Fuzzy basis function network for generator excitation control

M. A. Abido*, Y. L. Abdel-Magid

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

11 Scopus citations

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 (OLS) 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 (CPSS). 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.

Original languageEnglish
Pages1445-1450
Number of pages6
StatePublished - 1997

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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