Tuning of power system stabilizers using fuzzy basis function networks

Tuning of a Power System Stabilizer (PSS) using a Fuzzy Basis Function Network (FBFN) 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 machine 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 PSS is compared with those of two widely used conventional power system stabilizers. The results show the robustness and the capability of the proposed FBFN PSS to enhance system damping over a wide range of operating conditions and system parameter variations.