ARTIFICIAL NEURAL NETWORKS FOR SINGLE PHASE FAULT

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ARTIFICIAL NEURAL NETWORKS FOR SINGLE PHASE FAULTDETECTION IN RESONANT GROUNDED POWER DISTRIBUTIONSYSTEMSYasmine ASSEF Patrick BASTARD Michel MEUNIER

ECOLE SUPERIEURE D~ELE CTFU CI T~SUPELEC)Service Electrotechnique et Electronique Industrielle Gif-Sur-Yvette. FRANCE

Abstract-This paper presents a new protectionalgorithm for the detection of single phase-to-ground faults in a power distribution network withcompensated neutral grounding. This algorithmbased on the comparison of the residual current andphase currents uses artificial neural networks. Thismethod is tested with EMTP simulations as well aswith experimental data which are closer to reality.Hence the efficiency of the algorithm is shown.Key Words-Power distr ibution system Resonantgrounded system Relay Artificial neural networkMultilayer perceptron.

I. INTRODUCTIONA power system relaying device must detect a fault anddisconnect rapidly the faulted element. As long as the fault is

not cleared, the risk of equipment damage and supplyinterruption may be important. The fast growth of powersystem complexity may make traditional algorithms insomeways inadequate. Hence new data processing techniquesare becoming indispensable tools for the development ofrelaying devices. The artificial neural networks ANN)are oneof these techniques; they have already proven their efficiencyfor prediction, control and classification problems [6, 10, 111.A protection system may be basically considered as aclassifier; it must be able to make the difference betweenfaulty and sound situations. In the litterature somepublications can be found describing the use of ANN in faultdetection. Among these [15,16] deal with high speed proteciverelays on transmission lines using Multilayer FeedForward

0-7803-3522-8196 5.00 1996 IEEE

Neural Networks that seem the most well adapted among otherANN for classification problems.This paper deals with resonant grounded power systems.The advantages of compensated networks are quite wellknown. Most faults in this kind of power distribution systemsare self-extinguishing single-phase to ground faults. The smallfault currents that they induce allow the network to remain inoperation but make their detection more difficult [8,9]. Hencethe protection system must analyse transient state caused bythese events which contain meaningful information [1, 21.When a low resistance fault occurs, the zero-sequencecapacitors are loaded through the fault resistance. During thisphase, active energy coming from the faulty feeder passestowards the sound feeder. The direction of the energy flow atthe beginning of the fault may therefore be used to detect thefault on the line (upline or downline). This is the base ofwattmetric processes. This hn d of procedures are efficientwhen the fault resistance is quite small. For a largerresistance, the zero-sequence voltage and the residual currrentare not very important and the sensitivity limit is rapidlyreached. A new process called DeSIR (Detection Selective parles Intensites Residuelles) was developped to detect such highresistance faults [3].The protection algorithm described in this paper is basedonly on the measurement of the 3 phase currents; voltages andcurrents of other feeders no longer need to be measured. Thisis the main particularity of this work. Moreover , theimplementation of neural networks in the protection devicemakes the use of a threshold unecessary; this point will bedetailed in sectionIII.

11. THE COMPENSATED POWER DISTRIBUTIONNETWORKA . Description

In a compensated power distribution network, a Petersencoil is connected between the neutral of the power system andthe ground. The reactance of the Petersen coil is tuned forresonance with the zero-sequence capacitance of the system.Fig.1 shows a 20 kV power distribution system with asingle phase fault in the first feeder. This network is simulatedwith the ElectroMagnetic Transients Program (EMTP). Thesingle phase fault is characterized with its resistance Rd, its566


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distance to the busbar d and the phase of the feeding voltage.The grounding system is defined by an inductance Xn and aparallel resistance Rn. The total length of the feeder is L.

1 5

1

0 5E

ai336

1

1 6

Fig.1. Power Distribution System Simulated with EMTP.CT represents three phas e current transformers; magnetic saturation maydistortsignals.After a fault, three phenomena take place one after another:the first is the discharge of the faulty phase capacitance due tothe falling to zero of the faulty phase voltage (Fig.4) throughthe equivalent inductance of the network, the second is thecharge of the sound phase capacitancesby the fault current andthe third is the circulation of the current in the neutralimpedance. Let us suppose that the faulty phase is the phasea, the first phenomenon induces high frequency componenttransients in the current of phase a of the faulty feeder as wellas in the current of phase a of the sound feeder. This is shownin the following figures.Fig. 2 shows the three phase currents ( I , , Z b Zc ) and theresidual current Z = Z, b , ) in the faulty feeder where afault occurs in phase a with Rd =7R at 5km rom the busbar.

,

/\h

b

250 1

I0.02 0.04 0.06 0.08 0.1 0.12 0.1450: Ume(s)Fig. 2. Currents in the Faulty Feeder.Phase aPhase b and c _._._.ReSldUal( - - - )

Any fault in a feeder may induce in the other feedersconnected at the same busbar a capacitive current flow.This isshown by Fig. 3 that presents phase and residual currents

measured on one of the sound feeders during the fault describedabove.7 I

J0.02 0.04 0.06 0.08 0.1 0.12 0.14time(s)Fig. 3. Currents in the Sound Feeder.

Phase a -)Residual - - - )Phase b and c )

4 ob2 O M O b s aba 0.1 . ;2 0;4Fig.4. F au lt zg ke Voltage.Fig. 5. shows the circulation of the faulted current in thepower network after the fault occurence.

RV MVPhase 3Phase2Phase 1

Fig. 5. Circulation of Currents.Cd is the zero-sequence capacitance of the faulty feeder. C, is the zero-sequence capacitance of the sound feeder.567


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B. Simulation of the Set of Electrical EventsAs explained before, Fig. 1 was simulated with EMTP.The protection device of a feeder has to face at least thefollowing events:- fault in another feeder,- energization of the feeder,- fault in the feeder.Obviously, the relay has to trip only in the last case.Lines are simulated with the frequency independentDistributed-Parameter Transmission Line Model of theEMTP. Some details in EMTP input data are given inAppendix I.Loads are defined by an inductance and a resistancein parallel. Several single phase faults are simulated:permanent faults, self-extinguished faults and intermittent arcfaults.Other events like energization of a feeder with or withoutsaturation of the magnetic circuit of CT are also simulated.The simulation of the CT is based on the model in Fig. 6,where the leakage reactance is supposed to be negligiblecompared to the winding resistance Re of the currenttransformer [13]. Zch is the secondary burden of the CT. Themagnetizing inductance is represented by a type 96-element ofEMTP, computed from the geometric data of the CT and theHysteresis curve characterizing the magnetic material of thecore [14].

Re

Fig. 6 Current Transformer Model.We have simulated almost 260 faulty cases. The variousparameters considered are:- the phase of the feeding voltage cp) ,- the position of the fault ( d ),

- the fault resistance I Q I d 00Q ,- the neutral inductance ( Xn)completly or partially in- the length of feeders Li ).tune with the total capacitance of the system,

The simulation time interval is 300 ms The event alwaysbegins at 24 ms. The sampling frequency is I d Hz.One of the important points to consider is the saturation ofthe magnetic circuit of the current transformers. Indeed,measured currents may be distorted. Therefore, the residualcurrent computed from the 3 measured phase currents may befalse; and this point may disturb the reaction of the relay. Fig.7 presents phase and residual currents for an energization; as itis shown, currents have an important second harmoniccomponent due to the magnetic saturation of the 360 kVAdistribution transformers. This decreasing DC component of

the inrush currents saturates the CT and induces a falsecomputed residual current, whereas no fault occured.

(7a) Phase a. (7b) Phase b. (7c) Phase c0 ,

801 I(7d) Residual Current.Fig. 7. Feeder Energization Phase Currents seen by the saturated CT.Values are multiplied by the ratio of the CT.

The aim of the presented work is to exploit the similaritiesand differences between each phase current and the residualcurrent, to distinguish the faulty feeder. The scalar product ischosen as mean of comparison.

0 002 004 006 008 01 012 014time 5)

111. SCALAR PRODUCTA. Mathematical Remind er

A basic way to compare two signals is taking by theirscalar product. Let us consider two n-dimentional complexvectors x and y :x = X I ,. ,x n and y = ( y I , . n )

The scalar product {x. y ) is defined by:n

x. ) = xi.yi*i = l

where y is the conjuguate of yi568


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This product is maximum if x=y and zero if x and y areThe associated norm of a vector x is calculated as follows:

orthogonal.llxll= ( 2 )

By analogy, for time depending signals, the scalar productis given by [121:(SI.SZ)=p1t ) .4 ( t )dt (3)

and for sampled signals:t? 4)

B. Application to CurrentsIn order to measure similarities between the residual currentand each phase current, the scalar products of the associatedsampled signals are calculated using a 20 s moving datawindow:

threshold which could still permit the detection of highimpedance fault. Hence, the interest taken in the use of ANN.

o o l n

2W 400 800 1WO 1200samples(9a) Rd=30R.

Faulty Feeder -)Sound Feeder -.-.-.)141t?

( I j . r = c Z i t ) .Zr( t) At i E 1 , 2 , 3 }t t,and t2 - t l = 2 ms

Fig. 8 presents these values for a faulty feeder (3 curves)and a sound feeder (3 curves); the associated currents areformerly presented in Fig. 2and Fig. 3.

Ia 2W 400 600 800 1 msamples(9b) Rd=300R.

Faulty FeederSound Feeder -.-.-.)

-1001 I0 w 400 03 03 1 12M) 1400samplesFig.8. Scalar Products =?'a).

Faulty Feeder -)Sound Feeder -.-.-.)The similarity between the faulty phase and the residualcurrents is clearly shown by the rise of their scalar product atthe begining of the fault; we can easily recognize the faultyfeeder.Now, if the fault resistance Rd increases, the transients aremore damped. Fig. 9 shows the curves of the scalar productsobtained for larger fault resistances.We notice that waveforms are similar, but the more Rdrises, the more the curves of the faulty and sound feedersapproach one another. This reveals the difficulty of defining a

Fig .9. Scalar Products for Different Fault Resistance Values

IV. ARTIFICIAL NEURAL NETWORKSThe Neural Network employed is the Multi LayerPerceptron (MLP) [ 5 ] .After the training, the MLP is able to compute the rightoutput not only from the inputs of the training examples but

also from any unknown vectors. To obtain optimal results,the choice of the training example set is very important. Itmust be small enough to allow a fast convergence of weights,and big enough to represent correctly all the events whichmust be recognized by the MLP.A . Neural Network s architecture

The architecture of the MLP is also quite important for agood generalization. The MLP used in this study is made of 5perceptrons, with one hidden layer and one output; the latter is569


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equal to 1 if the feeder is faulty and -1 if it is sound. TheNN has three inputs which are calculated as follow:

The scalar products are scaled in order to have smallervalues. Every power network simulation allows to computethe inputs of the ANN for various position of the 20 msmoving data window [6].B. Training Phas e

The training set contains 55 different configurations of thenetwork, 51 cases of single phase faults and 4 ases of feederenergization. The data, sampled at 1 4 kHz were resampled atI O k H z . The training patterns of single phase faults aresimulated for the following values:

2R S Rd I30R2km I km0 1 cp 5360The length of the faulty feeder L is equal to I O km and thetotal lenght of the whole network is also fixed.The neural network is trained with the fast backpropagationlearning algorithm of Matlab [7]. Briefly, the backpropagationalgorithm is an iterative gradient algorithm designed tominimized the mean square error between the actual output of

the MLP and the desired output [ 5 ] .The number of learning loops required to make the sum-squared output error of the MLP smaller than 0.02 is about600 then the training is stopped. The evolution of this errorand that of the learning rate is shown in Fig. 10.

Trainingfor 609 Epochsio ioQo -6100 200 3 400 5W 00010-2

Epoch

1 I

Fig. 10.Sum -Squ ared Error and Learning Rate Evolu tions .

V . GENERALIZATION RESULTSThis following section describes the generalization phasewhich allows us to test the efficiency of our protectionalgorithm. First, we have used EMTP simulations to test theresponse of the MLP and after that we have employedexperimental data.

A. EMTP SimulationsThe generalization set contains permanent faults, selfextinguishing faults, intermittent arc faults, noisy signal,energization of the feeder with saturation of the CT andenergization without saturation of the CT.All of the former cases have never been seen before by theMLP. The following figures show some of the results; the

output of the MLP was multiplied by 100 in the figures.

OM OM OM OM 01 012 014 016beIs1(l la ) Rd=2fi L=lokm (p=160,neutral inductance c ompletly tunedwith total capacitance

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neutral induct ance under-tuned(90 )with total capacitance.

- I042 0M 0 N fl 01 012 011 011kn1(1 IC) Inte ant Arc Fault. (1 Id) En er giz at h of a Feeder.Fig.1 1. Generalization ResultsThe output of the relay of the faulty feeder is ploted with solid line andthat of the sound feeder with dashed line.

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Let us recall that the training set contains only permanentfault examples; nevertheless, the neural network recognizes aswell intermittent arc faults as it is shown in Fig. (1Oc).Besides, the MLP identifies correctly a feeder-energizationevent, even if the residual current is not equal to zero becauseof the saturation of the magnetic circuit of the phase currenttransformers. a-B, Pratical Experiments

It is interesting to see how the trained NN works withinputs which are not computed with EMTP. We have made atest model presented in Fig. 12. Actually, disruptive eventslike noises or unbalanced 3 phase loads are seen in theexperimental tests; one can say that these events may also besimulated with EMTP but we want to assure that the ANNhas not learned a particularity of EMTP simulations. This isan attempt to evaluate the effectiveness of the presented toolfor real world systems.

L22 v

3500VA=--mrdFig.12 .Test Bench

This kind of test bench has already been used for anotherprotection system developed in our laboratory. It allows toreproduce the voltages and currents which would be seen by arelay from the secondary of CT s and PT s. We also have toconsider the sampling frequency of the acquisition cardconnected to the PC. Three different frequencies are used:i = 5. I O 3 Hz and i = 2,5. O 3 Hz ; however, the EMTPsampling frequency is f = I O 4 H z ; therefore the inputs of theMLP are calculated by (4) and then multiplied byf A i E {1 , 2 } . Fig. 13shows some of the results in case offault occurence. The measured currents have been multipliedby the ratio of the CT which should be associated to our relay.The MLP recognizes as well the fault occurence, fordifferent frequencies. The saturation of the magnetic circuit ofthe CT is very well seen in the experimentation. For the timebeing, experimentation corresponds only to short

extinguishing arc faults and the fault resistance does not vary,it is always equal to zero; future works will deals with other-type of faults.

mdl OW d06 dM dM 0 OM OM a06 006 01wi(13a) f=2500Hz.

m0 001 0 M 0.03 0.M 0 E 0 06 dl 0W 0 W OSWI(13c) f=5000Hz.

(13b) f=2500Hz.

I

Fig. 13. Experimental Results.VI. SYNTHESIS OF RESULTS

Fig. 14 illustrates the presented method:

Network + 1I IFi g. 14. Design of he Proposed Method.

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The results show that in some cases, the output may fallbefore the end of the transient; but, it is important to noticethat one of the essential attributes of a relay is to operate onlyif there is a fault and not for any other power systemdisturbance (energization, fault in another feeder, ...). When afault occurs in one of the feeders of the power network, themost important task for the relay installed in the sound feedersis to recognize them as sound, even if the neighbouring faultdisturbs their input currents. This is the security of a relay.From this point of view, as the previous figures show, ouralgorithm has a correct performance.The ANN recognize very well the intermittent arc faults andthe self-extinguishing faults, even if they have not seen themduring the training phase. Also, the saturation of the magneticcircuit of the current transformers does not disturb the ANN.Experimental results are satisfactory. In the future, it willbe very interesting to validate the algorithm with recorded datain a real distribution network.

- - - -(lenghvsec)- 0.200 40.0 1 OE5 -0.100 31.6 i 1.0E5

VU. CONCLUSIONA new protection algorithm based on the measure ofsimilarity between the residual current and each phase current,associated to artificial neural networks has been presented.The novelty of this algorithm is that it works with a veryreduced number of inputs; indeed, it needs only the three phasecurrents of the feeder in which the associated relay is installed;whereas relays generally require currents as well as voltages.Moreover, the ANN as a decision helper, allows the algorithmto recognize small and high impedance faults.This principle has been validated using EMTP simulations

but also with experimental data. The results obtained werefound very promising.VIII. REFERENCES

0 Chaari, M. Meunier, A Recursive Wavelet Transform Analysisof Earth Fault Currents In Petersen-Coil-Protected Networks, inProceedings of the IEEE-SP International Symposium on Time-Frequency and Time Scale Analysis, Philadelphia, PA, USA October.I.oemans and J.C. Maun, Using the EMTP and the Omicron toDesign a Transients Based Digital Ground-Fault Relay for Isolatedor Compensated Network, in Proceedings of the InternationalConference on Power System Transients, IPST 95, Lisbon, Portugal,September 3-7 1995, pp. 270-275

25-28 199 4, pp. 1 62-165.

D. Griffel, Y. Harmand , J. Bergeal, M. ClBment, Nou vellesTechniques de mise la Terre des Neutres sur les rBseaux MT,Revue Ginirale d Electriciti (RGE), no 11/94, December 1994, pp.34-44.

[6] H.R6gal, P. Bertrand, P. Bastard, M. Meunier, A Neural NetworkClassifier for the Analysis of Power Transformer DifferentialCurrent, in Proceedings of the International Conference onIntelligent Systems Application tu Power Systems, ISAP 94, Vol. 2,5-9 Septembre 19 94, Monpellier, France, pp. 67 3-680.

1-71 Neural Network s TOOLBOX for Use with MA TLAB TM , User'sGuide.[SI W . Petersen, Limitation of Earth Current and Suppression of EarthFault Arcs by the Earthing Coil, Elektrotechnische Zeitschrif, Vol.[9] L. Berthet, R. Meunier, Maintient de la Foum iture pendant unD6faut Monophas6 MT: experience acquise au Poste deGuebwiller, in Proceedings of he Intematiun Symposium NMT 95,Mulhouse, France, 7-8 Novem ber 1995, pp. 46-51.[lo ] D.J. Sobajic, Y.H. Pao, Artificial Neural-Net based Dy nam icSecurity Assessment for Electric Power Systems, IEEE Trans.Power Systems, Vo1.4, no. 4, February 89, pp. 22 0-228 .[ l l ] V. Bohm, V. Passeltova, M. Navak, E. Pelikan, Neural NetworkPredincting System for Electric Load in West Bohemia , inProceedings of the International Conference on Intelligent SystemsApplication to Power Systems, ISAP 94, Vol. 2, 5-9 Septembre 1994,Monpellier,France, pp. 857-864.[121 F. de Coulon, Thiorie et Traitement des Signum, Trait6 d Electricit.4,Vol. VI, Presses Polytechniques Romandes, p. 47.[13] Power Systems Relaying Committee of the PES, Transient Responseof Current Transformers ; Ieee Inc; New York; January 1976.[14] P.Bastard, SynthBse et validation d'algorithmes pour protectionsnumkrique d'un reseau moyenne tension , Ph. D Thesis, Universityof Paris XI (Orsay), June 1992, pp. 50-58.[151 T.S.Sidhu, H. Singh, M.S. Sachdev, Design, Implementation andTesting of An Artificial Neural Network Based Fault DirectionDiscriminator for Protecting Transmission Lines , IEEE Trans. onPower Delivery, Vol. 10, No.2, April 1995, pp.697-7%.[I61 T. Dalstein, B. Kulicke, Neural Network Approach to FaultClassification for High Speed Protective Relaying , IEEE Trans. onPower Delivery, Vol. 10, No.2, April 1995, pp. 1002-1011.

40, January 2 and 9, 1919 , pp. 5-7 and 17-19.

APPENDIX I. EMTP INPUT DATADistribution Line Model (lumped resistive modelling, transposed line)

Resistance (Wlenght) 1 Surge Impedance(i2) I Propagation Velocitv I

IX. IOGRAPHIESYasmine ASSEF was bom in Tehran, IRAN, in 1970. She graduated fromthe &ole Sp6ciale des Travaux Publics, du BStiment et de 'Industrie, Paris,France, in 1993. Since 1994, she is working towards a Ph.D. Degree inElectrical Engineering from the University of Paris VI at the EcoleSuperieure d'Electricit6, Gif-sur-Yvette, France.Patrick BASTARD was born in Pont-Audemer. FRANCE. in 1966. Hegraduated from th e ficole Superieure d'Elec tricit&, Gif-sur-Y vette, France,in 1988. In 1992, he received a Ph. D. Degree in electrical engineeringfrom the uniyersity of paris XI. H~ is now a researcher in the ficolesuperieure &Electricit&,Michel MEUNIER was born in Merdrignac, FRANCE, in 1945. He

P. Ferracci, L. Berthet, M. Meunier, An Equivalent Circuit forEarth Fault T ransient Analysis in Resonant-Grounded DistributionPower Networks, in Proceedings of the International Conferenceon Power System Transients, IPST 95, Lisbon, Portugal, September3-7 1995, pp. 359-369. graduated from th e Ecole Supkrieure d'Electricit6, Gif-sur-Yvette, France,in 1968. He has been working in the &ole Supdrieure d'Electricit6 since1968. He is presently Professor at the fico le Supbrieure d'Electricit6 wherehe manages a research group on Rower networks. He is a member of theS.E.E. (Societe des Electriciens et Electroniciens).

_R.P. LippAa m, An Introduction to Computing with Neural Nets,IEEE ASSP Maaazine. A~ r i l1 987 . D . -22.

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ARTIFICIAL NEURAL NETWORKS FOR SINGLE PHASE FAULT

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