a firann as a differential relay for three phase.pdf

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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 16, NO. 2, APRIL 2001 2

A FIRANN as a Differential Relay for Three PhaPower Transformer Protection

ngel L. Orille-Fernndez , Member, IEEE , Nabil Khalil I. Ghonaim, and Jaime A. Valencia

Abstract This paper presents an application of a Finite Im-pulse Response Artificial Neural Network (FIRANN) as a differ-ential protection for a real three phase power transformer. ThreeFIRANNs are designed, trained and tested. The first one has anoutput, which identify internal faults from any other cases like in-rush current and external faults. The two others FIRANNs, eachhave twooutputs that classify between internaland external faults,so that, a backup protection is included. These FIRANNs have sixinputs, one for each sampled current signal from both transformersides. The sample rate selected is 2 kHz for a 50 Hz power fre-quency. All FIRANNs were trained to have a 3.5 ms fault detectiontime, which is considered as a very fast protection. The test resultsshow very good behavior of the FIRANN as a differential protec-tion and it is planned to build a prototype.

Index Terms Artificial neural network, digital relays, trans-former protection.

I. INTRODUCTION

THE FIRST useof artificial neuralnetworks (ANN) appliedon power transformer differential protection was in 1994[1]. Since that time there are really few papers suggested the ap-plication of ANN on power transformer protection and there isa lot of work to do in this subject. The uses of ANN on trans-mission line protection have been given more consideration.

There are different ways how ANN can be applied to a differ-

ential transformer protection. The first investigations suggestedthe use of ANN as an inrush current identifier to be includedas a part of a differential protection. In [1], it were proposedto use a Time Delay Artificial Neural Network (TDANN) toprocess the normalized sampled current signals. Another group[2], suggested the use of the DFT (discrete Fourier transform)to filter the fundamental component and theharmonics from thesecond order through fifth order of the current signals and applythem to a Multilayer Feed Forward Artificial Neural Network(MFANN).

A recentpaper kept thesame idea andsuggestedan additionalMFANN to reconstruct the distorted secondary current signalof the current transformer due to saturation in order to improveoperation of the power transformer protection [3]. Later papersproposed MFANN as a transformer differential protection, onesuggested to use harmonic ratios as inputs [4]; while the otherused the negative sequence component of current signals andthe voltage signals as inputs of the neural network [5].

Manuscript received February 7, 2000.. L. Orille-Fernndez and N. K. I. Ghonaim are with the Electrical Engi-

neering Department, Polytechnic University of Catalonia, Barcelona, Spain.J. A. Valencia is with the Electrical Engineering Department, University of

Antioquia, Colombia.Publisher Item Identifier S 0885-8977(01)03412-4.

Fig. 1. Three-phase transformer circuit scheme.

Thebehaviorof FIRANN (Finite ImpulseResponseArtificiaNeural Network)applied to a transformerdifferential protectionis presented in this paper. This neural network is well known fits ability to manage time variable signals [6]. Three differentFIRANNs were trained as differential protection for a threephase power transformer. The first one has an output to classifthe internal faults from any other case. The other two FIRANhavetwo outputs toclassifybetween internal andexternalfaultsThis, in fact, is a novel approach to differential protection thaimprovesselectivity of theproposedrelay andincludesa backupprotection for the other elements, which are connected directlto the power transformer.

The inputs of the FIRANNs are the normalized sampled curent signals from both sides of the transformer. It means that thFIRANNs acts as a signal processor based relay. The FIRANNstructure, training method, training strategy and test results wibe reported.

II. SIMULATEDSYSTEM

The system used in this work to generate the training patternof the FIRANN was a 15 kVA, 220 V/1300 V Yy solidlgrounded power transformer. It has six taps on every phaswinding of the high voltage side. The shortest segment betweetwo consecutive taps is 6% and the longest is 26% of thtotal coil turns. The method used to simulate internal faults iexplained in [7]. The Alternative Transient Program (ATP) wasused to simulate all cases needed in this work. The simulatiomodel was validated using real laboratory measurements.

The schemeof the system is shown in Fig. 1. The transformis modeled as coupled branches to simulate its coils wind-ings. Iron losses are simulated as three resistive branch Three nonlinear induction branches, , were added to consider the saturation effect for inrush simulation cases. The sampled current signals are taken from measurement switch typeso there was not included any current transformer model. Th

08858977/01$10.00 2001 IEEE

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216 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 16, NO. 2, APRIL 20

TABLE ITHREE-PHASETRANSFORMERSIMULATIONS

current sensors used on laboratory measurement were Hall ef-fect elements, which have lineal characteristic. The samplingfrequency used is 2 kHz.

III. SIMULATEDFAULTCASESOne of the most important stages during the designing of

ANN is the selection of the simulated cases that must be in-cluded in the training and testing sets. The transformer modelwas simulated to cover all possible operating and fault condi-

tions such as the source short circuit level, fault inception in-stants, fault types and inrush current. The simulated cases aredivided into three groups. The first is the training group and itspatterns areselected randomly andnormallydistributed in ordertomake theFIRANN togeneralize andtoprevent skewlearning.The second group is used to validate the FIRANN during thetraining process and the last one is the testing group.

Table I summarizes the cases simulated with the systemexplained above. Each row specifies a simulated case and eachcolumn designates a different parameter for each case. The IRset includes inrush cases; 8 different switching instants and27 initial conditions are combined in order to have 216 cases.The EB group has the external fault cases on source side; time

instants, loads and type of faults are combined. The EA rowincludes external fault cases on load side with equal parametercombination as in the previous group. Internal faults areclassified in two sets, the IT set that has turns to earth faults andthe IE set which has turn to turn faults. All the internal faultsare combined with the six taps in each high level side winding.

IV. THE ARTIFICIALNEURALNETWORKThe ANNs used in previous works were the MFANN and

TDANN. A neuron model based on Finite Impulse Response(FIR) filter theory is shown in Fig. 2. An ANN that has neuronsof this configuration is called the Finite Impulse Response Ar-

tificial Neural Network (FIRANN). This type of ANNs is sug-gested in [6] as good structure for temporal signal processing.The selection of the number of hidden layers, the neuron

number in every layer and the time delay on each neuron hasbeen done by trial and error method. The structure of a certainFIRANN depends on the protection functions that were tried toinclude in it. The number of total time delay is selected to havea detection time of 3.5 msec.

A. One Output FIRANN

The structure of this FIRANN is shown in Fig. 3. There are4 layers, the input layer has 6 inputs. The first hidden layerhas 6 neurons while the second hidden layer has 4 neurons. All

Fig. 2. Neuron model of a FIRANN.

Fig. 3. FIRANN structure with single output.

Fig. 4. FIRANN structure with 2 output (FIRANN-1 and FIRANN-2).

neurons have 2 time delay units. There is one output that hbeen trained to be 1 in case of internal faults and 1 in another case (i.e., inrush current cases, external faults and healthcases).

B. Two Output FIRANN

There are two FIRANNs, FIRANN-1 and FIRANN-2, bohave2 outputs. Both have thesame architecture, which is showon Fig. 4. There are 4 layers, the inputs are the same as in thfirst case. Each one of the two hidden layers have 8 neurons a2 time delay units per neuron.

These two FIRANNs has two outputs labeled internal fauland external fault. The difference between the two network

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ORILLEet al. : A FIRANN AS A DIFFERENTIAL RELAY FOR THREE PHASE POWER TRANSFORMER PROTECTION

TABLE IIOUTPUTDESIGN OFFIRANN-1

TABLE IIIOUTPUTDESIGN OFFIRANN-2

only exists in the designing of the outputs response. The firstoutput works as internal faults detector in both networks whilethesecondoutputdetects external faults in theforward directiononly in the case of the FIRANN-1. In the case of the FIRANN-2the second output detects external faults in both forward andbackward directions. This second output is very useful to in-crease the selectivity of the relay and make it works as backupprotection for the elementsconnected to the transformer such asthe transmission lines and generators.

The training patterns used to train both FIRANN-1 and FI-RANN-2 was included all types of internal and external shuntfaults except in the case of three-phase fault on source side. Inthis case transformer currents go to zero which looks like dis-

connecting the transformer and/or the source. Tables II and IIIshow output design for FIRANN-1 and FIRANN-2.

V. TRAININGMETHODThe training method used to train the FIRANN is called tem-

poralback-propagation [2]. A program based on temporalback-propagation was developed by authors to train theproposed net-works. This program was developed in C language and runsunder the Linux operative system.

The patternsused to train eachFIRANNisa subsetofall sim-ulated cases. This subset must be properly selected in order toinclude a representative of all cases and let the FIRANN to gen-

eralize. The one output FIRANN needed 882 simulated faultpatterns, the FIRANN-1 needed 630 and the FIRANN-2 used1260 fault patterns. Each simulated case consists of 87 samples,40 samples before fault inception and 47 samples after fault in-ception. Theideal valueof theFIRANN outputgoes from 1 to

1 in 3.5 msec exponentially to avoid false detection of faultsduring heavy transients and load swing. One of the training tar-gets is to search about the minimum training set, which is suf-ficient to make the FIRANN to generalize. Therefore, we usedless than 4% of all simulated cases as training data showing thegreat ability of this FIRANN to generalize.

The inputs of the FIRANN must be normalized and adjustedtohavethewaveforms of thetransformerprimary andsecondary

Fig. 5. Statistic results of the test.

current almost in phase and with the same magnitude. This wmake the FIRANN to generalize for different windings connetions, sizes and ratings.

The time consumed in the training cycle of these FIRANNwas about 26 hours in a PC Pentium II. Number of iterationneeded to get good results was in general less than 1500.

VI. TEST ANDRESULTS

The testof the FIRANNs isdoneusing a set ofpatterns, whichcompletely differs from thetrainingones.Fig. 5 summarizes tesstatistics.On training stage theFIRANNwastestedperiodicallyusing about 10% of the total simulated cases including fault annonfault events as a validation patterns. When it was considerethat training was over, a sample of 50% of total simulated caswas chosenforeach FIRANNin order to test itsbehavior. Thosesamples includedallkind ofeventanddidnot include casesusedon the training file.

We consider a tested case as Good when the FIRANN actual response is equal or faster than ideal one. The Short RetaSRmeans a delay less than2 msand LongRetardLRmeansa delay greater than 2 ms and less 10 ms. The Bad meansdelay more than 10 ms. Note that the SR and LR are causby light fault currents and the Bad is caused by very smafault currents.

The FIRANNs show Good behavior on more than 95% othe tested cases. The SR and LR classifications give more tha4.6%. Leaving BAD classification less than 0.4%. Also it canbenoticed that FIRANN-2 has less Good cases and greater retarcases than the others do. This difference is caused by the sourcside external faults, which make the transformer current to bless than the nominal values in both sides. In order to overcom

this problem, the training patterns that correspond to this casmust be increased and the FIRANN-2 must be trained again.Some test examples are shown in Figs. 68. The actual ne

work answer and input signals are displayed in the figures.Fig. 6 shows a typical behavior of the single output FIRANN

The diagram onupper left corner shows theactual answer of thFIRANN with marks. The other three diagrams show ththree-phase input current signals to the FIRANN.

Figs. 7 and 8 showthe novel approach toa differentialprotec-tion.Thefirstfigure case shows theresponseof thesingle outpuFIRANN-1 for an external fault while the second one shows thFIRANN-2 response for an internal fault. The IF and EF denothe internal fault and external fault outputs respectively.

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218 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 16, NO. 2, APRIL 20

Fig. 6. Single output FIRANN response.

Fig. 7. The FIRANN-1 response for a load side external fault.

Fig. 8. The FIRANN-2 response for internal fault.

VII. CONCLUSION

A new approach of power transformer differential protectionis proposed in this paper. The three FIRANNs performanceis very good. Their response to a fault case takes less than3.5 ms on more than 95% of the tested cases and less than10 ms in 4.5% of the tested cases. The prolonged delay is dueto very weak faults, which is a logic behavior of the FIRANNand could be improved by increasing the FIRANN size. Theresponse time of the proposed relays is inverse proportionalto the fault strength which makes them insensitive to loadswings and heavy transients. This demonstrates that the relaysare fast and reliable in general and more selective in the casesof FIRANN-1 and FIRANN-2 which can be used to backupother relays. The proposed relays can be generalized for all

connection types of power transformers by choosing a suitabcurrent normalization values in order to match the primarand secondary current magnitudes and to reduce the currentransformer errors. In addition a suitable method must bapplied to eliminate the phase shift between the primary anthe secondary currents in order to make the relay generafor all three phase transformer connections. A prototype othis FIRANNs will be created using a nonexpensive DigitSignal Processing (DSP) card. Finally it is clear that thesFIRANN had verified the four protection bases, i.e., reliabiliselectivity, speed and economy.

VIII. LIST SYMBOLSANN Artificial Neural NetworkFIRANN Finite Impulse Response Artificial Neura

NetworkMFANN Feed Forward Artificial Neural NetworkTDANN Time Delay Feed Forward Artificial Neura

Network

REFERENCES[1] L. G. Perez, A. J. Flechsig, J. L. Meador, and Z. Obradovic, Traini

an artificial neural network to discriminate between magnetizing inrusand internal faults,IEEE Trans. on Power Delivery , vol. 9, no. 1, pp.434441, Jan. 1994.

[2] M. Nagpal, M. S. Sachdev, K. Ning, and L. M. Wedephol, Usingneural network for transformer protection, inProc. of the InternationalConference on Energy, Management and Power Delivery , vol. 2, Singa-pore, Nov. 2123, 1995, pp. 674679.

[3] P. Pihler, B. Grcar, and D. Dolinar, Improved operation of power tranformer protection using artificialneural network, IEEE Trans. on Power Delivery , vol. 12, no. 3, pp. 11281136, July 1997.

[4] P. Bastard, M. Meunier, and H. Regal, Neural network-basedalgorithfor power transformer differential relays,IIE Proc.Gener. Transm. Distrib. , vol. 142, no. 4, pp. 386392, July 1995.

[5] A. L. Orille, N. Khalil, and J. A. Valencia, A transformer differetial protection based on finite impulse response artificial neural nework, in24th International Conf. on Computers & Industrial Engi-neering . Uxbridge, UK: Brunel University, Sept. 911, 1998.

[6] P. Bastard, P. Bertrand, and M. Meunier, A transformer model fwinding fault studies,IEEE Trans. on Power Delivery , vol. 9, no. 2,pp. 690699, Apr. 1994.

[7] S. Haykin,NEURAL NETWORKS A Comprehensive Foundation :Macmillan College Publishing Company, 1994, p. 510.

ngel L. Orille-Ferndezreceivedthe Dr. Ing. degree in electrical engineeringfrom Polytechnic University of Catalonia, Spain, in 1988. Since 1989, he is FProfessorof theDepartmentof ElectricalEngineeringof Polytechnic Universitof Catalonia and Head of this department since 1995.

Nabil Khalil I. Ghonaimwas born in Cairo, Egypt on October 1965. He re-ceived the B.Sc. degree with distinction first class honors and the M.Sc. degin electrical engineering from Helwan University, Cairo, Egypt, in 1987 an1993, respectively. From 1994 to the present, he has started his studies towathe Ph.D. degree from Polytechnic University of Catalonia, Barcelona, Spain the field of power system protection. His areas of interest are power systsimulation, planning and protection, signal processing and neural networks.

Jaime A. Valenciawas born in Medellin Colombia. Received the electrical engineer degree in 1982 from National University of Colombia and the M.Sc. dgree onmathematics in1988.He hasbeen TeacherAssistance atAntioquia Unversity since 1990 and a Ph.D. student at Polytechnic University of CatalonSpain since 1995.

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