A Wavelet- Based Fault Localization in Transmission Network Sushma Verma, Pratyay Konar, and Dr. Paramita Chattopadhyay.
Abstract-- The proposed technique consists of a pre-processing unit based on Continuous Wavelet Transform (CWT) in combination with an Artificial Neural Networks (ANN). CWT acts as an extractor of distinctive features in the transient current signals at the sending and receiving end of the transmission line. This information is then fed to the ANN for detecting the fault type and location of fault. The results presented clearly indicate that the present technique is very fast, computationally efficient and intelligent enough to accurately identify three different types of fault (LG & LLG) and their locations. Index Terms-- Artificial Neural Network (ANN), Alternate Transient Program(ATP), Continuous Wavelet Transform (CWT), Fault location, Wavelet Transform, Transmission line .
I. INTRODUCTION HE electrical power system network is spread widely throughout the geographical regions. The demand of power is growing with the development of the country. The growing demand leads to the interconnection of the power networks. As the number of interconnections is increasing everyday the existing power system has become increasingly complex. There are various equipments in the power system network at both the ends. The exposure of overhead line experiences a permanent or short duration fault. There are various causes like capacitor switching, lightning, high impedance faults etc. that leads to transient disturbances in transmission lines. These transients pose a great challenge to the power system engineers. The increasing complexity of the modern power system network requires a fast fault detection technique by making the transient an important phenomenon. The fault current has the fundamental, higher order harmonics and time-domain information. Analysis of these higher order harmonics and their time information plays a very important role for the detection of faults. The Wavelet Transforms are best suited for the analysis of transient waveforms than other
T
transform approaches. Due to this they have received great attention in the power community in the last few years. The potential benefits of applying Wavelet Transforms in combination with soft computing techniques for improving the performance of protective relays have already been recognized by various researchers. In the mainstream literature, wavelets were first applied to power system in 1994 by Robertson and Ribeiro as reported in [5]. From this year the number of publications in this area has increased exponentially. There are various approaches for analysis of power system faults based on expert system [1],[3],[4] fuzzy logic [6], optimization [2], pattern recognition approach [7], artificial neural network (ANN) [8], wavelet techniques [9], etc. Each of them has some difficulties in achieving speed, selectivity and accuracy. Discrete wavelet transform in combination with ANN has also been implemented for this purpose. However there is no report of using Continuous Wavelet Transform (CWT) in classification of power system transients. CWT has great potential in analyzing system transients. II. PROPOSED SCHEME Keeping the above viewpoints in mind the present work has been directed towards the development of CWT-ANN based fast, intelligent and reliable algorithm to discriminate and identify different power system transients. The work has been mainly focused on identification of different types of transmission line faults along with their locations. The schematic representation of the proposed work is shown in the fig. 1.
S. Verma is with Techno India College of Technology ,Rajarhat District Kolkata, India (e-mail:
[email protected]). P.Konar is with the Department of Electrical Engineering, B.E.S.U,Shibpur University, Howrah, India (e-mail:
[email protected]) P.Chattopadhyay is with the Department of Electrical Engineering,B.E.S.U, Shibpur, Howrah, India (e-mail:
[email protected]
978-1-4673-0136-7/11/$26.00 ©2011 IEEE
Fig. 1 Block diagram of the proposed scheme
III. SIMULATION OF TRANSMISSION LINE MODEL In this study a power system network consisting of two three phase voltage sources are used. The length of the transmission line is 80 km.
Fig. 2 Single line diagram for system under study
The power transmission system was modeled with EMTP to test the performance of the proposed scheme. The single line diagram for the system under study is presented in Fig. 2 along with the EMTP/ATP model in Fig. 3.
signal into shifted and scaled versions of the original (or mother) wavelet. So, sharp changes might be better analyzed with an irregular wavelet than with a smooth sinusoid. Therefore, to summarize the procedure of wavelet analysis may run like this – a wavelet prototype function, called an analyzing wavelet or mother wavelet is adopted and then temporal analysis is performed with a contracted, highfrequency version of the prototype wavelet, while frequency analysis is performed with a dilated, low frequency version of the same wavelet. Thus the original signal or function can be represented in terms of a wavelet expansion (using coefficients in a linear combination of the wavelet functions). Data operations can be performed using just the corresponding wavelet coefficients.
Fig. 3 EMTP Model for 80 Km line with single line to ground fault
Extensive simulations were carried out, with different fault locations , for single line to ground fault (LG) and double line to ground fault (LLG) for different fault resistances (10Ω, 50Ω and100Ω). Three phase transient current signals from both sending and receiving ends were captured using ATP/ EMTP simulation environment. The current wave forms were generated at a sampling frequency of 2 KHz as shown in Figs. 4 and 5.
Fig. 5 Phase ‘a’ current for single line to ground fault as recorded on receiving end side.
The continuous wavelet transform (CWT) is defined as the sum over all time of the signal multiplied by scaled, shifted versions of the wavelet function Ψ.
Lψ f ( s,τ ) = ∫ f (t )Ψs*,τ (t ) dt
(1)
f (t) is decomposed into a set of basis function ψ (t), called wavelets generated from a single basic wavelet ψ(t), the so called mother wavelet, by scaling and translation Ψs ,τ (t ) =
t −τ Ψ s s
1
(2)
s is a scale factor, τ is the translation factor and the factor s is for energy normalization across the different scale. Fig. 4 Phase ‘a’ current for single line to ground fault as recorded on sending end side.
A wavelet is a waveform of effectively limited duration that has an average value of zero. Comparing wavelets with sine waves, which are the basis of Fourier analysis, it can be appreciated that sinusoids are smooth and predictable; wavelets tend to be irregular and asymmetric. Fourier analysis consists of breaking up a signal into sine waves of various frequencies. Similarly, wavelet analysis is the breaking up of a
Scaling a wavelet simply means stretching or compressing it. In this work CWT has been used as a signal processing tool, because CWT has a great potential in analyzing system transients. The raw data of the transient current signals were imported in the MATLAB workspace and CWT coefficients were computed for ‘coif1’ mother wavelet using wavelet tool box commands. Typical CWT coefficient plots of the transient signals are shown in Fig 6 and Fig 7. For all simulation step length of moving window was set to 1/8th cycle (50 Hz
supply). Finally the RMS vale of the CWT coefficient was computed. The R.M.S values of CWT coefficients thus obtained were used as attributes to train the Artificial Neural Network (ANN). The ANN can easily identify the types of faults and their locations.
to the input layer through the hidden layer in the network to obtain the final desired outputs. During the forward pass all the weights of the networks are fixed. During the backward pass, on the other hand, all the weights are adjusted in accordance with an error-correction rule. V. RESULTS AND ANALYSIS The architecture of the developed wavelet based ANN models have been presented in Fig 8 (a) & (b).
Fig. 8(a) Neural Network Architecture fault classification Fig. 6 CWT Phase ‘a’ for Sending end current for single line to ground fault.
Fig. 8(b) Neural Network Architecture fault localization
Fig. 7 CWT Phase ‘a’ for Receiving end current for single line to ground fault.
IV. FAULT TYPE IDENTIFICATION AND FAULT LOCATION DETECTION USING ANN Post Processing and Diagnosis of faults was done by using Artificial Neural Network (ANN) as a classifier. In this work, a two layer feed forward neural network has been trained by back propagation algorithm. A feed-forward back-propagation neural network has three components: an input layer, one or more hidden layers, and an output layer. Each layer consists of one or more neurons called nodes. In the calculation process of problem solving, all input nodes are collected at each hidden node after being multiplied by weights. Later, a bias is attached to this sum, transformed through a nonlinearity function, and transferred to the next layer. There are several functions such as hyperbolic tangent, sigmoid and linear functions that can be used as transfer function. The same procedure can be followed in the next layer to provide the network output results. As the forward processing arrives at the output layer, the overall error between the network output and the actual observation is calculated. The error at the output layer propagates backward
They have been used for classifying types of fault and to determine fault locations of the three phase transmission networks. The developed ANNs have six input neurons namely RMS values of CWT coefficients of 3-ph sending and receiving end currents. The ANN models used are shown in fig. 8(a) and 8(b). The ANN model has three output neurons for fault classification and one output neuron for fault localization. The activation functions at the hidden layers and output layer in the network have been tan-sigmoid and logsigmoid respectively. Total 180 feature samples were simulated for different switching, single line to ground (LG) and double line to ground fault (LLG) fault conditions at different locations. Among them 120 samples were used for classification purpose and remaining 60 samples for testing the classifier’s generalization ability. From the test results furnished in Table I and Table II implies that wavelet based techniques correctly classify the types of faults and their locations also. TABLE I FAULT CLASSIFICATION ACCURACY Type of Fault
Training Accuracy
Testing Accuracy
L-G Fault in phase ‘a’ at 55 km (50 Ω)
99.6%
98.1%
L-L-G Fault in phase ‘a’ and ‘b’ at 45 km (100 Ω)
99.3%
99.4 %
L-L-G Fault in phase ‘c’ and ‘b’ at 35 km (50 Ω)
98.3%
99.2%
TABLE II FAULT LOCALIZATION ACCURACY Training
Testing
Desired
Network
Desired
Network
Output
Output
Output
Output
10
10.81
10
9.91
10
10.11
10
10.09
20
19.99
20
18.31
20
20.10
20
20.41
30
30.02
30
30.12
30
29.84
30
28.97
40
40.09
40
40.59
40
39.87
40
38.89
50
50.22
50
49.91
50
49.29
50
50.10
60
60.03
60
59.76
60
59.89
60
60.52
65
64.95
65
64.71
65
64.88
65
65.39
70
69.91
70
70.55
70
70.19
70
70.80
75
74.94
75
75.06
75
74.93
75
74.88
80
79.91
80
79.91
80
79.91
80
79.89
Vector Machines (SVM) or rough set theory can be applied for improved classifications or prediction and to overcome the limitations of ANN may be exploited. Type of Fault
L-G Fault (phase ‘a’) L-L-G Fault (phase ‘ab’)
VII. ACKNOWLEDGMENT The authors are thankful to Mr. Vijaykumar Garlapati for his efforts towards this project. VIII. REFERENCES [1]
[2] L-L-G Fault (phase ‘bc’) [3] L-L-G Fault (phase ‘ca’) L-G Fault (phase ‘c’)
[4]
[5]
L-G Fault (phase ‘b’)
[6]
L-L-G Fault (phase ‘ca’)
[7]
L-L-G Fault (phase ‘ca’)
[8]
L-L-G Fault (phase ‘ca’) L-G Fault (phase ‘b’)
VI. CONCLUSIONS In the present era of open access and deregulated electricity market, it is very important to restore the system after fault clearance. Accurate identification of fault and its location is thus of prime concern, where the transient signals can play a very vital role. This work is a preliminary report of a research project with a long term goal to utilize the power system transient signals for fast and accurate discrimination of different switching conditions. The present study demonstrates the effectiveness of transient current signals to identify the fault type and fault locations of three phase system. The properties of the proposed scheme are: • The Continuous wavelet transform (CWT) provides an efficient way to extract important fault features. • Neural network provides an intelligent method and a soft criterion for feature comparison. However there is ample scope of work. Variation of prefault load and system conditions and fault resistances, best feature selection of wavelet coefficients, impact of motherwavelet, variation of scale may be explored in future. Apart from the ANN other soft-computing techniques like Support
[9]
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IX. BIOGRAPHIES Sushma Verma born, on Dec 17 , 1976 is currently working as an Assistant Professor in Techno India college of Technology in Electrical Engineering Department, Rajarhat, Kolkata. She graduated from Government Engineering College, Bhuj, Gujarat., India and completed Masters Degree from Guru Nanak Dev Engineering College, Punjab from Punjab Technical University. After her graduation she worked as a Trainee Engineer in Standard Electricals Limited Company, Jalandhar, Punjab.She served as an expatriate lecturer in Royal Bhutan Institute of Engineering and Technology, Bhutan, for two years. Her special fields of interest are Power Sytem Transients and Power System Protection. She is currently working under the esteemed guidance of Dr. Paramita Chattopadhyay. Pratyay Konar received B.Tech. in Electrical Engineering from Asansol Engineering College under West Bengal University of Technology, India in 2007 and M.E. in Electrical Engineering with Electrical Machine specialization from Bengal Engineering and Science University, Shibpur, India in 2009. He is currently working to pursue Ph.D. in Bengal Engineering College and Science University, Shibpur, India. His research interests include image processing
and application of advanced signal processing and applied soft computing technique to condition monitoring of electrical machines. Paramita Chattopadhyay received the B.E., M.E., and Ph.D. in Electrical Engineering from Bengal Engineering College and Science University, Shibpur, India in 1993, 1996, and 2002, respectively. She is an Assistant Professor at Bengal Engineering and Science University, Shibpur, India in Electrical Engineering Department where she specializes in the field of condition monitoring, power system protection and power system transient analysis. Her research interests are applications of advanced signal processing and soft computing technique in the area of condition monitoring of electrical machines, power systems and nano material applications in power sectors.
