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Fault Detection and Diagnosis of Power Converters Using Artificial Neural Networks

Swarup, KS and Chandrasekharaiah, HS (1996) Fault Detection and Diagnosis of Power Converters Using Artificial Neural Networks. In: 1996 International Conference on Power Electronics, Drives and Energy Systems for Industrial Growth, 8-11 January, New Delhi,India, Vol.2, 1054 -1058.

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Abstract

Fault detection and diagnosis in real-time are areas of research interest in knowledge-based expert systems. Rule-based and model-based approaches have been successfully applied to some domains, but are too slow to be effectively applied in a real-time environment. This paper explores the suitability of using artificial neural networks for fault detection and diagnosis of power converter systems. The paper describes a neural network design and simulation environment for real-time fault diagnosis of thyristor converters used in HVDC power transmission system.

Item Type: Conference Paper
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: Fault detection and diagnosis;Neural network;Converter
Department/Centre: Division of Electrical Sciences > High Voltage Engineering (merged with EE)
Date Deposited: 03 May 2006
Last Modified: 19 Sep 2010 04:26
URI: http://eprints.iisc.ernet.in/id/eprint/6569

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