Velocity relaxed swarm intelligent tuning of fuzzy based power system stabilizer

This paper presents a novel velocity relaxed swarm intelligent approach for tuning of a dual input power system stabilizer (PSS) in a single machine infinite bus (SMIB) system. Velocity update relaxation particle swarm optimization (VURPSO) and binary coded genetic algorithm (GA) have been adopted individually to get optimal power system stabilizer parameters for off-line, nominal system operating conditions. Simulation study of SMIB system along with the power system stabilizer shows that the dual input PSS is very effective in damping of generator´s electromechanical local mode of oscillations and yields robust dynamic performance over a wide range of system operating conditions. Incorporation of velocity update relaxation (VUR) in traditional PSO helps to have reduced computational effort as compared with traditional PSO. GA based optimization takes more computational time than VURPSO based optimization technique. On real time measurements of system operating conditions Sugeno fuzzy logic (SFL) technique adaptively, very fast yields on-line, off-nominal optimal stabilizer parameters. Fourth order model of generator with automatic voltage regulator (AVR) and high gain thyristor excitation system is considered. Modal analysis is adopted to get the transient responses