Optimal Design of SSSC Damping Controller to Improve Power System Dynamic Stability Using Modified Intelligent Algorithms

In this paper, A modified intelligent Particle Swarm Optimization (PSO) and continuous Genetic Algorithms (GA) have been used for optimal selection of the static synchronous series compensator (SSSC) damping controller parameters in order to improve power system dynamic response and its stability. Then the performance of these methods on system stability has been compared. First intelligent PSO and genetic algorithms are used to select the effective feedback signal of the damping controller and then simulation results are presented to compare the performance of the proposed SSSC controller in damping the critical modes in a Single-Machine Infinite-Bus SMIB power system. The comparison shows that PSO can reach faster than genetic algorithm to optimal selection of the static synchronous series compensator (SSSC) damping controller parameters and has better performance in damping oscillations.

Keywords:

SSSC , PSO , Genetic algorithm , damping controller , dynamic stability