HYBRID NEURO-FUZZY POWER-SYSTEM STABILIZER FOR MULTIMACHINE POWER-SYSTEMS

A Fuzzy Basis Function Network (FBFN) based Power System Stabilizer (P SS) is presented in this paper to improve power system dynamic stabili ty. The proposed FBFN based PSS provides a natural framework for combi ning numerical and linguistic information in a uniform fashion. The pr oposed FBFN is trained over a wide range of operating conditions in or der to re-tune the PSS parameters in real-time based on machine loadin g conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious FBFN model. Time domain simulations of a single machine infinite bus system and a mult imachine power system subject to major disturbances are investigated. The performance of the proposed FBFN PSS is compared with that of conv entional (CPSS). The results show the capability of the proposed FBFN PSS to enhance the system damping of local modes of oscillations over a wide range of operating conditions. The decentralized nature of the proposed FBEN PSS makes it easy to install and tune.