the insulatorg. Therefore, LC is usually moni-urface condition under both eldtions. Several studies have beenation between the LC and degra-has been found that the level ofainly the fundamental and thirdcorrelated to the degree of insu-the fundamental component of

est, erosion is evident on the sur-y has been carried out by usingaccelerating aging technique to

Moreover, it has been obsnon-ceramic insulators occuin the eld as well [7,8]. Thisthat are exposed to high humThis value depends on the noity, and the period of the dryinsulator may or may not rec

So it is evident from the ption between the level of LC ainsulators. As a result, if thpossible to forecast the surfacial neural network (ANN)related to outdoor insulatorssured in clean-fog test hasbeen used to categorize thfor the leakage current (LC) of non-silicone rubber (SIR) insulators with, where the LC has been continuouslyned by the rate of change of the LCl basis network has been adopted toors and is compared with a classicalervals for the rst 5h are selected asperiod is considered as the output ofk with a random optimizing trainingaccuracy, if the training data and the

008 Elsevier B.V. All rights reserved.

d that the surface degradation ofen the LC exceeds certain values

articularly relevant for insulators, salt, and other pollutants [68].l voltage, pollution level, humid-vals withoutmoisture so that theits hydrophobicity.us studies that there is a correla-urface conditions of non-ceramicel of LC is predictable it will beegradation of SIR insulators. Arti-een employed in several studiesssication of LC waveforms mea-n investigated [3]. An ANN hasinto four classes, based on the

A.H. El-Hag et al. / Electric Power Systems Research 78 (2008) 16861692 1687

mentioned that the classication was accurate, no comment hasbeen made about the percentage error that was encountered dur-ing the study. An approach to forecast the number and location ofthe faults caused by pollution ashovers in a 15kV overhead linehas been presented by [15]. By using the monthly rainfall values,a feed-forward ANN has been employed to predict the ashover.Although the accuracy of the prediction was satisfactory for mostof the cases, the error was more than 100% for certain cases.

All of the previous ANN based studies have been based on usingfeed-forward back-propagation because of its simple approach andgeneralization capability [3,1416]. In a previous paper, the authorsof this paper have introduced a prediction of the LC by implement-ing a feed-forward back-propagation ANN and a cascade forwardback-propagation ANN [12]. The number of layers of the networkhas also been changed to minimize the error of prediction. Theobjective was to predict the level of the LC after 10h knowing theinitial value of the LC and its initial slope during for the rst hourduring the salt-fog test. The feed-forward back-propagation ANNwith two hidden layers was found to be a reliable tool to fore-cast the LC. The error of the prediction was around 12% and moreimportantly, it is limited to the prediction of LC of a specic type ofinsulators in a specic time frame (10h) which may not represent

the starting time of surface degradation. It may bemore realistic topredict the level of LC at the end of the early aging period and notrestrict it to a certain time frame. The end of the early aging periodrepresent the period at which the SIR insulator loses its hydropho-bicity and degradation due to dry-band arcing is likely to happen.

In this paper, the potential of ANN to predict the nal value ofthe LC of SIR insulators at the end of the early aging period based onprimarily monitored data is investigated. In addition, the networkis generalized to include different types of distribution class SIRinsulators.

2. Problem formulation

The initial development of the LC is dependent on the type of theinsulator and the test parameters [13]. In some cases, the devel-opment begins immediately after the voltage is applied. However,in several cases, there is no signicant current even after 80h. TheLC can be described by three distinct periods during the salt-fogtest; i.e., the early aging period (EAP), the transition period, andthe nal aging period. Typical time domain waveforms of LC andtheir frequency spectrumat these three stages aredepicted in Fig. 1.The level of LC current distortion is highly correlated with arcing

Fig. 1. Typical time and freque and (cncy snap shot of LC, (a) during the early aging period, (b) during transition period ) during the late aging period.

1688 A.H. El-Hag et al. / Electric Power Systems Research 78 (2008) 16861692

Fig. 2. The fundamental component of FFT of LC of a 15kV silicone rubber insulatorduring the salt fog test.

Fig. 3. TheEAPof the LCwithparameters uof the LC every 10min and S is the slope o

on the insulator surface [2]. So itLC frequency components duringmonitored fundamental componetrated in Fig. 2. Because of the fasis better to show it by moving avestrates the zoomedEAPof the LC, atechniquewithawindowsizeof 2in therst 10min, and S1 is the aveLC during the same interval. Thirtfrom the monitored current durinof the paper is to predict the leveThis timevaries fromonecase toamately 500min in the case shownother cases. Table 1 shows a sampdata of a 15kV SIR insulator with

Distinct differences can be seetion period. One such difference irespect to the time. The LC is exof change at the EAP compared tdemonstrated in Fig. 4 where an ais noticed as the LC approachesobservation is consistent for all tEAP is dened as the time at whrespect to time (di/dt), reaches aSome typical values of di/dt, If an

3. Experimental setup

Forty-eight distribution classent voltage ratings were tested inLC. Details of the tested insulator

Table 1A sample of measured input/output data for a 15kV SIR insulator

Time interval (min) Input ISi (mA) Input Si =di/dt (mA/min) Output If (mA)

010 0.1805 0.00011020 0.1808 0.0003 1.152030 0.1816 0.00073040 0.1828 0.00134050 0.1844 0.00165060 0.1864 0.00196070 0.1885 0.00227080 0.1907 0.00218090 0.1932 0.002590100 0.1963 0.0031

100110 0.1999 0.0036110120 0.2041 0.0042120130 0.2083 0.0042130140 0.2119 0.0037140150 0.2156 0.0037150160 0.2199 0.0044160170 0.2255 0.0055170180 0.2323 0.0068180190 0.2399 0.0077

0.0100

is illustrated in Fig. 5. The chamber ise dimensions of 1.8m1.8m1.8m.wooden, and the walls and the ceil-acrylic sheets that are bolted to theuts. The bottom of the chamber con-ets and is 0.8m above the oor. Also,the center of the chamber to allow theles, fabricated according to IEC 60507,of the chamber. The saltwater ow

at the end of EAP

Tf (min) di/dt at t = Tf (mA/min)

1250 0.035720 0.025

4500 0.0382540 0.0213150 0.053

rubber insulators

Creepage distance,(mm)

Number ofinsulators

350 8160 18280 22sed for prediction,where IS is the averagef the current.

is quite important to register thethe salt-fog test. A sample of thent of LC of a SIR insulator is illus-t and random variations of LC, itrage technique [2]. Fig. 3 demon-fter applying themoving averageh. Is1 is the averagevalueof the LCrageof the increasing slopeof they pairs of data (ISi, Si) are derivedg the rst 300min. The objectivel of the LC, If, at the end of EAP, Tf.nother. For example,Tf is approxi-in Fig. 3 and exceeds 80h in somele of measured input and outputcreepage distance of 350mm.n between the EAP and the transi-

190200 0.2500

schematic of the test setupcubical with the approximatThe frame of the chamber ising are made of 6mm thickframe with nylon bolts and nsists of 12mm thick PVC shethe bottom is sloped towardswater to drain. Salt-fog nozzare mounted at each corner

Table 2Typical LC parameters of insulators

Insulator If (mA)

1 0.462 0.943 1.154 0.625 1.34

Table 3Specications of the tested silicone

Type Nominal voltage(line to line, kV)

A 15B 10C 17s the rate of change of the LC withpected to exhibit a very low rateo the other aging periods. This isbrupt change in the LC derivativethe transition aging period. Thishe tested cases. In this study, theich the derivative of the LC, withvalue higher than 0.01mA/min.

d Tf are summarized in Table 2.

SIR insulators from three differ-a salt-fog chamber to study the

s are mentioned in Table 3 and aFig. 4. Derivation of the LC of Fig. 1, whereof EAP and transition period.a jump in di/dt determines the boundary

A.H. El-Hag et al. / Electric Power Systems Research 78 (2008) 16861692 1689

rate was 1.5 L/min with the air pressure of 0.35MPa and the waterconductivity was selected to be 0.25 S/m.

A 15kVA single-phase distribution transformer (480V/16kV)with leakage reactance of 4% was designated as the high voltagesource. The measurement instrumentation consisted of a NationalInstruments TM PCI 6110E data acquisition card, three shunt 100resistors to measure the LC of three insulators and one resistordivider (1000:1) to monitor the applied voltage.

4. Articial neural network and training methods

In a previous study conducted by the authors, a simple feed-forward ANN with two hidden layers was selected to predict thevalue of the fundamental component of LC after 10h, knowingthe LC average values of the rst hour [12]. It has been shownthat the ANN scheme is capable of predicting the level of the LCwithin 12% accuracy, compared with that of the actual measuredtest results. The method was limited to predict the LC of only onetype of insulators. So, in this paper, the work has been extended toincludemore insulators to improve the forecasting accuracy. After acareful study and investigation, the Gaussian radial basis function(GRBF) was selected for this application [1821]. In order to usethese networks, a modication has been done by using a new errorfunction and an improved gradient equation for weight modifyingpurposes.

4.1. Gaussian radial basis function network

Of all the existing forecasting and prediction ANN methods,GRBFhavebeenemployedbecauseof its capability togeneralize theprediction for all cases [1419]. TheGRBFhas a simple topology andis fast in learning. Theoretical and experimental analyses indicatethat GRBF can approximate any functions and identify nonlinearsystems [1821]. As depicted in Fig. 6, the GRBF network consists ofthree ordered layers, i.e. input layer, hidden layer and output layer.The input nodes of the GRBF network pass the input values to the

Fig. 6. Schematic of the GRBF network used in the study.

connecting arcs, and the rst layer connections are not weighted.Therefore, each hidden node receives each input value unaltered.The output layer is one neuron. The idea of theGRBF network is thatany function canbe approximated by an interpolation composedbythe sum of N Gaussian functions. The Gaussian function shown inFig. 6 is given by:

fj(xi) = exp(

12

Ni=1

(xi Cij)22

ij

)(1)

where Cij and ij are the center and variance of the Gaussian radialbasis function network, respectively. The use of the Gaussian func-tion allows the local characteristics to facilitate the training andimprove the function generalization. The principle advantage ofthe technique is that the network has only one hidden layer. TheFig. 5. Schematic of the salt-fog chamber test.

1690 A.H. El-Hag et al. / Electric Power Systems Research 78 (2008) 16861692

Fig. 7. Flow chart of the random optimweights.

normalized network output is giv

Ifn =60

i=1Wifi(v)60i=1fi(v)

Two ANN training methods hafundamental component of thework: amodied back-propagatiooptimization method.

4.2. Back-propagation training me

The back-propagation trainingGaussian centers (Cij), and variandient descendant equations:

Wi = WJ

wi,

Cij = CJ

Cij

ij = CJ

ij

where J= (If Ifn)/2, If is the target (monitored data), and Ifn is thenetwork output. Each parameter has a proper learning rate, .

A developed error function is employed to increase the speedof the convergence instead of the conventional error function, asfollows:

e = 60i=1

log(1 (Ifi Ifni)2) (6)

This logarithmic error function reduces the convergence time con-siderably, and also compels a given network to learnmore complextasks, compared to the standard error function, without increasingnetwork size.

4.3. Random optimization training method

A random search technique is employed to train the GRBF. Thistechnique is guaranteed to converge to the global minimum errorvalue. Also, the technique has a higher rate of convergence than

[20]. The idea of the algorithm is too the weights of the net, and computerget output error is less than the lastpt, until the desired error is obtained.timization and to uniformly cover theconsists of the arrangement of centerscomputed as follows:

(7)

e, isianceneurptimhe dafutuonst

f the

ectedinsuGRBization training method, adjusting the

en by:

that of the back propagationadd a white noise sequence tthe network output. If the taerror, the new weights are keFor training with random opdata input space a techniquein a regular trellis and to be

= exp(

2

82

)InEq. (7), is the covering ratbor centers, and is the varthe reception of the outputFig. 7 exhibits the random othis study. The rst value of tprediction quality after eachthe number of trained data c

5. Results and discussion o

The data of randomly seltraining and the 5 remainingworks both feed forward and(2)

ve been developed to predict theLC by employing the GRBF net-n training technique and random

thod

method adjusts theweights (Wi),ces (ij) using the following gra-

(3)

(4)

(5)

several times. Table 4 shows satrained network with the real Ltable that the GRBF demonstratesforward ANN. This was consistehence the GRBF is only considered

In order to nd a proper trpropagation and the random optsystemically study the possibilitydiction, the trained data of each ito predict the LC of each group, onall the insulators are used to predwell. In each group of insulators,

Table 4Samples of the predicted values of LC usin

Item Insulatortype

Measured If(mA, RMS)

Prfo

1 A 0.46 0.2 A 1.34 1.3 B 0.88 0.4 B 1.08 0.5 C 0.91 0.thedistancebetween twoneigh-of the GRBF. In order to increaseons, normalization is employed.ization algorithm, employed forta array is omitted to improve there prediction value, and to keepant.

results

43 insulators are used for ANNlators are utilized to test the net-F. This process has been repeatedmples of the output data of theC values. It is evident from thisa better accuracy than the feed-

nt with all the tested cases andfor further analysis in this study.aining method, both the back-imizing method were tested. Toof using the GRBF in the LC pre-

nsulator type (A, B, and C) is usede by one. Also, themixed data forict the LC of mixed insulators asone insulator was used as a test

g feed-forward and GRBF networks

edicted LC, feedrward (mA, RMS)

Predicted LC GRBF(mA, RMS)

35 0.4153 1.4177 0.88 0.9681 0.85

A.H. El-Hag et al. / Electric Power Systems Research 78 (2008) 16861692 1691

Fig. 8. Error functions of the random optimizing method and back-propagationtraining method for 15kV polymeric insulators using GRBF.

case and the rest as the training data. This process is repeated 10times for each group of insulators, and the average of the results ispresented in Table 5. Except for one case (item 8), it is evident fromTable 5 that the error of the random optimizing training is muchless than the error of the back propagation. In the case of items 1,5, and 9 of Table 5, for which f the network is trained and tested bythe same type of insulator data, the average error of prediction islower than using different types of insulators. For example, consid-ering type-C insulator, if the same type of insulator is used both fortraining and testing, the average error of prediction error is 3.5%.

However, the use of certain data of a specic insulator to pre-dict the LC of other types resulted in errors as high as 33.1% withthe randomoptimizingmethod, and approximately 50%with back-propagationmethod. If the data of different insulator ismixed, such

Table 5Prediction error for different combinations of data using GRBF with back-propagation and optimization methods

Item Traindata

Testdata

Numberof runs

Error %, backpropagation

Error %, randomoptimizing

1 A A 10 28.4 5.32 A B 10 33.6 14.63 A C 10 50.4 24.34 B A 10 19.0 9.85 B B 10 12.9 4.26 B C 10 40.5 33.17 C A 10 26.1 15.78 C B 10 18.4 20.49 C C 10 10.5 3.5

10 Mixed Mixed 20 38.3 18.9

Fig. 9. Error functions of the random otraining method for two-shed insulators (

Fig. 10. Sensitivity analysis of the output error versus number of input sets.

as item 10 of Table 5, the errorrandom optimization technique.is that when insulator type C is mprediction error is higher compaand B together as shown in Tablehas the lowest prediction error wand test data (item 9 in Table 5).that insulator C has the largest nuand hence when mixed with othsamples and because each insulatmance in salt-fog, type Cwill haveof other insulators. However, thisples of type C will have a positivtest the same type. As a result, ittype of insulator to train the netwtype of SIR insulator, as the predicaccurate prediction is desired, forprediction technique can be emplof insulator for training and pred

Figs. 8 and 9 present a compartwo learning techniques for insultively. Although the training timlonger compared with that of therror function of random optimiback-propagation technique. Sevnumber of iterations should be bethe minimum training error. Evidnetwork is a reliable network andthe EAP of SIR insulators.

Finally, a series of simulationssitivity of the output with changFig. 10 summarizes the result of s

le 3redi

d ranivityf less

ed assubsthebefoe emfy threcognize the surface degradation inptimizing method and back-propagationtype-C) data using GRBF.

insulators introduced in Tabbeen used for training and pof Table 5. GRBF network anbeen employed in this sensitthat to achieve an accuracy oare needed for the training.

This technique can be ussystem of overhead lines orLC of polymeric insulators incan be used to prevent faultsMoreover, this method can bvious studies [2,16] to classicomponents of the LC and todecreases to 18.9% by using theAnother interesting observationixed with either type A or B thered to mixing insulators type A5. On the other hand, insulator Chen it is used in both the trainingThis can be attributed to the factmber of insulators (22 samples)

er insulators as a test or trainingor type has its own aging perfor-signicant effect on the accuracyrelatively large number of sam-

e impact when used to train andis not recommended to use oneork and predict the LC of anothertion error is large. If a reasonablyexample an error below 10%, thisoyed only by using the same typeiction.ison of the error function for theators type-A and type-C, respec-e for the random optimizing ise back propagation, the trainingzing is less than the error of theeral simulations prove that thetween 1200 and 1500 to acquireently, the proposed GRBF neuralcan be used to predict the LC of

were conducted to study the sen-e in the number of input points.imulation for the three different. The same type of insulator hasction similar to items 1, 5 and 9dom optimization technique hasanalysis. It is evident from Fig. 10than 10%, around 25 sets of data

a part of an on-line monitoringtations to predict the level of theEAP. A surface degradation alarmre serious surface damage occur.ployed to complement the pre-

e magnitude of the fundamental

1692 A.H. El-Hag et al. / Electric Power Systems Research 78 (2008) 16861692

salt-fog test to reduce the test timing. Although there is enoughinformation on the correlation between the LC and surface degra-dation in salt-fog tests, more studies on the correlation of the LCmagnitude and the degree of degradation should be conducted oncomposite insulators of overhead lines during eld conditions.

6. Conclusions

The initial value of leakage current and the slope for the LC ver-sus time curve at each 10min during the rst 5h of salt-fog test areadopted as the input to twodifferent ANNs to predict the nal valueof the LC of EAP. Simulations conrm that the error of predictionof the feed-forward network is higher than that of the GRBF. More-over, of the two learning methods used in this study, the randomoptimizing method results in a lower error of prediction.

The proposed ANN scheme is capable of predicting the level ofthe LC in the early stages of the LC development with a reasonableaccuracy. As a result, the developed network can be used as part ofa monitoring system to predict the magnitude of the LC at the EAPof polymeric insulators installed at substations and overhead lines.

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Prediction of leakage current of non-ceramic insulators in early aging periodIntroductionProblem formulationExperimental setupArtificial neural network and training methodsGaussian radial basis function networkBack-propagation training methodRandom optimization training method

Results and discussion of the resultsConclusionsReferences