Coordination And Optimization Of Power System Stabilizer Parameter Based On Stochastic Focusing Particle Swarm Optimization

With the expansion of electric power system and installation of the fast and high magnification excitation, low frequency oscillation is becoming one of the important problem which influences the stability of interconnection power system. Power system stabilizers (PSS) employed in generators is an effective way to solve this problem. PSS generate such supplemental damping torque that increasing system damping to enhance the stability of system.PSS, employed in power system, exist the situation that suppressing one main oscillation mode but deteriorating another then deteriorating system further once the parameters are not suitable that control result inevitably deviates from the optimal point decreasing the effect of PSS. Therefore such problem needs the optimization algorithm to coordinate the working state of PSS.This thesis demonstrates the background, mathematical model and optimization process of SFPSO. Based on the model of single-machine with infinite-bus system and 2-area 4-machine system, the objective function is considered as the ability of system output tracking reference value according to the least error (ITAE criteria), then establishing the model of optimization of PSS parameters. This thesis also compares and summaries the popular optimization algorithms—PSO-ω(Particle swarm optimizer with inertia weight), AGA (adaptive genetical gorithm) etc. An advanced PSO—Stochastic focusing particle swarm optimization (SFPSO) is used to coordinate and optimize the parameters of PSS. By the simulation tests of the state of operating system and comparison results with various optimization algorithms in matlab, it demonstrats that PSS can obtain the favorable performance under the different disturbances using SFPSO and suppress the low frequency oscillation effectively to maintain the stability of system. Simultaneously, the validity and superiority of SFPSO is proved.