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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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88 A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 8598
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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A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 8598 89
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
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A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 8598 91
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).
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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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A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 8598 93
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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94 A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 8598
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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A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 8598 95
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).
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96 A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 8598
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
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A.Y. Abdelaziz et al. / Electric Power Systems Research 75 (2005) 8598 97
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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