Genetically optimized neuro-fuzzy PSS for damping modal oscillations of power system

An integrated approach of Takagi-Sugeno (TS) fuzzy scheme with a genetic optimization of their parameters has been developed in this paper to design intelligent adaptive controllers for improving the dynamic and transient stability performance of power systems. This concept is applied to power system stabilizer (PSS) connected to a nine bus three machine power system. The rules of the TS-fuzzy scheme are derived from the speed error and their derivatives. Further, to implement this combined scheme only one coefficient in the TS-fuzzy rules needs to be optimized. The optimization of this coefficient as well as the coefficient for auxiliary signal generation is performed through genetic algorithm. The performance of the new controller is evaluated in multimachine power systems subjected to various dynamic and transient disturbances. The new genetic-neuro-fuzzy control scheme exhibits a superior damping performance as well as a greater critical clearing time in comparison to the existing PI and supplementary controller with updating of its parameters through lag-lead compensation. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation.