Table of Contents
Advances in Electrical Engineering
Volume 2016, Article ID 8651630, 10 pages
Research Article

Intelligent Fault Diagnosis in a Power Distribution Network

1Centre for Space Transport and Propulsion, National Space Research and Development Agency (NASRDA), Epe, Lagos, Nigeria
2Department of Electrical and Electronics Engineering, University of Lagos, Lagos, Nigeria

Received 27 June 2016; Revised 15 September 2016; Accepted 26 September 2016

Academic Editor: Pascal Venet

Copyright © 2016 Oluleke O. Babayomi and Peter O. Oluseyi. 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.


This paper presents a novel method of fault diagnosis by the use of fuzzy logic and neural network-based techniques for electric power fault detection, classification, and location in a power distribution network. A real network was used as a case study. The ten different types of line faults including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults were investigated. The designed system has 89% accuracy for fault type identification. It also has 93% accuracy for fault location. The results indicate that the proposed technique is effective in detecting, classifying, and locating low impedance faults.