Fault Detection and Diagnosis of Power Systems using Artificial Neural Networks

Swarup, KS and Chandrasekharaiah, HS (1991) Fault Detection and Diagnosis of Power Systems using Artificial Neural Networks. In: 3rd International Conference on Neural Networks to Power Systems, 1991, 23-26 July, Seattle,WA, pp. 102-106.

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Abstract

Real time fault detection and diagnosis (FDD) is an important area of research interest in knowledge based expert systems. Neurocomputing is one of fastest growing areas of research in the fields of artificial intelligence and pattern recognition. The authors explore the suitability of pattern classification approach of neural networks for fault detection and diagnosis. The suitability of using neural networks as pattern classifiers for power system fault diagnosis is described in detail. A neural network design and simulation environment for real-time FDD is presented. An analysis of the learning, recall and generalization characteristic of the neural network diagnostic system is presented and discussed in detail.

Item Type:	Conference Paper
Publisher:	IEEE
Additional Information:	Copyright 1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords:	Pattern classification;Fault detection and diagnosis;Neural network;Ppower systems
Department/Centre:	Division of Electrical Sciences > High Voltage Engineering (merged with EE)
Date Deposited:	29 May 2006
Last Modified:	19 Sep 2010 04:27
URI:	http://eprints.iisc.ac.in/id/eprint/7046

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