Tuning of Power System Stabilizers using Fuzzy Basis Function Networks

Tuning of a Power System Stabilizer (PSS) using a Fuzzy Basis Function Netw ork (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 a's trained over a wide range of opera ting conditions in order to re-tune the PSS parameters in real-time based o n, machine loading conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious FBFN mode l. Time domain simulations of a synchronous machine equipped with the propo sed stabilizer subject to major disturbances are investigated. The performa nce of the proposed FBFN PSS is compared with those of two widely used conv entional power system stabilizers. The results show the robustness and the capability of the proposed FBFN PSS to enhance system damping over a wide r ange of operating conditions and system parameter variations.