ARTIFICIAL NEURAL NETWORK APPLICATIONS

FOR POWER SYSTEM PROTECTION

Gaganpreet ChawlaStudent Member, IEEE

Mohinder S. SachdevLife Fellow, IEEE

G. RamakrishnaMember, IEEE

Power System Research Group, University of Saskatchewan57 Campus Drive, Saskatoon, SK S7N 5A9 Canada

AbstractThe most commonly used systems for protecting transmission andsub transmission lines belong to the family of distance relays. Overthe past eighty years, successful designs based on electromechani-cal, solid-state and digital electronics technologies have been producedand marketed. These relays implement various characteristics, suchas impedance, offset-impedance, admittance, reactance and blinders.The Artificial Neural Network based designs of distance relays pro-posed so far work well for ideal fault conditions but are not able tomaintain the integrity of the boundaries of the relay characteristicsof generic designs. This paper reviews ANN models that have beenproposed in the past for protecting components of power systems andpresents a methodology that fully exploits the potential of ANNs in de-signing generic distance relays that retain the integrity of the bound-aries of their characteristics.

Keywords—Artificial Neural Networks; Distance Re-lays.

1 Introduction

Artificial neural network based technology, which is in-spired by biological neural networks, has developed rapidlyin the previous decade and has been applied in power sys-tem protection applications. Specific applications includedirection discrimination for protecting transmission lines[1-2], fault classification for faults on double circuit lines[3], ANN based distance relays [4], differential protection ofthree phase power transformers [7] and faults on generatorwindings [8]. The ANN based designs of generic protectionsystems proposed so far work well only for ideal fault condi-tions but do not maintain the integrity of the boundaries ofthe relay characteristics. This deficiency exists even if thenetworks are trained to identify the operating states in theneighborhood of the boundaries of the characteristics. Thisis because of the fact that none of the designs fully exploitthe potential of ANNs in implementing generic relay char-acteristics for maintaining the integrity of their boundaries.Investigations of the fundamental drawbacks of ANNs anddevelopment of new strategies for designing ANNs, whichwould work as generic distance relays with clearly definedoperating boundaries, are needed.This paper reviews a few ANN models that have been

proposed for protecting different components of power sys-tems, such as transmission lines, transformers and gener-ators. A methodology for the development of ANNs byanalyzing and utilizing the relationships between the inputdata and the outputs expected from the ANN is then pre-sented. The proposed methodology helps in fully utilizingthe potential of ANNs in implementing generic distance re-

lay characteristics in such a manner that the integrity ofthe boundaries of the relay characteristics is maintained.

2 Protecting Power System Components

2.1 Transmission Line Protection

One of the initial developments in application of ANNs forprotecting transmission lines was the design and implemen-tation of a fault direction discriminator [1]. A multi-layerfeed forward network with a 12-4-1 configuration was usedin this design. This ANN based directional relay used sam-pled values of voltages and currents, processed them to de-termine if a fault is on the line side of the relay or is onthe bus side of the relay. Patterns from all three phases(consisting of Va, Vb, Vc, and Ia, Ib, Ic) were used to trainthe network. The performance of the designed protectionsystem was tested by using data obtained from simulationsperformed on the EMTDC/PSCAD software package. Thiswas a significant development because it showed that itis possible to use ANNs in the designs of protection sys-tems. However, concerns were expressed about the use ofANN based systems in practical applications because theintegrity of their design for protecting parallel lines hadnot been checked. Also the design was not checked for dif-ferentiating between faults and major load changes.A few other ANN Models for protecting transmission

lines have been presented since then. A recent design usesa finite impulse response ANN (FIRANN) for detecting theonset of faults and determining the direction of the faulton high-voltage transmission lines [2]. The configuration ofthe proposed network is 45-35-5. Three of the five outputsof the network identified faults of the three phases (onefor each phase); the fourth output determined the direc-tion of the fault and the fifth output identified undercur-rent/undervoltage conditions. A total of 100,000 patterns(that comprised of voltages and currents of all three phasesand their sums) from different relays locations in a modeledsystem were used to train this network. This ANN designis rather complex as compared to the previously proposeddesigns. In this paper, one network provides five outputsbut does not clearly define the operations that take placeinside the network. Also, the reasons for using such a largenetwork and for using such a large number of patterns fortraining the network are not discussed. These essential butunaddressed issues lead to the uncertainty about the in-tegrity of the ANN when applied on a power system.

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2.2 Transformer Protection

Artificial Neural Networks have found their use in the pro-tection of power transformers as well. A time delay ar-tificial neural network processes the normalized values ofsamples of currents [5]. The Discrete Fourier transform fil-ters the fundamental frequency and harmonic componentsof the currents. The fundamental and harmonics of secondto fifth order are applied to a multi-layer feed forward net-work for implementing differential protection [6].A recent paper describes the use of a FIRANN as a differ-

ential relay for protecting three phase power transformers[7]. Two FIRANNs with delay units form the two networks.The first FIRANN with two hidden layers and a configu-ration of 6-6-4-1 detects the existence of a system fault.The second network, which has a configuration of 6-8-8-2,provides two outputs. One output indicates a fault in thetransformer protection zone and the second output indi-cates that that fault is outside the protection zone. Eventhough these two networks have different structures, sameinput patterns, which comprised of voltages and currentsfrom all the phases, were used to train the networks. Thechoice of the configurations of the networks and the numberof the patterns used for training the networks is not dis-cussed. The paper does not provide much insight in to theinternal processes of the used ANN. Moreover, the paperdoes not include enough evidence to show that the designednetwork works with adequate integrity in the neighborhoodof the boundary of the relay characteristics.

2.3 Generator Protection

The use of ANN based systems for protecting generatorshas not received much attention so far. A recent paperpresents the implementation of an ANN-based fault diag-nosis scheme for generator windings [8]. According to thispaper, the proposed network has the ability for detectingand classifying generator winding faults with higher sensi-tivity and stability boundaries as compared to conventionaldifferential relays in addition to the ability for identifyingthe faulted phases.This paper states that there is no way to determine the

best configuration for an ANN, therefore three networksare tried and the network that provides the best resultsis chosen. The first network has a configuration of 6-3-7,uses six samples of currents as inputs and provides sevenoutputs identifying phase to ground, two phase and threephase faults. The second design uses three networks witha configuration of 2-2-1; each network (one for each phase)uses two sets of inputs and provides three outputs. In thethird design, seven networks are used; a set of six inputs isapplied to each network that has a configuration of 6-3-1.Each network detects one type of fault.

2.4 Overview and Comments

The papers reviewed in Sections 2.1 to 2.3 use ANNs forprotection of different components of a power system. The

question of maintaining the integrity of the boundaries ofthe relay characteristics, however, is not addressed in them.If a trained ANN does not perform well, especially near theboundary of the desired characteristic, during the testingphase, then appropriate inputs have to be given to train itagain to improve its performance. At this point, it becomesextremely essential to understand the impact of the differ-ent types of inputs on the training of the ANN for obtainingthe desired results from them. Therefore, comprehension ofthe internal structure of an ANN is very important.

3 Proposed Methodology

A methodology that fully exploits the potential of ANNsand makes the whole process simple is presented in thispaper. In this methodology, the processes assigned to thedifferent layers of an ANN are segregated by dividing thenetwork into sub-networks; each sub-network is responsiblefor performing an assigned protection function. This pro-cess helps in better understanding the internal structure ofthe ANN and makes the process of modifying the networksimpler whenever required.As discussed previously in this paper, ANNs are associ-

ated with some acceptability issues. Analysis of an artificialneural network based fault direction discriminator [10] wasan attempt to address some of those issues. The acceptabil-ity issues arise because of undefined relationships betweenthe inputs and the outputs provided by an ANN.The proposed networks use the normal structure of an

ANN as shown in Figure 1. The inputs (Va, Vb, Vc and Ia,Ib, Ic in almost all the cases) are given to the input layerand the outputs are obtained from output layer.

Figure 1: Normal Structure of an ANN.

3.1 The Proposed Design

In this section, an ANN based methodology for protectingtransmission lines is presented. The proposed methodology

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can be used for designing ANN based systems for protect-ing other components of power systems. To develop a sys-tem that maintains the integrity of operation around theboundaries of the relay characteristics, modifications mustbe made to the inputs that are provided to the network. In-stead of providing the conventional inputs (Va, Vb, Vc andIa, Ib, Ic), inputs that assist in achieving the desired relaycharacteristics were used to train the network. This createda direct relationship between the inputs and the outputs ex-pected from the networks while maintaining the integrity ofoperation around the boundary of the relay characteristics.Figure 2 shows the characteristics of a mho (admittance)

relay. An ANN was designed and was trained to give +1output for faults which are in the protected zone of the re-lay (class P1) and -1 output for patterns of normal systemoperation (class P2). The network acted like a pattern clas-sifier and differentiated between two classes of patterns.The characteristic of an admittance relay, shown as an

example, was achieved by the developed design. A similarprocedure could be used to develop networks that wouldimplement other characteristics, such as offset mho, reac-tance and blinders. Figure 3 shows a modified model of

Figure 2: Characteristics of a mho (admittance) relay.

the ANN Structure used in the proposed development. Foreach phase, voltages and currents obtained from power sys-tem simulations were applied to the network as illustratedin figure 3. This structure segregated the whole networkinto 3 sub-networks, one sub-network for each phase. Thisapproach is similar to that used in a computer programwhich has a number of sub-routines instead of a singlelarge program. This methodology helped in assigning spe-cific functions to each sub-network by changing the internalstructure of the conventional ANN. Each sub-network wasresponsible for one protection function and it was not nec-essary to change the whole structure of the ANN for mak-ing changes to a protection function. Outputs from thesesub-networks were given to an output layer to obtain the

required results. Making the following three modifications

Figure 3. Representation for the proposed Model of theANN.

to this network, it became possible to identify all kinds offaults.a. One output was obtained from each sub-network.The

output from a sub-network identified faults for the assignedtype such as phase A, B or C fault. If during the testingstage, an ANN did not work well for faults of the assignedtype, that sub-network was modified or its training dataand procedure were evaluated and modified.b. A combination of these outputs detected phase to

phase faults. By using logic comparators in the next layer,phase to phase faults were detected. Outputs from the threeAND logic comparators were combined to detect A-B, B-Cand A-C faults.c. Combining the outputs from all three networks de-

tected three-phase faults. Instead of using neural layers asthe next stage, an AND neuron was used by fixing specificweights of the neuron.

3.2 Training the Proposed Network

The adopted structure of the network allows keeping acheck on all the operations taking place in an ANN. Also,simply by looking at the output of each sub-network, it ispossible to modify the inputs to be given to the ANN forits proper training.The training was conducted by back-propagating the er-

rors in such a manner that the ANN maintained the in-tegrity of operation around the boundary of the relay char-acteristics. The outputs obtained from all the layers of theANN were examined. This ensured that appropriate errorsare back-propagated for updating the weights of the ANN.The inputs used to train the ANN were suitable for de-

tecting faults in zone 1 only i.e. 80 % of the transmissionline. This approach will be adapted in the future work

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for designing ANN based systems for protecting zone-2 andzone-3 of the transmission system.

4 Sample Study

Figure 4: Sample waveforms for point A

Figure 5: Sample waveforms for point B

Figure 4 and 5 show sample waveforms of voltage and cur-rent for points A and B respectively in the relay character-istics shown in Figure 2. These inputs were used to test thetrained ANN. It can be seen from the relay characteristics,point A is just within the boundary region being protectedby the relay and point B is right outside the protection zoneof the relay; both the points having the same magnitude ofimpedance. In spite of a marginal distinction between thetwo set of waveforms, the trained ANN was able to differen-tiate between the faults within the protected zone from thepatterns of normal system operation. The output obtainedfrom the trained ANN when subjected to the first inputwas +1 (point A) and when subjected to second input was-1(point B). These results confirm that the adopted struc-ture of the ANN maintains the integrity of generic relaycharacteristics.

5 Conclusions

ANN based designs of relays proposed previously have beenexamined in this paper. A new methodology that fully ex-ploits the potential of an artificial neural network for its ap-plication to protect transmission lines has been presented.The proposed design provides a better understanding of theinternal structure of an ANN and makes it convenient tomodify the ANN during training. The conventional inputsthat are generally used to train ANNs have been modified

so that the integrity of the generic relay characteristics ismaintained. The proposed methodology is a general pur-pose approach that can be applied to design networks forprotecting other components of power system as well.

References

[1] H.Singh, M.S. Sachdev, T.S. Sidhu "Design, Imple-mentation and Testing of an Artificial Neural Net-work Based Fault Direction Discriminator for pro-tecting Transmission Lines," IEEE Transactions onPower Delivery , Vol. 10, No. 2, 1995, pp 697-706.

[2] A.L.O Fernandez, N.K.I Ghonaim, "A Novel Ap-proach using a FIRANN for Fault Detection andDirection Estimation for High Voltage Transmis-sion Lines," IEEE Transactions on Power Delivery,Vol. 17, No. 4, Oct 2002, pp 894-900.

[3] A. Bennett, A. T. Johns, Q. Y. Xuann , R.K Aggarwal,R. W. Dunn, "A Novel Classification Technique forDouble-circuit lines Based on Combined Unsuper-vised/Supervised Neural Network," IEEE Transac-tions on Power Delivery , Vol. 14, No. 4, 1999, pp1250-1255.

[4] B Balamurugan, R Venkatesan, "A Real-Time Hard-ware Fault Detector Using an Artificial NeuralNetwork for Distance Protection," IEEE Transac-tions on Power Delivery, Vol.16, No. 1, Jan 2001, pp75-82.

[5] A.J.Flechsig, J.L.Meador, L.G.Perez, Z.Obradovic,"Training an Artificial Neural Network to Dis-criminate between Magnetizing Inrush and Inter-nal Faults," IEEE Transactions on Power Delivery,Vol.9, No. 1, Jan 1994, pp 434-441.

[6] K. Ning, L.M.Wedephol, M. Nagpal, M.S. Sachdev,"Using a Neural Network for Transformer Protec-tion," Proceedings of the International Conference ofEnergy, Management and Power Delivery, Vol. 2, Sin-gapore, Nov 21-23, 1995, pp 674-679.

[7] A.L.Orille-Fernandez, Jaime A. Valencia , N.K.IGhonaim,"A FIRANN as a Differential Relayfor Three Phase Power Transformer Protection,"IEEE Transactions on Power Delivery, Vol.16, No. 2,April 2001, pp 215-218.

[8] Abdel-Maxoud I. Talaab, Hatem A. Darwish, TamerA. Kawady,"Development and Implementation ofan ANN-Based Fault Diagnosis Scheme for Gen-erator Winding Protection ," IEEE Transactions onPower Delivery, Vol.16, No. 2, April 2001, pp 208-214.

[9] M.S. Sachdev, Co-ordinator, ,Microprocessor Relaysand Protection Systems Tutorial Text Publicationno.88 EH-O269-1-PWR, IEEE, New York, 1987.

[10] T.S. Sidhu, L. Mital and M.S. Sachdev,"A Com-prehensive Analysis of an Artificial Neural Net-work Based Fault Direction Discriminator," IEEETransactions on Power Delivery, Vol.19, No. 3, July2004, pp 1042-1048.

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