1-s2.0-S0378779605000969-main

Electric Power Systems Research 75 (2005) 85–98

Modern approaches for protection of seriescompensated transmission lines

A.Y. Abdelaziz∗, A.M. Ibrahim, M.M. Mansour, H.E. TalaatDepartment of Electrical Power and Machines, Faculty of Engineering, Ain Shams University, Abdo Basha squere, Abbassia, Cairo, Egypt

Received in revised form 28 June 2004; accepted 24 October 2004Available online 10 May 2005

Abstract

Series compensation has been employed to improve power transfer in long-distance transmission systems worldwide. However, this inturn introduces problems in conventional distance protection. The complex variation of line impedance is accentuated, as the capacitor’s ownprotection equipment operates randomly under fault conditions. This paper proposes two approaches based on travelling waves and artificialneural networks (ANN) for fault type classification and faulted phase selection of series compensated transmission lines.

A modal transformation technique, which decomposes the three-phase line into three single-phase lines, is used for this purpose. Algorithmsb om a corre-s tion bas

s and ANN

©

K

1

pdlbdaabs

s

issionensa-ction

rcuitgreer, itsries

isrs arepac-ition,eriesand

gseries

0d

ased on two different modal transformations are developed for phase selection and fault classification. Each algorithm is derived frponding truth table. The truth tables are constructed for different types of faults with different faulted phases and different transformaes.The proposed ANN topology is composed of two levels of neural networks:

In level-1, a neural network (ANNF) is used to detect the fault. In level-2, four neural networks (ANNA, ANNB, ANNC and ANNG) are usedto identify faulted phase(s), and activated by the output of ANNF if there is a fault.System simulation and test results, which are presented and analyzed in this paper indicate the feasibility of using travelling wavein the protection of series compensated transmission lines.

2005 Elsevier B.V. All rights reserved.

eywords:Series compensated transmission lines; Traveling waves; Neural network

. Introduction

The conventional series compensation schemes haveroven to be an important component in economical longistance power transmission. This is mainly because of the

ow cost of the series compensation compared to the cost ofuilding a new transmission line. Series capacitors provide airect mean of reducing the transmission inductive reactancend in turn increasing transfer capability, reducing the lossesssociated with transmission lines, controlling the load flowetween parallel circuits and improving transient and steady-tate stability margins.

For the reasons mentioned, series-compensated transmis-ion lines have become rather common in locations where the

∗ Corresponding author.E-mail address:[email protected] (A.Y. Abdelaziz).

distances between load centers is great and large transminvestments are required. Even though the series comption has been known to create problems in system proteand sub-synchronous resonance.

The addition of series capacitors in the transmission cimakes the design of the protection more complex. The deof complexity depends on the size of the series capacitolocation along the transmission line and method of secapacitor bypass.

Series capacitors introduce more difficulties; thisbecause the fundamental voltage and current phasofunctions of distance to fault, the amount of series caitors and the placements of series capacitors. In addoperation of the overvoltage protection scheme of the scapacitors introduces different frequency componentsaffects the steady-state fault signals[1]. Furthermore, durinfaults on series compensated transmission lines the

378-7796/$ – see front matter © 2005 Elsevier B.V. All rights reserved.oi:10.1016/j.epsr.2004.10.016

86 A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 85–98

capacitors form resonant circuits with the system inductance.The frequencies of these circuits are in the vicinity of thefundamental frequency[2]. Consequently, these extraneousfrequencies cause considerable difficulties if not accountedfor by the relaying algorithm.

It is well known that one of the main considerations in thedesigning capacitor is overvoltage protection of the capacitoritself. In recent years, the new metal oxide varistor (MOV),which has been widely used as the overvoltage protection de-vice for the series capacitor, has also been shown to improvestability in power systems. Because the MOV has a non-linearresistance characteristic and does not conduct symmetricallyunder unbalanced faults, this is in turn poses problem forconventional protection. Over the last decade, various tech-niques have been developed and published in the literatureto solve the problem of protecting the series compensatedlines.

Thomas et al.[1] developed an algorithm based ontraveling waves techniques for series compensated trans-mission systems. The algorithm uses correlation techniquesto recognize transient components, which departs from therelaying points and returns to it later after a direct reflectionfrom the fault. From the timing of the departure and arrival ofthese signals at the relaying point, the location of fault can befound.

High-level faults are usually experienced in seriesc aredr agea ropec l tot typec duet yings ctiver

ion. Itw vail-a ngh andr ver-v units[

id-u f dis-t urs ona

cyo linesw ltageb ther eriesc n asv acitoa elay.T as ar

Aggarwal and Johns[7] proposed a high speed numericalmethod based on the directional comparison principle forseries compensated transmission systems. The basic featureof their proposed method is to use communication channelsextracting information about voltage and current wave-forms from both ends of the protected area. The algorithmanalyzes this information and determines the location offault.

Abou-El-Ela et al.[8] implemented the phase modifiedFourier transform principle suggested by Johns and Martin[9] to estimate the impedance of the series compensated lines.The effect of the sub-synchronous resonance phenomena andseries capacitor flashover on the performance of distance re-lay has been investigated.

Ghassemi and Johns[10] modified the technique proposedin [8] and suggested a method for eliminating the source oferror in measurement of phase to ground faults due to residualcompensation factor.

The artificial neural networks provide a very interestingand valuable alternative for the protection of series compen-sated transmission lines because they can deal with most sit-uations, which are not defined sufficiently for deterministicalgorithms to execute. ANN can also handle non-linear tasks[11–13].

In this paper, two approaches are proposed based on trav-elling wave and ANN for fault type classification and faultedp satedt hichd lines,i rentm n andf or-r d ford dif-f

g lo-c s sam-p ibilityo riesc

2d

2

set ttc ationf Theat at ther

ompensated transmission lines, and if faults are not cleapidly they may cause system instability as well as damnd hazards to equipment and persons. Hence, the plassification of transmission line faults is essentiahe appropriate operation of power systems. Faultlassification is an essential protective relaying featureo its significant effect on the enhancement of relacheme operation. Correct operation of major proteelays may be depending on fault classification[3].

Faulted phase selection is as important as fault detectould lead to increase the system stability and system ability by allowing single pole tripping. Single pole trippias many benefits like improving the transient stabilityeliability of the power system, reducing the switching ooltages and shaft torsional oscillations of large thermal4].

Ghassemi and Johns[5] investigated the effect of the resal compensation factor on the measuring accuracy o

ance protection measurements when an earth fault occseries compensated line.A method is described in[6] that enhances the accura

f digital distance relays applied on series compensatedhere the series capacitors are protected against overvoy MOV. The technique is applicable to systems whereelaying voltage is taken from the bus bar side of the sapacitor. The basis of the technique is a method knowoltage compensation. The voltage across the series capnd overvoltage protective device is calculated in the rhus, the over-reach or under-reach of distance relaysesult of MOV operation is eliminated.

r

s

r

hase selection for the protection of series compenransmission lines. A modal transformation technique, wecomposes the three-phase line into three single-phase

s used for this purpose. Algorithms based on two diffeodal transformations are developed for phase selectio

ault classification. Each algorithm is derived from a cesponding truth table. The truth tables are constructeifferent types of faults with different faulted phases and

erent transformation bases.The ANN proposed scheme is trained and tested usin

al measurements of three-phase voltages and currentles. System simulation and test results indicate the feasf using travelling waves and ANN in the protection of seompensated transmission lines.

. Fault detection principles and relayingiscriminant using travelling wave theory

.1. Relaying signals for single-phase line

The inception of a fault in a transmission line will cauhe postfault voltagevR and currentiR at the relaying poino deviate from the steady-state prefault voltagev′

R andurrenti′R, respectively, as shown inFig. 1, wherevR andiR denote the fault generated voltage and current devi

rom prefault steady-state values as functions of time.pproach described in this paper, like others[14–18], utilizes

hese superimposed quantities of voltage and currentelaying point for making its decisions:

A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 85–98 87

Fig. 1. Principle of superposition.

- Forward relaying signal:

SF = (vR − z iR) = −2Vmaxsin(ωt + ϕ) for internal fault

= 0 for no/or external fault(1)

- Backward relaying signal:

SB = (vR + z iR) = −2Vmax sin(ωt + ϕ) for internal fault

= 0 for no/or exte(2)

2.2. Single-phase line relaying discriminants

The characteristic magnitude becomes a ramp function:2√

2Vrms, which would be difficult to detect. This problemis avoided by using the wave characteristic in combinationwith its derivative to define a “forward fault traveling wavediscriminant”,DF:

- Forward discriminant function:

DF = S2F + (dSF/dt)2

ω2= 8V 2

rms for internal fault

= 0 for no/or external fault(3)

Following the same procedures used in deriving the for-ward wave discriminant, a backward discriminantDB can bee

-

t

De

i ard

fault. The discrimination is seen to be quite reliable with thisprocedure.

2.3. Three-phase line relaying discriminants

According to the theory of natural modes[19], a three-phase coupled line can be decomposed into three indepen-dent single-phase lines (modes). The discriminants for faultdetection in a three-phase line are defined by utilizing the su-perimposed modal voltages and currents at the relaying pointas follows:

D(k)F = (v

(k)R − z(k) i

(k)R )

2 + 1

ω2d

dt(v

(k)R − z(k) i

(k)R )

2

(5)

for the mode (k) forward discriminants;

D

fm -k elayp ans-f fol-l

[

[

w . Fora l to[ encem

elyr pro-t osed[ (e.g.[ thea

s forp mely

stablished in the following form:

Backward discriminant function:

DB = S2B + (dSB/dt)2

ω2= 8V 2

rms for internal fault

= 0 for no/or external faul(4)

The direction discrimination on calculating bothDF andB can be summarized as follows:If DB converges (exceeds a certain threshold) beforDF

t means that it is a backward fault, otherwise it is a forw

rnal fault

(k)B = (v

(k)R − z(k) i

(k)R )

2 + 1

ω2d

dt(v

(k)R − z(k) i(k)

R )

2

(6)

or the mode (k) backward discriminant, wherez(k) is theode (k) surge impedance, andv

(k)R andi

(k)R are the mode

superimposing voltage and current, respectively, at roint “R”. These modal voltages and currents can be tr

ormed from the corresponding phase quantities by theowing equations:

v(t)] = [S][v(mode)(t)] (7)

i(t)] = [Q][ i(mode)(t)] (8)

here [S] and [Q] are the modal transformation matricesn ideally transposed single circuit line [Q] will be equa

S] and both will be constant, but except for the zero sequode, they will not be uniquely defined.Discrete transposition of transmission lines is relativ

are. However, conventional practice involves setting theective relays assuming that the line is ideally transp15]. Therefore, in the present study, like some others15,16,18]), the developed algorithm will be based onssumption of perfectly transposed transmission lines.

Two of these constant modal transformation matriceerfectly transposed lines are considered in this paper, na


Wedepohl transformation[16,19,20]:

[Q] = [S] =

1 1 1

1 0 −2

1 −1 1

(9)

Karrenbauer transformation[15,18]:

[Q] = [S] =

1 1 1

1 −2 1

1 1 −2

(10)

2.4. Faulted phase selection and fault classification

Faulted phase selection, and hence selective pole trip-ping, is an important relaying capability because it in-creases the system stability as well as its availability. Faultclassification is a relaying feature that also enhances theprotection scheme. In this section a phase selection andfault classification relaying principle based on the forego-ing discussion is developed through modal transformationtheory.

Consider, for example, the Karrenbauer transformation(Eq.(10)), from whichTable 1is constructed[21]. This tableshows the forward modal discriminant functions for differ-e ions.T asedo pectt s oftc e.g.W

blesi varyw

fort ngedf s.2 uilt,r fort -z nfl ares ngt

3

istorw ursi citori r re-s aseu Ta

ble

1D

iscr

imin

antc

ompo

nent

sin

the

Kar

renb

ausr

dom

ain

Dis

crim

inan

tcom

pone

nts

Line

-to-

grou

ndLi

ne-t

o-lin

eLi

ne-t

o-lin

e-to

-gro

und

3LS

a–G

b–G

c–G

a–b

b–c

c–a

a–b–

Gb–

c–G

c–a–

G

D0

8 3

(Z

0

Z0+

2Z1

) 28 3

(Z

0

Z0+

2Z1

) 28 3

(Z

0

Z0+

2Z1

) 20

00

8 3

(Z

0

2Z0+

Z1

) 28 3

(Z

0

2Z0+

Z1

) 28 3

(Z

0

2Z0+

Z1

) 20

D1

8 3

(Z

0

Z0+

2Z1

) 28 3

(Z

0

Z0+

2Z1

) 20

8/9

2/9

2/9

8/9

8 9

( Z2 0+

Z2 1+

Z0Z

1

(2Z

0+

Z1)2

)8 9

( Z2 0+

Z2 1+

Z0Z

1

(2Z

0+

Z1)2

)8/

9

D2

8 3

(Z

0

Z0+

2Z1

) 20

8 3

(Z

0

Z0+

2Z1

) 22/

92/

98/

98 9

( Z2 0+

Z2 1+

Z0Z

1

(2Z

0+

Z1)2

)8 9

( Z2 0+

Z2 1+

Z0Z

1

(2Z

0+

Z1)2

)8/

98/

9

D0,D

1an

dD

2ar

eth

ere

layi

ngdi

scrim

inan

tcom

pone

nts

inK

arre

nbau

erdo

mai

n;Z

0an

dZ

1ar

eth

eze

roan

dpo

sitiv

ese

quen

cesu

rge

impe

danc

es,r

espe

ctiv

ely;

allt

hequ

antit

ies

are

norm

aliz

edw

ithre

spec

tto

V2 rm

slin

e–lin

epr

efau

ltvo

ltage

.

nt fault types and different faulted phase(s) combinathe transformation to the modal domain in this table is bn phase ‘A’. The table contents are normalized with res

o V 2rms, i.e., the square of the operating voltage. Detail

he derivation of this table are given in[22]. Similar tableould be derived for the other types of transformation,edepohl as shown inTable 2.By investigating any of Karrenbauer or Wedepohl ta

t should be noted that some discriminant componentsith respect to the faulted phase(s).Thus, by calculating the discriminant components

he same faults with the transformation base phase charom “a” to “b” and then to “c”, the truth tables in Figa and 3a for Karrenbauer and Wedepohl can be bespectively. In each of these tables the “0” standshe zero value of “DF” and the “1” stands for the nonero very high value of “DF”. Out of these tables decisioow charts for phase selection and fault classificationhown in Figs.2b and 3b for each of the correspondiransformations.

. Digital simulation of MOV protection scheme

The protection scheme consists of a metal oxide varith a 120 kV protective level voltage. When a fault occ

n the transmission line and the voltage crossing the capas detected to exceed the protected level, the non-lineaistance (MOV) conducts and limits future voltage increntil the fault is cleared.


Tabl

e2

Dis

crim

inan

tcom

pone

nts

inth

eW

edep

ohld

omai

n

Dis

crim

inan

tcom

pone

nts

Line

-to-

grou

ndLi

ne-t

o-lin

eLi

ne-t

o-lin

e-to

-gro

und

3LS

a–G

b–G

c–G

a–b

b–c

c–a

a–b–

Gb–

c–G

c–a–

G

D0

8 3

(Z

0

Z0+

2Z1

) 28 3

(Z

0

Z0+

2Z1

) 28 3

(Z

0

Z0+

2Z1

) 20

00

8 3

(Z

0

Z0+

2Z1

) 28 3

(Z

0

Z0+

2Z1

) 28 3

(Z

0

Z0+

2Z1

) 20

D1

6(Z

1

Z0+

2Z1

) 20

6(Z

1

Z0+

2Z1

) 22

1/2

22( Z

2 1+

Z2 0+

Z0Z

1

(2Z

0+

Z1)2

)2

( Z2 1+

Z2 0+

Z0Z

1

(2Z

0+

Z1)2

)3 2

( 16Z

2 0+

12Z

0Z

1+

3Z2 1

(Z0+

Z1)2

)2

D2

2 3

(Z

1

Z0+

2Z1

) 28 3

(Z

0

Z0+

2Z1

) 22 3

(Z

1

2Z1+

Z0

) 22

1/2

02 3

( 3Z2 0+

Z2 1+

3Z0Z

1

(2Z

0+

Z1)2

)2 3

( 3Z2 0+

Z2 1+

3Z0Z

1

(2Z

0+

Z1)2

)2 3

(Z

1

(Z1+

2Z0)) 2

2/3 Figs. 4 and 5 show an A-phase to ground fault.Fig. 4

indicates the phase voltage across the capacitor, it can beseen that when the fault occurs, the phase voltage exceedsthe protected level, and it is clear to note that the reinser-tion of the capacitors is instantaneous and automatic. Thismeans that MOV protection scheme can improve the stabil-ity of the system.Fig. 5 shows the A-phase current acrossthe varistor and the capacitor, respectively. It indicates thatthe MOV shares with the capacitor to conduct and limits thecapacitor’s voltage increase during the fault condition. Alsoit is important to know that the conduction of the MOV isnot symmetrical during the unbalanced fault and the effect ofconduction through the MOV on the impedance of the trans-mission line is different at different fault location as shown inFigs. 6 and 7. The impedance relationship between the MOVand transmission line is non-linear and cannot be defined dur-ing the fault conditions. Hence, the conventional distance pro-tection scheme is limited for series compensated transmissionsystems. Consequently, a protection scheme using ANN isproposed.

4. Computer simulation and the resultingcharacteristic features

A power system with series compensation is consideredf pedr ns.T tionb etict -e lationp

4

, twos theirp ns-m OVp

uni-c of the

or the purpose of evaluating the viability of the develoelaying technique with different fault types and locatiohis is achieved through computer numerical simulay utilizing the available version of the electromagn

ransient program (EMTDC)[23], which is considred as an advanced power system computer simurogram.

.1. The system under study

The system studied is composed of two generatorseries capacitors that provide 80% compensation androtection equipment (MOV) in the 100 miles, 500 kV traission line. The volt–ampere characteristics of the Mrotection is calculated as in[24].

The characteristics of the line:

Phase mode:

Z1 = 0.041+ j0.528(Ω/mile)

Y1 = 7.86(S/mile)

Ground mode:

Z0 = 0.449+ j2.02(Ω/mile)

Y0 = 4.25(S/mile)

The system is completely transposed and has commation channels between phases. A single line diagram


Fig. 2. Phase selection and fault classification based on the Karrenbauer transform.

study system is shown inFig. 8. All the components are mod-eled by the modified version of the Electromagnetic TransientProgram.

4.2. Computer simulation

A constant frequency model has been used in this paper.It has been confirmed in[25,26] that frequency dependen-cies would not have significant effect on the discriminantfunctions. Different fault types are considered with a certainfault inception angle and at different fault locations on theline. The sampling rate is 16 samples per cycle of powerfrequency.

For the sake of clarity, samples of some simulatedconditions based on Karrenbauer transformation are shownin Figs. 9–14and others based on Wedepohl transformationare shown inFigs. 15–20. Each figure corresponds to acertain type of fault as indicated on the figures themselves.Fault inception angle is taken to be 45 of phase ‘A’for all the cases shown. The first four figures based onKarrenbauer transformation (Figs. 9–12) are forward faults,and Figs. 13 and 14show L–G and L–L backward faultsnear the relay location.Figs. 15–18are forward faults basedon Wedepohl transformation, andFigs. 19 and 20showL–G and L–L backward faults near the relay location. The

different types of forward faults are applied at 80% from thelength of the line. In each figure, a semi-log scale is used.Each one corresponds to the behavior of the discriminantcomponents with phase “A” as a transformation basis. Thetime span shown is one-half cycle before fault (steady state)to one-half cycle after fault inception.

4.3. Characteristic features of the proposed relayingscheme based on travelling wave

The following conclusions are based on the analysis of thedeveloped relaying approach as well as the computer numer-ical study:

1. The values of the different discriminant components afterfault inception angle are supposed to be, ideally, equal tothe corresponding values given onTables 1 and 2.

2. Based on fault calculation of the backward discriminantcomponents (sayD1

B andD2B) with respect to one phase

as a basis, and simultaneous calculation of the forward“D‘s” ( D0

F, D1F andD2

F) with respect to each phase as abasis, the fault direction, the forward fault type and theforward faulted phases could be defined within less thana quarter of a power frequency cycle.


Fig. 3. Phase selection and fault classification based on the Wedepohl transform.

3. Each of the Wedepohl and Karrenbauer transforms has dif-ferent fault type resolution than the other. However, noneof them would lead to a complete fault type classification[15].

4. The used modal transforms are based on ideally trans-posed transmission lines[15,21,25,26]. It is shown in Figs.9–12and15–18that at the beginning the value ofD1

F ispractically zero but after some little time some ripples ap-

Fig. 4. A-phase voltage across the capacitor (an “A” phase-to-ground fault).

pear which could be due to computation methods and/orreflection and refraction of waves. However, comparedwith high values ofD1

F, D2F (notice the vertical log scale),

D0F can be practically considered as zero values.

5. Fault inception angle and fault resistance do not havea great impact on the suggested relaying approach[15,25,26].

Fig. 5. A-phase currents across the MOV and capacitor (an “A” phase-to-ground fault).


Fig. 6. A-phase measured current at the bus-bar (an “A” phase-to-groundfault occurring at different fault locations).

Fig. 7. A-phase measured voltage at the bus-bar (an “A” phase-to-groundfault occurring at different fault locations).

6. If the relay works in conjunction with the communicationchannel a complete protection can be provided for themajority of the faults. The direction decision (forward orbackward) and the phase selection and fault classificationare made independently at each line terminal and thena trip signal for internal faults (or blocking for externalfaults) is provided over the channel.

7. It is seen from the flow chart of Karrenbauer transforma-tion shown inFig. 2that the faulted phase in case of L–Lfault and in case of L–L–G cannot be identified; also fromthe flow chart of Wedepohl transformation shown inFig. 3the faulted phase in case of L–L–G cannot be identified.

Fig. 8. Study System.

Fig. 9. L–G fault (phase“A”).

Fig. 10. L–L fault (A–B).

Fig. 11. L–L–G fault (A–B–G).


Fig. 12. L–L–L–G fault.

Fig. 13. L–G backward fault (phase B). Travelling wave discriminant com-ponents w.r.t. phase “A” (using Karrenbauer transformation).

Fig. 14. L–L backward fault (A–C). Travelling wave discriminant compo-nents w.r.t. phase “A” (using Karrenbauer transformation).

Fig. 15. L–G fault (phase“A”).

Fig. 16. L–L fault (A–B).

Fig. 17. L–L–G fault (A–B–G).


Fig. 18. L–L–L–G fault (A–B–G).

Fig. 19. Backward fault L–G (phase“B”). Travelling wave discriminantcomponents w.r.t. phase “A” (using Wedepohl transformation)

Fig. 20. Backward fault L–L (B–C) travelling wave discriminant compo-nents w.r.t. phase “A” (using Wedepohl transformation).

5. Artificial neural networks

It is well known that artificial neural networks (ANN) canbe used to solve complex and non-linear engineering prob-lems by learning from previous experience, without lookingfor a complex mathematical relationship between inputs andoutputs. Once the neural network with appropriate input andoutput signals is trained, the interconnections will contain thenon-linearity of the desired mapping in the neural network,so that looking for a complex non-linear relationship can beavoided. Further details of artificial neural network methods,and the various enhancements which have been used here,can be found in the extensive literature on the subject, e.g. in[27].

6. The proposed ANN-based approach

The proposed topology of the protection scheme is com-posed of two levels of neural networks shown inFig. 21.In level-1 a neural network (ANNF) is used to detect thefault, while in level-2, four neural networks (ANNA, ANNB,ANNC and ANNG) are used to identify faulted phase(s).The output of ANNF activates (ANNA, ANNB, ANNC andANN ) if there is a fault. Therefore, the proposed topol-o se(s)s

localm ples.T All 10p is 16s

akenf cases test-i kenr rentsd ain-i , 30,4 aret ca-t th oft ineda db Nsa pern signo

sistso fourr ts), ah uron(A wni

Ggy determines both the fault type and the faulted phaelection.

The proposed scheme is trained and tested usingeasurements of three-phase voltage and current samhese samples are generated using EMTDC package.ossible fault types are simulated. The sampling rateamples per cycle of power frequency.

A sampling time of 0.0833 ms and 13 samples are trom the instantaneous voltages and currents for eachtudy (during a cycle) and used in the training and theng sets. Data window of four samples, which are taecursively from the instantaneous voltages and cururing a quarter of a cycle are also used in the tr

ng and testing processes. Seven fault locations at (100, 50, 60, 70 and 90%) from the length of the line

aken for the training process. Another four fault loions are taken at (20, 45, 65 and 80%) from the lenghe line for the testing process. These ANNs are trand tested using neural-desk package[28] with a standarackpropagation training algorithm. The different ANre trained by different methods until getting the proumber of samples per input pattern and proper def ANN.

The proper design for all ANNs used in this paper conf three layers; an input layer having 24 input nodes (ecursive samples of three-phase voltages and currenidden layer of 10 neurons, and an output layer of one nefault detection in ANNF and faulted phase in ANNA, ANNB,NNC and ANNG. The architecture of these ANNs is sho

n Fig. 22.


Fig. 21. The ANN proposed scheme.

Fig. 22. Architecture of the neural networks (ANNF, ANNA, ANNB, ANNC

and ANNG).

6.1. Testing of the ANN-based approach

The training process is terminated when a suitable topol-ogy with a satisfactory performance is established. In this

study, it is found that a neural network with 10 hidden neu-rons had an acceptable performance, which converged in ashortest time when a sigmoidal function is used. The sampledvoltages and currents are scaled to have a maximum value of+1 and a minimum value of 0. The learning factor, whichcontrols the rate of convergence and stability, is chosen to be0.05. The momentum constant is chosen to be 0.9, and thetraining process is proceeding until the average error betweenthe actual output and the desired output reached an acceptablevalue, which was taken to be 0.001.

The output of (ANNF) is either 0 or 1 indicating that thereis a fault or not and the output of (ANNA, ANNB, ANNC andANNG) is also either 0 or 1 indicating that there is a fault onthe phase or not.

For example, if the outputs of the scheme are:

OF = 1, OA = 0, OB = 1, OC = 0 andOG = 1;

this means that, there is a line-to-ground fault and thefaulted phase is B. Another example, if the outputs

Fig. 23. Three-phase voltages (kV), three-phase currents (kA), ANNs outp .303
ut for line to ground fault. “A–G” at 80% of the line (fault inception at 0s).


Fig. 24. Three-phase voltages (kV), three-phase currents (kA), ANNs output for line to line fault. “A–B” at 80% of the line (fault inception at 0.303 s).

Fig. 25. Three-phase voltages (kV), three-phase currents (kA), ANNs output for line to line to ground fault. “A–B–G” at 80% of the line (fault inception at0.303 s).

Fig. 26. Three-phase voltages (kV), Three-phase currents (kA), ANNs output for 3 line to ground fault. “A–B–C–G” at 80% of the line (fault inception at0.303 s).

are:

OF = 1, OA = 0, OB = 1, OC = 1 andOG = 0;

this means that, there is a line-to-line fault and the faultedphases are B and C.

The classification accuracy in the training phase was per-fect (100%), irrespective of fault location and fault type, whilethat of testing phase was fairly good.

From the testing process, it is seen that when fault occursfrom any type and at different fault locations, the actualoutput can detect the fault precisely by using a threshold of0.4. All the test results show that the ANNF is suitable fordetecting the fault and ANNA, ANNB, ANNC and ANNGare suitable for detecting the fault on phase A, B, C andthe ground.Figs. 23–26show the three-phase voltages andcurrents, ANNs output for different fault types at 80% ofthe line with a fault inception at 0.303 s. These figures


show the validation of the proposed ANN-based relayingapproach.

7. Conclusion

A new travelling-wave protection principle for digitaltransmission line relaying has been presented in this paper.This relaying principle features have phase selection and faultclassification capabilities. The major advantages of the newprinciple as compared to previous travelling-wave-based re-lays can be briefly itemized as follows:

1. Faulted phase selection capability for different types offaults, which should then lead to selective pole-trippingand hence enhanced system stability and availability.Meanwhile, fault classification is another inherent specialfeature of this relay, which has not been realized beforein any other travelling-wave-based relaying scheme.

2. The relaying discriminant functions used for fault detec-tion and direction discrimination are quite decisive andinsensitive to parameter variation, different system con-figurations, and fault initiation angle.

Also an ANN-based protection scheme for the series com-pensated transmission lines is presented. This approach isd iden-t osedo ork( et-w -tA ep thef

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compensated lines protected by a metal oxide varistor, IEE Proc.Generation Transm. Distrib. 145 (July (4)) (1998) 403–408.

[7] R.K. Aggarwal, A.T. Johns, D.S. Tripp, The development and appli-cation of directional comparison protection for series compensatedtransmission systems, in: IEEE Transactions on Power Delivery, vol.2, No. 4, October 1987, pp. 1037–1045.

[8] M.S. Abou-El-Ela, F. Ghassemi, A.T. Johns, Performance of digitaldistance protection for series compensated systems, in: 24th Univer-sities Power Engineering Conference, Sunderland, UK, 1987.

[9] J. Johns, M.A. Martin, New ultra high speed distance protectionusing finite transform techniques, IEE Proc. 130 (May (3 Pt C))(1983).

[10] F. Ghassemi, A.T. Johns, Analysis and compensation of errors indistance protection measurements for series compensated systems, in:27th Universities Power Engineering Conference, Sunderland, UK,1990.

[11] Q.Y. Xuan, R. Morgan, D. Williams, Y.H. Song, A.T. Johns, Adaptiveprotection for series compensated EHV transmission systems usingneural networks, in: International Conference, Control ’94, vol. 1,March 1994, pp. 728–732.

[12] B. Bachmann, D. Novosel, D. Hart, Yi Hu, M.M. Saha, Applica-tion of artificial neural networks for series compensated line protec-tion, in: Intelligent Systems Applications to Power Systems, Proceed-ings of ISAP ’96 International Conference, January 1996, pp. 68–73.

[13] Y.H. Song, A.T. Johns, Q.Y. Xuan, Artificial neural-network-basedprotection scheme for controllable series-compensated EHV trans-mission lines, IEE Proc. Generation Transm. Distrib. 143 (November(6)) (1996) 535–540.

[14] E.O. Schweitzer, A.J. Flechsig, An efficient distance algorithm fordigital computer relaying, Mexico City, Mexico, July 17–22, in:

[ HVrans.

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esigned to detect the faults, classify the fault type andify the faulted phase. The proposed topology is compf two levels of neural networks. In level-1, a neural netwANNF) is used to detect the fault. In level-2, four neural norks (ANNA, ANNB, ANNC and ANNG) are used to iden

ify faulted phase(s), the output of ANNF activates (ANNA,NNB, ANNC and ANNG) if there is a fault. Therefore, throposed topology determines both the fault type and

aulted phase(s) selection.The ANN-based approach is compared with the tra

ing wave-based relaying technique for similar case stu29,30]. ANN shows higher resolution regarding selecaulted phases.

eferences

[1] D.W. Thomas, C. Christopoulos, Ultra-high speed protection of scompensated lines, IEEE Trans. Power Deliv. 7 (January (1)) (1139–145.

[2] A.G. Phadke, J.S. Thorp, Computer Relaying for Power SystJohn Wiley & Sons Inc., 1988.

[3] S.H. Horowitz, A.G. Phadke, Power System Relaying, RSPLEngland, 1992.

[4] IEEE Working Group—Power System Relaying Committee, Siphase tripping and autoreclosing of transmission lines, IEEE TPower Deliv. 7 (January (1)) (1992) 182–192.

[5] F. Ghassemi, A.T. Johns, Investigation of alternative residualrent compensation for improving series compensated line disprotection, IEEE Trans. Power Deliv. 5 (April (2)) (1990) 56574.

[6] F. Ghassemi, J. Goodarzi, A.T. Johns, Method to improve ddistance relay impedance measurement when used in

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transmission lines-development design and application, IEEE TPAS-96 (November/December (6)) (1978) 2104–2116.

16] IEEE Tutorial Course, Computer Relaying, 40-50, Course79EH014-7PWR, 1979 (Chapter 5).

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19] H.M. Dommel, Digital computer solution of electro magnetic trsients in single and multi-phase networks, IEEE Trans. PA(April) (1969) 388–399.

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21] M.M.S. Mansour, A travelling-wave relay featuring fault classcation and phase selection, Ph.D Thesis, University of ManiWinnipeg, Canada, 1984.

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25] M.M. Mansourm, G.W. Swift, Multi-microprocessor-based travewave relay, in: Third IEE International Conference on Developmin Power System Protection, London, UK, 17–19 April 1985,91–95.

26] M.M. Mansour, G.W. Swift, Design and testing of a mumicroprocessor-based traveling wave relay, IEEE Paper, 1986 WMeeting, 115-0.

27] M. Caudill, C. Butler, Understanding Neural Networks, vol. I: BaNetworks, The MIT Press, London, England, 1992.


[28] Neural Desk User’s Guide, Copyright 1992 Neural computer Sci-ences, Delta Technology Ltd., Haddenham, Aylesbury, Bucking-hamshire.

[29] A.M. Ibrahim, An intelligent adaptive distance protection for powernetworks, M.Sc. Thesis, Ain Shams University, Cairo, February2003.

[30] A.Y. Abdelaziz, Y.G. Mostafa, A.M. Ibrahim, M.M. Mansour,H.E. Talaat, A neural network based approach for protectionof series compensated transmission lines, in: Proceedings of theNinth International Middle-East Power Systems Conference MEP-CON’2003, Menofia University, Egypt, December 2003, pp. 405–411.

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