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Advances in Artificial Intelligence
Volume 2013 (2013), Article ID 271865, 12 pages
http://dx.doi.org/10.1155/2013/271865
Research Article

Artificial Neural Network-Based Fault Distance Locator for Double-Circuit Transmission Lines

Department of Electrical Engineering, National Institute of Technology, Raipur 492010, India

Received 21 May 2012; Accepted 3 January 2013

Academic Editor: Jun He

Copyright © 2013 Anamika Jain. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits). Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions.