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Mathematical Problems in Engineering
Volume 2014, Article ID 240565, 19 pages
Research Article

Accurate Fault Classifier and Locator for EHV Transmission Lines Based on Artificial Neural Networks

Latice Laboratory at Higher National Engineering School of Tunis, ENSIT, University of Tunis, 05 Avenue Taha Hussein, Montfleury, 1008 Tunis, Tunisia

Received 6 March 2014; Revised 31 May 2014; Accepted 31 May 2014; Published 15 July 2014

Academic Editor: Haipeng Peng

Copyright © 2014 Moez Ben Hessine and Souad Ben Saber. 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.


The ability to identify the fault type and to locate the fault in extra high voltage transmission lines is very important for the economic operation of modern power systems. Accurate algorithms for fault classification and location based on artificial neural network are suggested in this paper. Two fault classification algorithms are presented; the first one uses the single ANN approach and the second one uses the modular ANN approach. A comparative study of two classifiers is done in order to choose which ANN fault classifier structure leads to the best performance. Design and implementation of modular ANN-based fault locator are presented. Three fault locators are proposed and a comparative study of the three fault locators is carried out in order to determine which fault locator architecture leads to the accurate fault location. Instantaneous current and/or voltage samples were used as inputs to ANNs. For fault classification, only the pre-fault and post-fault samples of three-phase currents were used. For fault location, pre-fault and post-fault samples of three-phase currents and/or voltages were used. The proposed algorithms were evaluated under different fault scenarios. Studied simulation results which are presented confirm the effectiveness of the proposed algorithms.