protection of series compensated lines.pdf

8/10/2019 Protection of Series compensated lines.pdf

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Electric Power Systems Research 75 (2005) 8598

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 2004

Available online 10 May 2005

Abstract

Series compensation has been employed to improve power transfer in long-distance transmission systems worldwide. However, this in

turn introduces problems in conventional distance protection. The complex variation of line impedance is accentuated, as the capacitors own

protection equipment operates randomly under fault conditions. This paper proposes two approaches based on travelling waves and artificial

neural networks (ANN) for fault type classification and faulted phase selection of series compensated transmission lines.

A modal transformation technique, which decomposes thethree-phase line into three single-phaselines, is used for this purpose. Algorithms

based on two different modal transformations are developed forphase selection and fault classification. Each algorithmis derived from a corre-

sponding truthtable. The truthtables areconstructed for different typesof faults withdifferent faulted phases and different transformationbases.

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, ANNCand ANNG) are used

to identify faulted phase(s), and activated by the output of ANNFif there is a fault.

System simulation and test results, which are presented and analyzed in this paper indicate the feasibility of using travelling waves and ANN

in the protection of series compensated transmission lines.

2005 Elsevier B.V. All rights reserved.

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

1. Introduction

The conventional series compensation schemes have

proven to be an important component in economical long

distance power transmission. This is mainly because of the

low cost of the series compensation compared to the cost of

building a new transmission line. Series capacitors provide a

direct mean of reducing the transmission inductive reactanceand in turn increasing transfer capability, reducing the losses

associated with transmission lines, controlling the load flow

between parallel circuits and improving transient and steady-

state stability margins.

For the reasons mentioned, series-compensated transmis-

sion lines have become rather common in locations where the

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

distancesbetween load centers is great andlargetransmission

investments are required. Even though the series compensa-

tion has been known to create problems in system protection

and sub-synchronous resonance.

The addition of series capacitorsin the transmission circuit

makes the design of the protection more complex. The degree

of complexity depends on the size of the series capacitor, its

location along the transmission line and method of seriescapacitor bypass.

Series capacitors introduce more difficulties; this is

because the fundamental voltage and current phasors are

functions of distance to fault, the amount of series capac-

itors and the placements of series capacitors. In addition,

operation of the overvoltage protection scheme of the series

capacitors introduces different frequency components and

affects the steady-state fault signals[1].Furthermore, during

faults on series compensated transmission lines the series

0378-7796/$ see front matter 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.epsr.2004.10.016


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86 A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 8598

capacitors form resonant circuits with the system inductance.

The frequencies of these circuits are in the vicinity of the

fundamental frequency[2].Consequently, these extraneous

frequencies cause considerable difficulties if not accounted

for by the relaying algorithm.

It is well known that one of the main considerations in the

designing 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 improve

stability in powersystems.Because theMOVhas a non-linear

resistance characteristic and does not conduct symmetrically

under unbalanced faults, this is in turn poses problem for

conventional protection. Over the last decade, various tech-

niques have been developed and published in the literature

to solve the problem of protecting the series compensated

lines.

Thomas et al. [1] developed an algorithm based on

traveling waves techniques for series compensated trans-

mission systems. The algorithm uses correlation techniquesto recognize transient components, which departs from the

relaying points and returns to it later after a direct reflection

from the fault. From the timing of the departure and arrival of

these signals at the relaying point, the location of fault can be

found.

High-level faults are usually experienced in series

compensated transmission lines, and if faults are not cleared

rapidly they may cause system instability as well as damage

and hazards to equipment and persons. Hence, the proper

classification of transmission line faults is essential to

the appropriate operation of power systems. Fault type

classification is an essential protective relaying feature dueto its significant effect on the enhancement of relaying

scheme operation. Correct operation of major protective

relays may be depending on fault classification[3].

Faulted phase selectionis as importantas fault detection. It

would lead to increase the system stability and system avail-

ability by allowing single pole tripping. Single pole tripping

has many benefits like improving the transient stability and

reliability of the power system, reducing the switching over-

voltages and shaft torsional oscillations of large thermal units

[4].

Ghassemi and Johns[5] investigated the effect of the resid-

ual compensation factor on the measuring accuracy of dis-

tance protection measurements when an earth fault occurs on

a series compensated line.

A method is described in [6] that enhances the accuracy

of digital distance relays applied on series compensated lines

where the series capacitors are protected against overvoltages

by MOV. The technique is applicable to systems where the

relaying voltage is taken from the bus bar side of the series

capacitor. The basis of the technique is a method known as

voltage compensation. The voltage across the series capacitor

and overvoltage protective device is calculated in the relay.

Thus, the over-reach or under-reach of distance relays as a

result of MOV operation is eliminated.

Aggarwal and Johns[7]proposed a high speed numerical

method based on the directional comparison principle for

series compensated transmission systems. The basic feature

of their proposed method is to use communication channels

extracting information about voltage and current wave-

forms from both ends of the protected area. The algorithm

analyzes this information and determines the location offault.

Abou-El-Ela et al. [8] implemented the phase modified

Fourier transform principle suggested by Johns and Martin

[9] to estimate theimpedance of the seriescompensated lines.

The effect of the sub-synchronous resonance phenomena and

series capacitor flashover on the performance of distance re-

lay has been investigated.

Ghassemi and Johns[10] modified the technique proposed

in[8] and suggested a method for eliminating the source of

error in measurement of phase to ground faults dueto residual

compensation factor.

The artificial neural networks provide a very interesting

and 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 deterministic

algorithms to execute. ANN can also handle non-linear tasks

[1113].

In this paper, two approaches are proposed based on trav-

elling wave and ANN for fault type classification and faulted

phase selection for the protection of series compensated

transmission lines. A modal transformation technique, which

decomposesthe three-phase line into three single-phase lines,

is used for this purpose. Algorithms based on two different

modal transformations are developed for phase selection and

fault classification. Each algorithm is derived from a cor-responding truth table. The truth tables are constructed for

different types of faults with different faulted phases and dif-

ferent transformation bases.

The ANN proposed scheme is trained and tested using lo-

cal measurements of three-phase voltages and currents sam-

ples.System simulationand testresults indicate the feasibility

of using travelling waves and ANN in the protection of series

compensated transmission lines.

2. Fault detection principles and relaying

discriminant using travelling wave theory

2.1. Relaying signals for single-phase line

The inception of a fault in a transmission line will cause

the postfault voltage vR and current iR at the relaying point

to deviate from the steady-state prefault voltage vR andcurrentiR, respectively, as shown inFig. 1,wherevR andiRdenote the fault generated voltage and current deviation

from prefault steady-state values as functions of time. The

approach described in this paper, like others[1418],utilizes

these superimposed quantities of voltage and current at the

relaying point for making its decisions:


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A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 8598 87

Fig. 1. Principle of superposition.

- Forward relaying signal:

SF= (vR z iR) = 2Vmaxsin(t+ ) for internal fault= 0 for no/or external fault (1)

- Backward relaying signal:

SB= (vR + z iR) = 2Vmax sin(t+ ) for internal fault= 0 for no/or external fault (2)

2.2. Single-phase line relaying discriminants

The characteristic magnitude becomes a ramp function:

2

2Vrms, which would be difficult to detect. This problem

is avoided by using the wave characteristic in combinationwith its derivative to define a forward fault traveling wave

discriminant,DF:

- Forward discriminant function:

DF= S2F +(dSF/dt)

2

2 = 8V2rms for internal fault

= 0 for no/or external fault(3)

Following the same procedures used in deriving the for-

ward wave discriminant, a backward discriminantDBcan be

established in the following form:

- Backward discriminant function:

DB= S2B +(dSB/dt)

2

2 = 8V2rms for internal fault

= 0 for no/or external fault(4)

The direction discrimination on calculating both DF and

DBcan be summarized as follows:

IfDB converges (exceeds a certain threshold) before DFit means that it is a backward fault, otherwise it is a forward

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

procedure.

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 fault

detection in a three-phase line are defined by utilizing the su-

perimposed modal voltages and currents at the relaying point

as follows:

D(k)F = (v

(k)R z(k) i

(k)R )

2 + 12

d

dt(v

(k)R z(k) i

(k)R )

2(5)

for the mode (k) forward discriminants;

D(k)B = (v

(k)R z(k) i

(k)R )

2 + 12

ddt(v

(k)R z(k) i(k)R )2

(6)

for the mode (k) backward discriminant, where z(k) is the

mode (k) surge impedance, and v(k)R and i

(k)R are the mode-

k superimposing voltage and current, respectively, at relay

point R. These modal voltages and currents can be trans-

formed from the corresponding phase quantities by the fol-

lowing equations:

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

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

where [S] and [Q] are the modal transformation matrices. Foran ideally transposed single circuit line [Q] will be equal to

[S] andboth will be constant, but except for the zero sequence

mode, they will not be uniquely defined.

Discrete transposition of transmission lines is relatively

rare. However, conventional practice involves setting the pro-

tective relays assuming that the line is ideally transposed

[15].Therefore, in the present study, like some others (e.g.

[15,16,18]), the developed algorithm will be based on the

assumption of perfectly transposed transmission lines.

Two of these constant modal transformation matrices for

perfectlytransposedlines are consideredin thispaper,namely


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Wedepohl transformation[16,19,20]:

[Q] = [S] =

1 1 1

1 0 21 1 1

(9)

Karrenbauer transformation[15,18]:

[Q] = [S] =

1 1 1

1 2 11 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. Fault

classification is a relaying feature that also enhances the

protection scheme. In this section a phase selection andfault classification relaying principle based on the forego-

ing discussion is developed through modal transformation

theory.

Consider, for example, the Karrenbauer transformation

(Eq.(10)), from whichTable 1is constructed[21]. This table

shows the forward modal discriminant functions for differ-

ent fault types and different faulted phase(s) combinations.

The transformation to the modal domain in this table is based

on phase A. The table contents are normalized with respect

to V2rms, i.e., the square of the operating voltage. Details of

the derivation of this table are given in [22]. Similar table

could be derived for the other types of transformation, e.g.Wedepohl as shown inTable 2.

By investigating any of Karrenbauer or Wedepohl tables

it should be noted that some discriminant components vary

with respect to the faulted phase(s).

Thus, by calculating the discriminant components for

the same faults with the transformation base phase changed

from a to b and then to c, the truth tables in Figs.

2a and 3a for Karrenbauer and Wedepohl can be built,

respectively. In each of these tables the 0 stands for

the zero value of DF and the 1 stands for the non-

zero very high value of DF. Out of these tables decision

flow charts for phase selection and fault classification are

shown in Figs. 2b and 3b for each of the correspondingtransformations.

3. Digital simulation of MOV protection scheme

The protection scheme consists of a metal oxide varistor

with a 120 kV protective level voltage. When a fault occurs

in the transmission line and the voltage crossing the capacitor

is detected to exceed the protected level, the non-linear re-

sistance (MOV) conducts and limits future voltage increase

until the fault is cleared. Table1

DiscriminantcomponentsintheKarrenbausrdomain

Discriminantcomponents

Line-to-ground

Line-to-l

ine

Line-to-l

ine-to-gro

und

3LS

aG

b

G

cG

ab

bc

ca

ab

G

bcG

caG

D0

8 3

Z0

Z0+

2Z1

2

8 3

Z0

Z0+

2Z

1

2

8 3

Z0

Z0+

2Z

1

2

0

0

0

8 3

Z0

2Z

0+

Z1

2

8 3

Z0

2Z

0+

Z1

2

8 3

Z0

2Z0

+

Z1

2

0

D1

8 3

Z0

Z0+

2Z1

2

8 3

Z0

Z0+

2Z

1

2

0

8/9

2/9

2/9

8/9

8 9Z

2 0+

Z2 1+

Z0Z

1

(2Z

0+

Z1

)2

8 9Z

2 0+

Z2 1+

Z0Z

1

(2

Z0+

Z1

)2

8/9

D2

8 3

Z0

Z0+

2Z1

2

0

8 3

Z0

Z0+

2Z

1

2

2/9

2/9

8/9

8 9Z

2 0+

Z2 1+

Z0Z

1

(2Z

0+

Z1)

2

8 9Z

2 0+

Z2 1+

Z0Z

1

(2Z

0+

Z1

)2

8/9

8/9

D0

,D

1andD

2aretherelayingdiscriminant

componentsinKarrenbauerdomain;Z

0andZ1arethezeroandpositivesequencesurgeimp

edances,respectively;allthequantitiesarenormalizedwithrespectto

V2rm

slinelineprefaultvoltage.


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Table2

DiscriminantcomponentsintheWedepohld

omain

Discriminantcomponents

Line-to-ground

L

ine-to-l

ine

Line-to-l

ine-to-ground

3LS

aG

b

G

cG

a

b

bc

ca

ab

G

bcG

caG

D0

8 3

Z0

Z0+

2Z

1

2

8 3

Z0

Z0+

2Z

1

2

8 3

Z0

Z0+

2Z

1

2

0

0

0

8 3

Z0

Z0+

2Z

1

2

8 3

Z0

Z0+

2Z

1

2

8 3

Z0

Z0+

2Z1

2

0

D1

6

Z1

Z0+

2Z

1

2

0

6

Z1

Z0+

2Z

1

2

2

1/2

2

2Z

2 1+

Z2 0+

Z0Z

1

(2Z

0+

Z1

)2

2Z

2 1+

Z2 0+

Z0Z

1

(2Z

0+

Z1

)2

3 2

16Z

2 0+

12Z

0Z

1+

3Z

2 1

(Z0+

Z1

)2

2

D2

2 3

Z1

Z0+

2Z

1

2

8 3

Z0

Z0+

2Z

1

2

2 3

Z1

2Z

1+

Z0

2

2

1/2

0

2 33Z

2 0+

Z2 1+

3Z

0Z

1

(2Z

0+

Z1

)2

2 3

3Z

2 0+

Z2 1+

3Z

0Z

1

(2Z

0+

Z1

)2

2 3

Z1

(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 be

seen that when the fault occurs, the phase voltage exceeds

the protected level, and it is clear to note that the reinser-

tion of the capacitors is instantaneous and automatic. This

means that MOV protection scheme can improve the stabil-

ity of the system.Fig. 5shows the A-phase current acrossthe varistor and the capacitor, respectively. It indicates that

the MOV shares with the capacitor to conduct and limits the

capacitors voltage increase during the fault condition. Also

it is important to know that the conduction of the MOV is

not symmetrical during the unbalanced fault and the effect of

conduction through the MOV on the impedance of the trans-

mission line is different at different fault location as shown in

Figs. 6 and 7. The impedance relationship between the MOV

and transmission line is non-linear and cannot be defined dur-

ing the fault conditions. Hence, the conventionaldistance pro-

tection scheme is limited for series compensated transmission

systems. Consequently, a protection scheme using ANN is

proposed.

4. Computer simulation and the resulting

characteristic features

A power system with series compensation is considered

for the purpose of evaluating the viability of the developed

relaying technique with different fault types and locations.

This is achieved through computer numerical simulation

by utilizing the available version of the electromagnetic

transient program (EMTDC) [23], which is consid-

ered as an advanced power system computer simulationprogram.

4.1. The system under study

The system studied is composed of two generators, two

series capacitors that provide 80% compensation and their

protection equipment (MOV) in the 100 miles, 500 kV trans-

mission line. The voltampere characteristics of the MOV

protection is calculated as in[24].

The characteristics of the line:

Phase mode:

Z1= 0.041 +j0.528(/mile)

Y1= 7.86(S/mile)Ground mode:

Z0= 0.449 +j2.02(/mile)

Y0= 4.25(S/mile)

The system is completely transposed and has communi-

cation channels between phases. A single line diagram of the


6/14


7/14


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

3. Each of theWedepohland Karrenbauer transforms hasdif-ferent fault type resolution than the other. However, none

of 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].ItisshowninFigs.

912and1518that at the beginning the value ofD1F is

practically 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, compared

with high values ofD1F, D2F(notice the vertical log scale),

D0Fcan be practically considered as zero values.

5. Fault inception angle and fault resistance do not have

a 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).


8/14


Fig. 6. A-phase measured current at the bus-bar (an A phase-to-ground

fault 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 communication

channel a complete protection can be provided for the

majority of the faults. The direction decision (forward or

backward) and the phase selection and fault classification

are made independently at each line terminal and then

a trip signal for internal faults (or blocking for external

faults) 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 LL

fault and in case of LLG cannot be identified; also from

the flow chart of Wedepohl transformation shown in Fig. 3

the faulted phase in case of LLG cannot be identified.

Fig. 8. Study System.

Fig. 9. LG fault (phaseA).

Fig. 10. LL fault (AB).

Fig. 11. LLG fault (ABG).


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Fig. 12. LLLG fault.

Fig. 13. LG backward fault (phase B). Travelling wave discriminant com-

ponents w.r.t. phase A (using Karrenbauer transformation).

Fig. 14. LL backward fault (AC). Travelling wave discriminant compo-

nents w.r.t. phase A (using Karrenbauer transformation).

Fig. 15. LG fault (phaseA).

Fig. 16. LL fault (AB).

Fig. 17. LLG fault (ABG).


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Fig. 18. LLLG fault (ABG).

Fig. 19. Backward fault LG (phaseB). Travelling wave discriminant

components w.r.t. phase A (using Wedepohl transformation)

Fig. 20. Backward fault LL (BC) 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) can

be used to solve complex and non-linear engineering prob-

lems by learning from previous experience, without looking

for a complex mathematical relationship between inputs and

outputs. Once the neural network with appropriate input andoutput signals is trained, the interconnections will contain the

non-linearity of the desired mapping in the neural network,

so that looking for a complex non-linear relationship can be

avoided. 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 in Fig. 21.

In level-1 a neural network (ANNF) is used to detect the

fault, 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 and

ANNG) if there is a fault. Therefore, the proposed topol-

ogy determines both the fault type and the faulted phase(s)

selection.

The proposed scheme is trained and tested using local

measurements of three-phase voltage and current samples.

These samples are generated using EMTDC package. All 10

possible fault types are simulated. The sampling rate is 16samples per cycle of power frequency.

A sampling time of 0.0833 ms and 13 samples are taken

from the instantaneous voltages and currents for each case

study (during a cycle) and used in the training and the test-

ing sets. Data window of four samples, which are taken

recursively from the instantaneous voltages and currents

during a quarter of a cycle are also used in the train-

ing and testing processes. Seven fault locations at (10, 30,

40, 50, 60, 70 and 90%) from the length of the line are

taken for the training process. Another four fault loca-

tions are taken at (20, 45, 65 and 80%) from the length of

the line for the testing process. These ANNs are trained

and tested using neural-desk package [28] with a standard

backpropagation training algorithm. The different ANNs

are trained by different methods until getting the proper

number of samples per input pattern and proper design

of ANN.

The proper design for all ANNs used in this paper consists

of three layers; an input layer having 24 input nodes (four

recursive samples of three-phase voltages and currents), a

hidden layer of 10 neurons, and an output layer of one neuron

(fault detection in ANNFand faulted phase in ANNA,ANNB,

ANNCand ANNG. The architecture of these ANNs is shown

inFig. 22.


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Fig. 21. The ANN proposed scheme.

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

shortest time when a sigmoidal function is used. The sampled

voltages and currents are scaled to have a maximum value of

+1 and a minimum value of 0. The learning factor, which

controls the rate of convergence and stability, is chosen to be

0.05. The momentum constant is chosen to be 0.9, and the

training process is proceeding until the average error between

theactual output andthe desired output reached an acceptable

value, which was taken to be 0.001.

The output of (ANNF) is either 0 or 1 indicating that there

is a fault or not and the output of (ANNA, ANNB, ANNCand

ANNG) is also either 0 or 1 indicating that there is a fault on

the phase or not.

For example, if the outputs of the scheme are:

OF= 1, OA= 0, OB= 1, OC= 0and OG= 1;

this means that, there is a line-to-ground fault and the

faulted phase is B. Another example, if the outputs

Fig. 23. Three-phase voltages (kV), three-phase currents (kA), ANNs output for line to ground fault. AG at 80% of the line (fault inception at 0.303 s).


12/14


Fig. 24. Three-phase voltages (kV), three-phase currents (kA), ANNs output for line to line fault. AB 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. ABG at 80% of the line (fault inception at

0.303 s).

Fig. 26. Three-phase voltages (kV), Three-phase currents (kA), ANNs output for 3 line to ground fault. ABCG at 80% of the line (fault inception at0.303 s).

are:

OF= 1, OA= 0, OB= 1, OC= 1and OG= 0;

this means that, there is a line-to-line fault and the faulted

phases are B and C.

The classification accuracy in the training phase was per-

fect(100%), irrespective of faultlocationand faulttype, while

that of testing phase was fairly good.

From the testing process, it is seen that when fault occurs

from any type and at different fault locations, the actual

output can detect the fault precisely by using a threshold of

0.4. All the test results show that the ANNF is suitable for

detecting the fault and ANNA, ANNB, ANNC and ANNGare suitable for detecting the fault on phase A, B, C and

the ground.Figs. 2326show the three-phase voltages and

currents, ANNs output for different fault types at 80% of

the line with a fault inception at 0.303 s. These figures


13/14


show the validation of the proposed ANN-based relaying

approach.

7. Conclusion

A new travelling-wave protection principle for digitaltransmission line relaying has been presented in this paper.

This relaying principle features have phaseselectionand fault

classification capabilities. The major advantages of the new

principle as compared to previous travelling-wave-based re-

lays can be briefly itemized as follows:

1. Faulted phase selection capability for different types of

faults, which should then lead to selective pole-tripping

and hence enhanced system stability and availability.

Meanwhile, fault classification is another inherent special

feature of this relay, which has not been realized before

in any other travelling-wave-based relaying scheme.

2. The relaying discriminant functions used for fault detec-tion and direction discrimination are quite decisive and

insensitive 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 is

designed to detect the faults, classify the fault type and iden-

tify the faulted phase. The proposed 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 net-

works (ANNA, ANNB, ANNCand ANNG) are used to iden-

tify faulted phase(s), the output of ANNF activates (ANNA,

ANNB, ANNCand ANNG) if there is a fault. Therefore, the

proposed topology determines both the fault type and the

faulted phase(s) selection.

The ANN-based approach is compared with the travel-

ling wave-based relaying technique for similar case studies

[29,30]. ANN shows higher resolution regarding selecting

faulted phases.

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