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Abstract

A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper. The proposed FBFN based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is trained over a wide range of operating conditions in order to re-tune the PSS parameters in real-time based on generator loading conditions. The orthogonal least squares learning algorithm is developed for designing an adequate and parsimonious FBFN model. Time domain simulations of a synchronous machine equipped with the proposed stabilizer subject to major disturbances are investigated. The performance of the proposed FBFN based PSS is compared with that of a conventional power system stabilizer. The results show the robustness of the proposed FBFN PSS and its ability to enhance system damping over a wide range of operating conditions and system parameter variations

Item Type:	Article
Subjects:	Computer
Department:	College of Engineering and Physics > Electrical Engineering
Depositing User:	Mr. Admin Admin
Date Deposited:	24 Jun 2008 13:37
Last Modified:	01 Nov 2019 14:05
URI:	http://eprints.kfupm.edu.sa/id/eprint/14474