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A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

Changaroon, B and Srivastava, SC and Thukaram, D (2000) A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system. In: IEEE Transactions on Energy Conversion, 15 (1). pp. 103-109.

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Abstract

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Item Type: Journal Article
Additional Information: Copyright 2000 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.
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 04 Apr 2012 10:14
Last Modified: 04 Apr 2012 10:14
URI: http://eprints.iisc.ernet.in/id/eprint/44225

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