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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), 
ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME 
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING  
TECHNOLOGY (IJEET) 
ISSN 0976 ā€“ 6545(Print) 
ISSN 0976 ā€“ 6553(Online) 
Volume 5, Issue 7, July (2014), pp. 32-44 
Ā© IAEME: www.iaeme.com/IJEET.asp 
Journal Impact Factor (2014): 6.8310 (Calculated by GISI) 
www.jifactor.com 
IJEET 
Ā© I A E M E 
DETECTION CLASSIFICATION AND LOCATION OF FAULTS ON 220 KV 
TRANSMISSION LINE USING WAVELET TRANSFORM AND NEURAL 
NETWORK 
R.P. Hasabe, A.P. Vaidya 
Electrical Engineering Department, 
Walchand College of Engineering, Sangli, Maharashtra. India 
32 
ABSTRACT 
This paper presents a discrete wavelet transform and neural network approach to fault 
detection and classification and location in transmission lines. The fault detection is carried out by 
using energy of the detail coefficients of the phase signals and artificial neutral network algorithm 
used for fault type classification and fault distance location for all the types of faults for 220 KV 
transmission line. The energies of the all three phases A, B, C and ground phase are given in put to 
the neural network for the fault classification. For each type of fault separate neural network is 
prepared for finding out the fault location. An improved performance is obtained once the neutral 
network is trained suitably, thus performance correctly when faced with different system parameters 
and conditions. 
Index Terms: Fault Detection, Fault Classification, Wavelet Transform. 
I. INTRODUCTION 
Transmission lines are a crucial part of an electrical power system as they allow bulk energy 
to be transported from a group of generating units to an area where the energy is needed. Protecting 
of transmission lines is one of the important tasks to safeguard electric power systems. For safe 
operation of transmission line systems, the protection system need to be able to detect, classify, 
locate accurately and clear the fault as fast as possible to maintain stability in the network. The 
occurrence of any transmission line faults gives rise to transient condition. Fourier transform gives 
information about all frequencies that are present in the signal but does not give any information 
about the time at which these frequencies were present. Wavelet transform has the advantage of fast 
response and increased accuracy as compared to conventional techniques. The wavelet 
transformation is a tool which helps the signal to be analyzed in time as well as frequency domain
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), 
ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME 
effectively. It uses short windows at high frequencies, long windows at low frequencies. Using multi 
resolution analysis a particular band of frequencies present in the signal can be analyzed. The 
detection of fault is carried out by the analysis of the wavelets coefficients energy related to phase 
currents. ANN based techniques have been used in power system protection and encouraging results 
are obtained [1], [2], [3]. Neural networks are used for different applications as classification, pattern 
recognition. In classification, the objective is to assign the input patterns to one of the different 
classes [4], [5]. Fault location in a transmission line using synchronized phasor measurements has 
been studied for a long time. Some selected papers are listed as [6]ā€“[10]. Takagi et al. [6] use current 
and voltage phasors from one terminal for their method based on reactive power. Girgis et al. [7], 
Abe et al. [8], Jiang et al. [9] and Gopalakrishnan et al. [10] use voltage and current phasors from 
both ends. 
In this paper a scheme is propose for 220KV transmission line for fast and reliable fault 
detection using energy of the detail coefficients of the phase signals, classification and location using 
neural network. For fault classification current signals (Ia2, Ib2, Ic2, and IG) detail coefficients 
energy values are given as input to the neural network. For each type of fault location separate neural 
network with different combination of input signals are prepared. In each of these cases, the current, 
voltage and ground phase current signals detail coefficients energies values of only phase involving 
in the fault signals are given as input to the neural network. The MATLAB 7.10 version is used to 
generate the fault signals and verify the correctness of the algorithm. The proposed scheme is 
insensitive to variation of different parameters such as fault type, fault resistance etc. 
33 
II. DISCRETE WAVELET TRANSFORM 
Discrete Wavelet Transform is found to be useful in analyzing transient phenomenon such as 
that associated with faults on the transmission lines. The fault signals are generally non stationary 
signals, any change may spread all over the frequency axis. The wavelet transform technique is well 
suited to wide band signals that may not be periodic and may contain both sinusoidal and non 
sinusoidal components. Multi-Resolution Analysis (MRA) is one of the tools of Discrete Wavelet 
Transform (D.W.T), which decomposes original, typically non-stationary signal into low frequency 
signals called approximations and high frequency signals called details, with different levels or 
scales of resolution. The use of detail coefficients for fault detection is discussed in this paper. 
Detail coefficients contain information about the fault, which is required for fault detection. 
Fig.1: Wavelet filter Bank 
In the first decomposition, signal is decomposed into D1 and A1, the frequency band of D1 
and A1 is
respectively where the sampling frequency is
. The signal 
of desired frequency component can be obtained from repetitive decompositions as shown by Fig.1. 
The mother wavelet determines the filters used to analyze signals. In this paper Db4 (Daubechies 4) 
wavelet was chosen because of its success in detecting faults [4], [5].
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), 
ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME 
P Q 
Load 
G 90 km 
Load 
Load3 
Load4 
1. Generator 500MVA, 13.8kv, 50Hz, synchronous 
generator pu model 
2. Transformer1 13.8kv/220kv, 500MVA. 
3. Transfomer2 220kv/13.8kv, 500MVA. 
4. Load1 50MW, 220kv, 50MW, 1Mvar, RL load. 
5. Load2 50MW, 220kv, 50MW, 1MVar, RL load 
6. Load3 13.8kv, 40MW, RL load 
7. Load4 13.8kv, 40MW, RL load 
8 Transmission line Length=90 km. 
34 
III. ARTIFICIAL NEURAL NETWORKS 
Artificial Neural Networks simulate the natural systems behavior by means of the intercon-nection 
of basic processing units called neurons. ANNs have a high degree of robustness and ability 
to learn [8]. Once the network is trained, it is able to properly resolve the different situations that are 
different from those presented in the learning process. The multilayered feed forward network has 
the ability of handling complex and nonlinear input-output relationship with hidden layers. In this 
method, errors are propagated backwards; the idea of back- propagation algorithm is to reduce errors 
until the ANN learns the training data [13] [14]. The multilayered feed forward network has been 
chosen to process the prepared input data obtained from the W.T. 
IV. TRANSMISSION LINE MODEL 
In Fig.2, model of 220kv, 90 km transmission line from P to Q is chosen. Generator of 
500MW is connected at one end and 4 loads are connected at 13.8kv and 220kv. 
Fig.2: Transmission Line Single Line Model 
TABLE I: MODEL PARAMETERS 
Various faults are simulated on that line by varying various parameters. Ratings of power 
system model are shown in Table I. As shown in Fig.2 a transmission line model is prepared in 
MATLAB7.10. The transmission line positive and zero sequence parameters are R1=0.10809/km, 
R0=0.2188/km, L1=0.00092H/km, L0=0.0032H/km, C1=1.25*
, C0=7.85*
f/km. 
The distributed parameter model of transmission line is considered for analysis. The current signals 
are sampled at sampling frequency of 20 kHz.
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 
ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 
V. DESIGN OF FAULT DETECTION 
32-44 Ā© IAEME 
DETECTION, CLASSIFICATION AND LOCATION 
The design process of proposed fault detection, classification and location approach is 
shown in Fig.3 Combination of different fault conditions are to be considered and training patterns are 
required to be generated by simulating different kinds k 
of faults on the power system. The fault 
resistance, fault location, and fault type are changed to generate different training patterns. 
Data acquisition of current signals 
D.W.T multiresolution analysis, 
. 
calculation, fault detection based on energy 
ANN based classification and Location of 
Fig.3: Process of fault detection 
VI. FAULT DETECTION 
detection, classification and Location 
The signals taken from the scope are 
filtered, sampled at 20 kHz sampling frequency. Then 
DWT is applied up to level 5, and detail coefficients 
detail coefficients energy is calculated. 
amount of energy than the level 4 [11], 
taken and decomposition is done and 
data window. As the fault signals contain the high 
signal increases at the occurrence of fault as shown in F 
Here, for detecting the fault, 
considered. The energy of detail coefficients for a 
Where, k=window number, l=level of the DWT, N=length of Detail coefficients at level l. 
accurately detecting the 
Fig. 4: Energy of the detail level 5 vs. window number 
35 
and approximate coefficients are calculated and 
Then, we come to know that detail level 5 contains highest 
[12]. A moving data window of one cycle ( 
energy of the details coefficients at level 5 is 
amount of harmonic components, the 
ccurrence Fig.4 
, difference of energies between two adjacent windows 
. window is given by equation (1), 
(1) 
Energy 
Feature extraction 
faults 
ā€“ 6545(Print), 
as 
inds 400 samples) is 
obtained for each 
energy of the 
has been 
For
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 
ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 
Red 
Green 
Black 
Blue 
32-44 Ā© IAEME 
Fig. 5: F.D index for single line to ground fault vs. window number 
presence of faults, the difference between the two consecutive energies of the moving windows is 
calculated by (2) and shown in Fig.5. 
F.D (k) = 
In this sampling frequency of 20 kHz 
window slides taking only 1 new sample 
cycle corresponds to nearly 400 data samples 
The fault is present on R-phase and ground 
phase, green colour shows the ground 
shows the B phase.The Fault Detection value 
data windows, and then decision is made whether f 
Fault Detection values the faults can be 
threshold values are set and the fault detection is achieved. The transient energy is present mainly 
during fault inception and clearing. The high frequency content energy is smaller than the low 
frequency content energy of the current signals. 
VII. ANN BASED FAULT CLASSIFICATION 
All different faults are simulated for different conditions and 
from the energy values of the detail coefficients. The 4 
selected. The two hidden layers are 
network is selected. The average value 
of fault are given as input to the neural network, along with the three lines energies, zero sequence 
current energy is also given as fourth input to t 
the three phases, if fault is present it is shown by the presence of ā€˜1 
Similarly fourth output indicates the 
by the presence of ā€˜1ā€™, otherwise it is presented b 
different training patterns is done as shown in Table 
36 
number. 
. 
F.D (k-1) + [Ed (k) - Ed (k - 400)] (2) 
gives 400 samples for each cycle of 20ms. 
at each move and keeping 399 previous 
ponds samples. 
(G) for the present case. Red colour shows the R 
reen (G) phase, black colour represents the Y phase and blue colour 
is compared with threshold value for consecutive 
fault is permanent or temporary 
accurately detected [7]. For different phases diffe 
ld training patterns are generate 
input neurons and 4 output neurons are 
s selected. Feed forward multilayer back propagation neural 
values of energies of current signals, half cycle after the occurrence 
the neural network. Three outputs show 
1ā€™, otherwise with presence of ā€˜0 
ground fault. If ground is involved in the fault will be indicated 
by ā€˜0ā€™. This is shown in Table 
ns III. 
ā€“ 6545(Print), 
. 
Here, moving 
samples. So one 
10 
ault temporary. By using these 
different 
generated 
the statuses of 
ā€™, 0ā€™. 
round II. Generation of
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), 
ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME 
TABLE II 
TARGET OUTPUTS 
Fault Type A B C G 
AG 1 0 0 1 
BG 0 1 0 1 
CG 0 0 1 1 
AB 1 1 0 0 
BC 0 1 1 0 
CA 1 0 1 0 
ABG 1 1 0 1 
BCG 0 1 1 1 
CAG 1 0 1 1 
ABC 1 1 1 0 
TABLE III 
TRAINING PATTERNS 
Type of fault LG, LLG, LL, LLL. 
Location of fault (%) 
from busbar P. 
20,30,40,50,60,70,80 
Fault resistance 5,10,15,20 . 
For training neural network different fault conditions are simulated, features are extracted and 
network is trained. At 7 different locations on the transmission line fault is created, at 20, 30, 40, 50, 
60, 70, 80% of the transmission line length from the sending end, 4 different values of fault resis-tances 
can be used and total 10 different faults are created, and this gives 7*4*10=280 cases for 
37 
training neural network. 
The different training algorithms are presented to train the neural network; they use the 
gradient of the performance function to determine how to adjust the weights to minimize a 
performance function. The gradient is determined using back propagation technique, which involves 
performing computations backwards through the network. A variation of back propagation algorithm 
called Levenberg-Marquardt (LM) algorithm was used for neural network training, since it is one of 
the fastest methods for training moderate-sized feed forward neural networks. 
LM algorithm to weight update is given by (3), 
      Ī¼      ! (3) 
Where J is Jacobean matrix that contains first derivatives of the network error with respect to 
the weights and biases, e is a vector of network errors.is an approximation of the Hessian 
Matrix, ! is the gradient and  is the scalar affecting performance function. LM algorithm based 
method for training neural network is much faster than the other methods. Fig.6 shows the 
Multilayered Feed forward Neural Network (M.F.N.N.)
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 
ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 
1 
4 
Ea 
. 
. 
. 
En 
32-44 Ā© IAEME 
Fig.6: Multilayer feed forward network for fault classification 
Fig.7: 4-22-22 
Network with 2 hidden layers worked out to be better than the 1 
4-22-22-4 configuration give better results than the 4 
functions used for the hidden layers 
respectively. The Fig.7 shows the neural network. 
The data used for training data division is done randomly; training function used is LM 
algorithm. Performance function used is Mean least square error 
chosen is . Fig.8 shows the performance curve. F 
we cannot distinguish between the faults with ground 
VIII. TEST RESULTS 
A validation data set consisti 
line model shown in Fig.2. The validation test patterns were different than they were used for the 
training of the neural network .For different faults on the model system 
fault resistance values are changed to 
proposed algorithm. Test results are as shown in 
network for varying fault location values and 
The output layer activation function used is ā€˜Purelinā€™, because of its success in the 
classification of faults correctly. The tansig and logsig transfer functions did not show a good 
classification capability. The output layer transfer function is fixed at 
transfer function was changed. 
If the transfer functions of the hidden layers I and II are chosen as 1) Tansig 
Logsig. 3) Tansig-Logsig, the Table V test result shows that the accuracy obtained with the Ta 
38 
1 
2 
3 
22 
1 
22 
1 
4 
22-4 ā€˜Tansigā€™, ā€˜Logsigā€™, ā€˜Purelinā€™ configuration 
hidden 
4-22-4, 4-10-4 configurations. Activation 
I, II and output layer are ā€˜tansigā€™, ā€˜logsigā€™ 
Fig.8: Performance curve. 
method. The performance goal 
For network configurations 4- 
without ground. 
consisting of different fault types was generated using the 
system, fault type; fault location and 
investigate the effects of these factors on the performance of the 
Table IV. These results show the accuracy of neural 
varying fault resistance value. 
ā€˜Purelinā€™ and the hidden layer 
Tansig-Tansig. 2) Logsig 
A 
. 
. 
. 
. 
G 
I II 
ā€“ 6545(Print), 
layer network. 
and ā€˜purelinā€™ 
-22-4 and 4-10-4, 
transmission 
Logsig- 
Tansig-
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), 
ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME 
Logsig type of neural network is more and it is having good generalization capability. The 
classification results for almost all types of faults are satisfactory. 
TABLE IV 
TESTING RESULTS 
TABLE V 
Fault 
Resistance 
. 
COMPARISON OF TRANSFER FUNCTIONS 
39 
Fault 
type 
Fault 
Location 
from P(%) 
Transfer Functions for 
hidden layers. 
No. neurons in hidden 
layers 
Tansig-tansig. 
22-22 
Logsig-logsig. 
22-22 
Tansig-logsig. 
22-22. 
Performance error of test 
results 
2.9*10^(-7) 5.5*10^(-7) 5.39*10^ (-8). 
IX. ANN BASED FAULT DISTANCE LOCATOR 
In this paper single line to Ground fault locator explains in detail. 
SINGLE LINE TO GROUND FAULTS LOCATOR 
A. Selecting the right architecture 
One factor in determining the right size and structure for the network is the number of inputs 
and outputs that it must have. However, sufficient input data to characterize the problem must be 
ensured. The network inputs chosen here are the magnitudes of the detail coefficients energies 
fundamental components (50 Hz) of phase voltages and currents measured (AG-Ia2, Va2, IG, 
BG-Ib2, Vb2, IG,) at the relay location. As the basic task of fault location is to determine the 
distance to the fault, the distance to the fault, in km with regard to the total length of the line, should 
be the only output provided by the fault location network. Thus the input and the output for the fault 
location network are: 
Input = different combinations of Va2, Vb2, Vc2, Ia2, Ib2, Ic2 
and IG as per faults. (1) 
Output Lf = Fault distance in KM. (2) 
Output of neurons 
A B C G 
AG 30% 10 1.0001 2*10^-3 4*10^-3 1.00 
BCG 50% 15 0 1.00 0.9989 1.00 
CAG 50% 10 1 0 1.00 0.998 
CG 50% 10 1*10^-3 0.000 1.00 1.00 
ABC 30% 10 1.00 1.00 0.999 0.00 
ACG 70% 5 0.9996 -3*10^-4 0.997 1.000 
AB 70% 5 1.018 1.0847 0.1587 0.052
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), 
ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME 
For each type of fault separate neural network is prepared for finding out the fault location. 
The ANN architecture, including the number of inputs to the network and the number of neurons in 
hidden layers, is determined empirically by experimenting with various network configurations. 
Through a series of trial and error, and modifications of the ANN architecture, the best performance 
is achieved by using a four layer neural network with 3 inputs and 1 output as shown in Fig. 9. The 
number of neurons for the hidden layer is 10 and 5. The final determination of the neural network 
requires the relevant transfer functions in the layers to be established. After analysing the various 
possible combinations of transfer functions normally used, such as logsig, tansig and linear 
functions, the tansig function was chosen as transfer function for the hidden layer, and pure linear 
function ā€œpurelinā€ in the output layer. 
40 
1 
3 
1 
2 
3 
10 
1 
5 
1 
Ia2 
Va2 
IG 
. 
. 
. 
. 
. 
Fig.9: Structure of the chosen ANN with configuration for LG fault 
B. Learning rule selection 
The back-propagation learning rule can be used to adjust the weights and biases of networks to 
minimize the sum-squared error of the network. The simple back-propagation method is slow 
because it requires small learning rates for stable learning, improvement techniques such as momen-tum 
and adaptive learning rate or an alternative method to gradient descent, Levenbergā€“Marquardt 
optimisation, can be used. Various techniques were applied to the different network architectures, 
and it was concluded that the most suitable training method for the architecture selected was based 
on the Levenbergā€“Marquardt (Trainlm) optimization technique. 
C. Training process 
To train the network, a suitable number of representative examples of the relevant phenome-non 
must be selected so that the network can learn the fundamental characteristics of the problem 
and, once training is completed, provide correct outputs in new situations not used during training. 
To obtain enough examples to train the network, a software package MATLABĀ® 7.10 is used. Using 
SIMULINK  SIMPOWER SYSTEM toolbox of MATLAB all the ten types of fault at different 
fault locations between 0-100% of line length and different fault resistance have been simulated as 
shown below in Table VI. Feed forward back-propagation network have been surveyed for the 
purpose of single line-ground fault location, mainly because of the availability of the sufficient 
relevant data for training. In order to train the neural network, several single phase faults have been 
simulated on transmission line model. For each of the three phases, faults have been simulated at 
every 10 km on a 90 km transmission line. Total of 648 cases have been simulated with different 
fault resistances 1, 2, 3 ohms respectively. In each of these cases, the current and voltage signals 
detail coefficients energies of only phase involving in the fault and ground phase current signals 
given as input to the neural network such as Ia2, Ib2, Ic2,Va2 ,Vb2, Vc2 and IG. The output of the 
neural network is the distance to the fault from the sending end of the transmission line. 
The ANN based fault distance locator was trained using Levenbergā€“Marquardt training 
algorithm using neural network toolbox of Matlab as shown in Fig. 10. 
Lf 
II 
I 
. 
.
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), 
ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME 
TABLE VI: TRAINING PATTERNS GENERATION 
41 
Sr. 
No. 
Parameter Set value 
1 Fault type LG: AG-Ia2, Va2, IG BG- Ib2, Vb2, IG 
CG -Ic2, Vc2, IG 
LL: AB- Ia2, Va2, Ib2, Vb2, 
LLG: ABG -Ia2, Va2, Ib2, Vb2, IG 
LLL: ABC- Ia2, Va2, Ib2, Vb2, Ic2, Vc2 
LLLG:ABCG- Ia2, Va2, Ib2, Vb2, Ic2, Vc2, IG 
2 Fault location 
(Lf in KM) 
10, 20, 30, ā€¦80 and 90 km 
3 
Fault resistance 
(Rf) 
1, 2, 3 ohm 
Once the network is trained sufficiently and suitably with large training data sets, the network 
gives the correct output after one cycle from the inception of fault. 
Fig. 10: Overview of the chosen ANN (3-10-5-1) 
Fig. 11 plots the mean square error as a function of time during the learning process and it 
can be seen that the achieved MSE is about 2.61. 
Fig.11: MSE performance of the Network with configuration (3-10-5-1)

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40220140507004

  • 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING TECHNOLOGY (IJEET) ISSN 0976 ā€“ 6545(Print) ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME: www.iaeme.com/IJEET.asp Journal Impact Factor (2014): 6.8310 (Calculated by GISI) www.jifactor.com IJEET Ā© I A E M E DETECTION CLASSIFICATION AND LOCATION OF FAULTS ON 220 KV TRANSMISSION LINE USING WAVELET TRANSFORM AND NEURAL NETWORK R.P. Hasabe, A.P. Vaidya Electrical Engineering Department, Walchand College of Engineering, Sangli, Maharashtra. India 32 ABSTRACT This paper presents a discrete wavelet transform and neural network approach to fault detection and classification and location in transmission lines. The fault detection is carried out by using energy of the detail coefficients of the phase signals and artificial neutral network algorithm used for fault type classification and fault distance location for all the types of faults for 220 KV transmission line. The energies of the all three phases A, B, C and ground phase are given in put to the neural network for the fault classification. For each type of fault separate neural network is prepared for finding out the fault location. An improved performance is obtained once the neutral network is trained suitably, thus performance correctly when faced with different system parameters and conditions. Index Terms: Fault Detection, Fault Classification, Wavelet Transform. I. INTRODUCTION Transmission lines are a crucial part of an electrical power system as they allow bulk energy to be transported from a group of generating units to an area where the energy is needed. Protecting of transmission lines is one of the important tasks to safeguard electric power systems. For safe operation of transmission line systems, the protection system need to be able to detect, classify, locate accurately and clear the fault as fast as possible to maintain stability in the network. The occurrence of any transmission line faults gives rise to transient condition. Fourier transform gives information about all frequencies that are present in the signal but does not give any information about the time at which these frequencies were present. Wavelet transform has the advantage of fast response and increased accuracy as compared to conventional techniques. The wavelet transformation is a tool which helps the signal to be analyzed in time as well as frequency domain
  • 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME effectively. It uses short windows at high frequencies, long windows at low frequencies. Using multi resolution analysis a particular band of frequencies present in the signal can be analyzed. The detection of fault is carried out by the analysis of the wavelets coefficients energy related to phase currents. ANN based techniques have been used in power system protection and encouraging results are obtained [1], [2], [3]. Neural networks are used for different applications as classification, pattern recognition. In classification, the objective is to assign the input patterns to one of the different classes [4], [5]. Fault location in a transmission line using synchronized phasor measurements has been studied for a long time. Some selected papers are listed as [6]ā€“[10]. Takagi et al. [6] use current and voltage phasors from one terminal for their method based on reactive power. Girgis et al. [7], Abe et al. [8], Jiang et al. [9] and Gopalakrishnan et al. [10] use voltage and current phasors from both ends. In this paper a scheme is propose for 220KV transmission line for fast and reliable fault detection using energy of the detail coefficients of the phase signals, classification and location using neural network. For fault classification current signals (Ia2, Ib2, Ic2, and IG) detail coefficients energy values are given as input to the neural network. For each type of fault location separate neural network with different combination of input signals are prepared. In each of these cases, the current, voltage and ground phase current signals detail coefficients energies values of only phase involving in the fault signals are given as input to the neural network. The MATLAB 7.10 version is used to generate the fault signals and verify the correctness of the algorithm. The proposed scheme is insensitive to variation of different parameters such as fault type, fault resistance etc. 33 II. DISCRETE WAVELET TRANSFORM Discrete Wavelet Transform is found to be useful in analyzing transient phenomenon such as that associated with faults on the transmission lines. The fault signals are generally non stationary signals, any change may spread all over the frequency axis. The wavelet transform technique is well suited to wide band signals that may not be periodic and may contain both sinusoidal and non sinusoidal components. Multi-Resolution Analysis (MRA) is one of the tools of Discrete Wavelet Transform (D.W.T), which decomposes original, typically non-stationary signal into low frequency signals called approximations and high frequency signals called details, with different levels or scales of resolution. The use of detail coefficients for fault detection is discussed in this paper. Detail coefficients contain information about the fault, which is required for fault detection. Fig.1: Wavelet filter Bank In the first decomposition, signal is decomposed into D1 and A1, the frequency band of D1 and A1 is
  • 3. respectively where the sampling frequency is . The signal of desired frequency component can be obtained from repetitive decompositions as shown by Fig.1. The mother wavelet determines the filters used to analyze signals. In this paper Db4 (Daubechies 4) wavelet was chosen because of its success in detecting faults [4], [5].
  • 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME P Q Load G 90 km Load Load3 Load4 1. Generator 500MVA, 13.8kv, 50Hz, synchronous generator pu model 2. Transformer1 13.8kv/220kv, 500MVA. 3. Transfomer2 220kv/13.8kv, 500MVA. 4. Load1 50MW, 220kv, 50MW, 1Mvar, RL load. 5. Load2 50MW, 220kv, 50MW, 1MVar, RL load 6. Load3 13.8kv, 40MW, RL load 7. Load4 13.8kv, 40MW, RL load 8 Transmission line Length=90 km. 34 III. ARTIFICIAL NEURAL NETWORKS Artificial Neural Networks simulate the natural systems behavior by means of the intercon-nection of basic processing units called neurons. ANNs have a high degree of robustness and ability to learn [8]. Once the network is trained, it is able to properly resolve the different situations that are different from those presented in the learning process. The multilayered feed forward network has the ability of handling complex and nonlinear input-output relationship with hidden layers. In this method, errors are propagated backwards; the idea of back- propagation algorithm is to reduce errors until the ANN learns the training data [13] [14]. The multilayered feed forward network has been chosen to process the prepared input data obtained from the W.T. IV. TRANSMISSION LINE MODEL In Fig.2, model of 220kv, 90 km transmission line from P to Q is chosen. Generator of 500MW is connected at one end and 4 loads are connected at 13.8kv and 220kv. Fig.2: Transmission Line Single Line Model TABLE I: MODEL PARAMETERS Various faults are simulated on that line by varying various parameters. Ratings of power system model are shown in Table I. As shown in Fig.2 a transmission line model is prepared in MATLAB7.10. The transmission line positive and zero sequence parameters are R1=0.10809/km, R0=0.2188/km, L1=0.00092H/km, L0=0.0032H/km, C1=1.25*
  • 6. f/km. The distributed parameter model of transmission line is considered for analysis. The current signals are sampled at sampling frequency of 20 kHz.
  • 7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. V. DESIGN OF FAULT DETECTION 32-44 Ā© IAEME DETECTION, CLASSIFICATION AND LOCATION The design process of proposed fault detection, classification and location approach is shown in Fig.3 Combination of different fault conditions are to be considered and training patterns are required to be generated by simulating different kinds k of faults on the power system. The fault resistance, fault location, and fault type are changed to generate different training patterns. Data acquisition of current signals D.W.T multiresolution analysis, . calculation, fault detection based on energy ANN based classification and Location of Fig.3: Process of fault detection VI. FAULT DETECTION detection, classification and Location The signals taken from the scope are filtered, sampled at 20 kHz sampling frequency. Then DWT is applied up to level 5, and detail coefficients detail coefficients energy is calculated. amount of energy than the level 4 [11], taken and decomposition is done and data window. As the fault signals contain the high signal increases at the occurrence of fault as shown in F Here, for detecting the fault, considered. The energy of detail coefficients for a Where, k=window number, l=level of the DWT, N=length of Detail coefficients at level l. accurately detecting the Fig. 4: Energy of the detail level 5 vs. window number 35 and approximate coefficients are calculated and Then, we come to know that detail level 5 contains highest [12]. A moving data window of one cycle ( energy of the details coefficients at level 5 is amount of harmonic components, the ccurrence Fig.4 , difference of energies between two adjacent windows . window is given by equation (1), (1) Energy Feature extraction faults ā€“ 6545(Print), as inds 400 samples) is obtained for each energy of the has been For
  • 8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. Red Green Black Blue 32-44 Ā© IAEME Fig. 5: F.D index for single line to ground fault vs. window number presence of faults, the difference between the two consecutive energies of the moving windows is calculated by (2) and shown in Fig.5. F.D (k) = In this sampling frequency of 20 kHz window slides taking only 1 new sample cycle corresponds to nearly 400 data samples The fault is present on R-phase and ground phase, green colour shows the ground shows the B phase.The Fault Detection value data windows, and then decision is made whether f Fault Detection values the faults can be threshold values are set and the fault detection is achieved. The transient energy is present mainly during fault inception and clearing. The high frequency content energy is smaller than the low frequency content energy of the current signals. VII. ANN BASED FAULT CLASSIFICATION All different faults are simulated for different conditions and from the energy values of the detail coefficients. The 4 selected. The two hidden layers are network is selected. The average value of fault are given as input to the neural network, along with the three lines energies, zero sequence current energy is also given as fourth input to t the three phases, if fault is present it is shown by the presence of ā€˜1 Similarly fourth output indicates the by the presence of ā€˜1ā€™, otherwise it is presented b different training patterns is done as shown in Table 36 number. . F.D (k-1) + [Ed (k) - Ed (k - 400)] (2) gives 400 samples for each cycle of 20ms. at each move and keeping 399 previous ponds samples. (G) for the present case. Red colour shows the R reen (G) phase, black colour represents the Y phase and blue colour is compared with threshold value for consecutive fault is permanent or temporary accurately detected [7]. For different phases diffe ld training patterns are generate input neurons and 4 output neurons are s selected. Feed forward multilayer back propagation neural values of energies of current signals, half cycle after the occurrence the neural network. Three outputs show 1ā€™, otherwise with presence of ā€˜0 ground fault. If ground is involved in the fault will be indicated by ā€˜0ā€™. This is shown in Table ns III. ā€“ 6545(Print), . Here, moving samples. So one 10 ault temporary. By using these different generated the statuses of ā€™, 0ā€™. round II. Generation of
  • 9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME TABLE II TARGET OUTPUTS Fault Type A B C G AG 1 0 0 1 BG 0 1 0 1 CG 0 0 1 1 AB 1 1 0 0 BC 0 1 1 0 CA 1 0 1 0 ABG 1 1 0 1 BCG 0 1 1 1 CAG 1 0 1 1 ABC 1 1 1 0 TABLE III TRAINING PATTERNS Type of fault LG, LLG, LL, LLL. Location of fault (%) from busbar P. 20,30,40,50,60,70,80 Fault resistance 5,10,15,20 . For training neural network different fault conditions are simulated, features are extracted and network is trained. At 7 different locations on the transmission line fault is created, at 20, 30, 40, 50, 60, 70, 80% of the transmission line length from the sending end, 4 different values of fault resis-tances can be used and total 10 different faults are created, and this gives 7*4*10=280 cases for 37 training neural network. The different training algorithms are presented to train the neural network; they use the gradient of the performance function to determine how to adjust the weights to minimize a performance function. The gradient is determined using back propagation technique, which involves performing computations backwards through the network. A variation of back propagation algorithm called Levenberg-Marquardt (LM) algorithm was used for neural network training, since it is one of the fastest methods for training moderate-sized feed forward neural networks. LM algorithm to weight update is given by (3), Ī¼ ! (3) Where J is Jacobean matrix that contains first derivatives of the network error with respect to the weights and biases, e is a vector of network errors.is an approximation of the Hessian Matrix, ! is the gradient and is the scalar affecting performance function. LM algorithm based method for training neural network is much faster than the other methods. Fig.6 shows the Multilayered Feed forward Neural Network (M.F.N.N.)
  • 10. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 1 4 Ea . . . En 32-44 Ā© IAEME Fig.6: Multilayer feed forward network for fault classification Fig.7: 4-22-22 Network with 2 hidden layers worked out to be better than the 1 4-22-22-4 configuration give better results than the 4 functions used for the hidden layers respectively. The Fig.7 shows the neural network. The data used for training data division is done randomly; training function used is LM algorithm. Performance function used is Mean least square error chosen is . Fig.8 shows the performance curve. F we cannot distinguish between the faults with ground VIII. TEST RESULTS A validation data set consisti line model shown in Fig.2. The validation test patterns were different than they were used for the training of the neural network .For different faults on the model system fault resistance values are changed to proposed algorithm. Test results are as shown in network for varying fault location values and The output layer activation function used is ā€˜Purelinā€™, because of its success in the classification of faults correctly. The tansig and logsig transfer functions did not show a good classification capability. The output layer transfer function is fixed at transfer function was changed. If the transfer functions of the hidden layers I and II are chosen as 1) Tansig Logsig. 3) Tansig-Logsig, the Table V test result shows that the accuracy obtained with the Ta 38 1 2 3 22 1 22 1 4 22-4 ā€˜Tansigā€™, ā€˜Logsigā€™, ā€˜Purelinā€™ configuration hidden 4-22-4, 4-10-4 configurations. Activation I, II and output layer are ā€˜tansigā€™, ā€˜logsigā€™ Fig.8: Performance curve. method. The performance goal For network configurations 4- without ground. consisting of different fault types was generated using the system, fault type; fault location and investigate the effects of these factors on the performance of the Table IV. These results show the accuracy of neural varying fault resistance value. ā€˜Purelinā€™ and the hidden layer Tansig-Tansig. 2) Logsig A . . . . G I II ā€“ 6545(Print), layer network. and ā€˜purelinā€™ -22-4 and 4-10-4, transmission Logsig- Tansig-
  • 11. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME Logsig type of neural network is more and it is having good generalization capability. The classification results for almost all types of faults are satisfactory. TABLE IV TESTING RESULTS TABLE V Fault Resistance . COMPARISON OF TRANSFER FUNCTIONS 39 Fault type Fault Location from P(%) Transfer Functions for hidden layers. No. neurons in hidden layers Tansig-tansig. 22-22 Logsig-logsig. 22-22 Tansig-logsig. 22-22. Performance error of test results 2.9*10^(-7) 5.5*10^(-7) 5.39*10^ (-8). IX. ANN BASED FAULT DISTANCE LOCATOR In this paper single line to Ground fault locator explains in detail. SINGLE LINE TO GROUND FAULTS LOCATOR A. Selecting the right architecture One factor in determining the right size and structure for the network is the number of inputs and outputs that it must have. However, sufficient input data to characterize the problem must be ensured. The network inputs chosen here are the magnitudes of the detail coefficients energies fundamental components (50 Hz) of phase voltages and currents measured (AG-Ia2, Va2, IG, BG-Ib2, Vb2, IG,) at the relay location. As the basic task of fault location is to determine the distance to the fault, the distance to the fault, in km with regard to the total length of the line, should be the only output provided by the fault location network. Thus the input and the output for the fault location network are: Input = different combinations of Va2, Vb2, Vc2, Ia2, Ib2, Ic2 and IG as per faults. (1) Output Lf = Fault distance in KM. (2) Output of neurons A B C G AG 30% 10 1.0001 2*10^-3 4*10^-3 1.00 BCG 50% 15 0 1.00 0.9989 1.00 CAG 50% 10 1 0 1.00 0.998 CG 50% 10 1*10^-3 0.000 1.00 1.00 ABC 30% 10 1.00 1.00 0.999 0.00 ACG 70% 5 0.9996 -3*10^-4 0.997 1.000 AB 70% 5 1.018 1.0847 0.1587 0.052
  • 12. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME For each type of fault separate neural network is prepared for finding out the fault location. The ANN architecture, including the number of inputs to the network and the number of neurons in hidden layers, is determined empirically by experimenting with various network configurations. Through a series of trial and error, and modifications of the ANN architecture, the best performance is achieved by using a four layer neural network with 3 inputs and 1 output as shown in Fig. 9. The number of neurons for the hidden layer is 10 and 5. The final determination of the neural network requires the relevant transfer functions in the layers to be established. After analysing the various possible combinations of transfer functions normally used, such as logsig, tansig and linear functions, the tansig function was chosen as transfer function for the hidden layer, and pure linear function ā€œpurelinā€ in the output layer. 40 1 3 1 2 3 10 1 5 1 Ia2 Va2 IG . . . . . Fig.9: Structure of the chosen ANN with configuration for LG fault B. Learning rule selection The back-propagation learning rule can be used to adjust the weights and biases of networks to minimize the sum-squared error of the network. The simple back-propagation method is slow because it requires small learning rates for stable learning, improvement techniques such as momen-tum and adaptive learning rate or an alternative method to gradient descent, Levenbergā€“Marquardt optimisation, can be used. Various techniques were applied to the different network architectures, and it was concluded that the most suitable training method for the architecture selected was based on the Levenbergā€“Marquardt (Trainlm) optimization technique. C. Training process To train the network, a suitable number of representative examples of the relevant phenome-non must be selected so that the network can learn the fundamental characteristics of the problem and, once training is completed, provide correct outputs in new situations not used during training. To obtain enough examples to train the network, a software package MATLABĀ® 7.10 is used. Using SIMULINK SIMPOWER SYSTEM toolbox of MATLAB all the ten types of fault at different fault locations between 0-100% of line length and different fault resistance have been simulated as shown below in Table VI. Feed forward back-propagation network have been surveyed for the purpose of single line-ground fault location, mainly because of the availability of the sufficient relevant data for training. In order to train the neural network, several single phase faults have been simulated on transmission line model. For each of the three phases, faults have been simulated at every 10 km on a 90 km transmission line. Total of 648 cases have been simulated with different fault resistances 1, 2, 3 ohms respectively. In each of these cases, the current and voltage signals detail coefficients energies of only phase involving in the fault and ground phase current signals given as input to the neural network such as Ia2, Ib2, Ic2,Va2 ,Vb2, Vc2 and IG. The output of the neural network is the distance to the fault from the sending end of the transmission line. The ANN based fault distance locator was trained using Levenbergā€“Marquardt training algorithm using neural network toolbox of Matlab as shown in Fig. 10. Lf II I . .
  • 13. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME TABLE VI: TRAINING PATTERNS GENERATION 41 Sr. No. Parameter Set value 1 Fault type LG: AG-Ia2, Va2, IG BG- Ib2, Vb2, IG CG -Ic2, Vc2, IG LL: AB- Ia2, Va2, Ib2, Vb2, LLG: ABG -Ia2, Va2, Ib2, Vb2, IG LLL: ABC- Ia2, Va2, Ib2, Vb2, Ic2, Vc2 LLLG:ABCG- Ia2, Va2, Ib2, Vb2, Ic2, Vc2, IG 2 Fault location (Lf in KM) 10, 20, 30, ā€¦80 and 90 km 3 Fault resistance (Rf) 1, 2, 3 ohm Once the network is trained sufficiently and suitably with large training data sets, the network gives the correct output after one cycle from the inception of fault. Fig. 10: Overview of the chosen ANN (3-10-5-1) Fig. 11 plots the mean square error as a function of time during the learning process and it can be seen that the achieved MSE is about 2.61. Fig.11: MSE performance of the Network with configuration (3-10-5-1)
  • 14. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME TEST RESULT OF ANN BASED FAULT DISTANCE LOCATOR Once training was completed, ANN based Fault distance locator was then extensively tested using independent data sets consisting of fault scenarios never used previously in training. For different faults of the validation/test data set, fault type, fault location, and fault resistance were changed to investigate the effects of these factors on the performance of the proposed algorithm. The network was tested and performance was validated by presenting all the ten types of fault cases with varying fault locations (Lf=0-90KM), fault resistances (Rf=1, 2, 3 etc). TABLE VII Percentage errors as a function of fault distance and fault resistance for the ANN 42 chosen for single phase fault location. TABLE VII Fault Distance (Km) Measured Fault Location (Km) Percentage Error (%) RF=1 RF=4 RF=1 RF=4 RF=1 RF=4 9 9 8 7.3 1.11 1.8 18 18 15 15.5 3 2.7 54 54 52 51 2.22 3 63 63 60 57 3 6 72 72 70.5 70 1.6 2 Table VII shows some of the test results of ANN based fault locator under different fault conditions. It can be seen that all results are correct with reasonable accuracy. At various locations different types of LG faults were tested to find out the maximum deviation of the estimated distance Lf measured from the relay location, from the actual fault location La. Then the resulted estimated error ā€œeā€ is expressed as a percentage of total line length L In all the fault cases, the results have shown that the errors in locating the fault are less than 1.11% to +6%. Table VII can show the percentage errors in fault location as a function of Fault Distance and Fault resistance. Different cases are shown with different fault resistances. Thus, the neural network performance is considered satisfactory and can be used for the purpose of single line- ground fault location. X. CONCLUSION In this paper accurate fault detection, classification and location technique is discussed. This technique depends upon the current and voltage signals. The features are extracted from the current and voltage signals by using wavelet transform. The feature vector is then given as input to the neural network. The capabilities of neural network in pattern classification were utilized. Simulation studies were performed and the performance of the scheme with different system parameters and conditions was investigated. The test result shows that the accuracy obtained for fault classification with the ā€œtansig-logsigā€ transfer function for hidden layers I and II is satisfactory. For fault location after analysing the various possible combinations of transfer functions normally used, such as logsig, tansig and linear functions, the tansig function was chosen as transfer function for the hidden layer I and II, and pure linear function ā€œPurelinā€ in the output layer gives satisfactory results.
  • 15. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME 43 REFERENCES [1] K. Gayathri and N. Kumarapan, ā€œComparative Study of Fault Identification and Classifica-tion on EHV Lines Using Discrete Wavelet Transform and Fourier Transform Based ANNā€, World Academy of Science, Engineering and Technology, pp.822-831.2008. [2] D. V. Coury, D. C. Jorge, ā€œArtificial Neural Network Approach To Distance Protection of Transmission Lines.ā€ IEEE Transactions on Power Delivery, Vol. 13, No. 1, January 1998. [3] H. Khorashadi-Zadeh, M. R. Aghaebrahimi, ā€œA Novel Approach to Fault Classification and Fault Location for Medium Voltage Cables Based on Artificial Neural Networkā€, World Academy of Science, Engineering and Technology, pp.1100-1103.2008. [4] V. S. Kale, S. R. Bhide, P. P. Bedekar, ā€œFaulted Phase Selection on Double circuit Transmission Line using Wavelet Transform and Neural Networkā€, Third International Conference On Power Systems, Kharagpur, INDIA, December 27-29. [5] A. Abdollahi, S. Seyedtabaii, ā€œComparison of Fourier Wavelet Transform Methods for Transmission Line Fault Classificationā€, The 4th International Power Engineering and Optimization Conf. (PEOC2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010. [6] K. Takagi, Y. Yomakoshi, M. Yamaura, R. Kondow, and T. Matsushima, ā€œDevelopment of a new type fault locator using the one terminal voltage and current data,ā€ IEEE Trans. Power App. Syst., vol. PAS-101, pp. 2892ā€“2898, Aug. 1982. [7] A. Girgis, D. Hart, andW. Peterson, ā€œA new fault location technique for two- and three-terminal lines,ā€ IEEE Trans. Power Delivery, vol. 7, pp. 98ā€“107, Jan. 1992. [8] M. Abe, T. Emura, N. Otsuzuki, and M. Takeuchi, ā€œDevelopment of a new fault location system for multi-terminal single transmission lines,ā€ IEEE Trans. Power Delivery, vol. 10, pp. 159ā€“168, Jan. 1995. [9] J.-A. Jiang, J.-Z. Yang, Y.-H. Lin, C.-W. Liu, and J.-C. Ma, ā€œAn adaptive PMU based fault detection/location technique for transmission lines part I: Theory and algorithms,ā€ IEEE Trans. Power Delivery, vol. 15, pp. 486ā€“493, Apr. 2000. [10] A. Gopalakrishnan, D. Hamai, M. Kezunovic, and S. McKenna, ā€œFault location using the distributed parameter transmission line model,ā€ IEEE Trans. Power Delivery, vol. 15, pp. 1169ā€“1174, Oct. 2000. [11] V. S. Kale, S. R. Bhide, P. P. Bedekar, G. V. K. Mohan, ā€œDetection and Classification of Faults on Parallel Transmission Lines using Wavelet Transform and Neural Networkā€, International Journal of Electrical and Computer Engineering, pp.1063-1067, 16, 2008. [12] N. Zamanan, M. Gilany, ā€œA Sensitive Wavelet-Based Algorithm for Fault Detection in Power Distribution Networksā€ ACEEE Int. J. on Communication, Vol. 02, No. 01, Mar 2011, pp.46-50. [13] S. Haykin, Neural Networks, IEEE Press, New York, 1994. [14] Mamta Patel, R. N. Patel, ā€œFault Detection and Classification on a Transmission Line using Wavelet Multi Resolution Analysis and Neural. [15] Suresh J. Thanekar, Waman Z. Gandhare and Anil P. Vaidya, ā€œVoltage Stability Assessment of a Transmission System -A Reviewā€, International Journal of Electrical Engineering Technology (IJEET), Volume 3, Issue 2, 2012, pp. 182 - 191, ISSN Print: 0976-6545, ISSN Online: 0976-6553. [16] Soumyadip Jana, Sudipta Nath and Aritra Dasgupta, ā€œTransmission Line Fault Classification Based on Wavelet Entropy and Neural Networkā€, International Journal of Electrical Engineering Technology (IJEET), Volume 3, Issue 2, 2012, pp. 94 - 102, ISSN Print: 0976-6545, ISSN Online: 0976-6553.
  • 16. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ā€“ 6545(Print), ISSN 0976 ā€“ 6553(Online) Volume 5, Issue 7, July (2014), pp. 32-44 Ā© IAEME 44 AUTHORā€™S DETAIL R. P. Hasabe received the B.E. degree in electrical engineering and the M.E. degree in electrical power systems from Shivaji University, Kolhapur, India in 2001 and 2006, respectively, and is currently pursuing the Ph.D. degree in Electrical engineering at Shivaji University Kolhapur. Currently, he is an Assistant Professor in the Department of Electrical Engineering, Walchand College of Engineering, Sangli. His research interests include power system protection, planning and design, system modeling, and simulation. A. P. Vaidya received the B.E. in electrical engineering and the M.E. in electrical power systems from Shivaji University, Kolhapur, India, in 1983 and 1993 respectively, and the Ph.D. degree from the IISc, Bangalore in 2005. Currently, he is Professor in the Department of Electrical Engineering, Walchand College of Engineering, Sangli. He has published more than 10 papers in journals and conferences at international and national levels. His research interests include power system protection, automation, planning and design, system modeling and simulation, and artificial intelligence.
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