Tuning of power system stabilizers using particle swarm optimization with passive congregation

Power System Stabilizers (PSSs) are the most well-known and efficient devices to damp the power system oscillations caused by interruptions. This paper introduces a novel algorithm to determine the PSS parameters, using the multi-objective optimization approach called particle swarm optimization with the passive congregation (PSOPC). The tuning of the PSS parameters is usually formulated as the objective function with constraints, including the damping ratio and damping factor. Maximization of the damping factor and the damping ratio of power system modes are taken as the goals or two objective functions, when designing the PSS parameters. The optimization procedure handles the problem-specific constraints using a penalty function. This could enhance the diversity of the swarm and lead to a better outcome. The two-area multi-machine power system, under a wide range of system configurations and operation conditions is investigated, to illustrate the performance of the proposed approach. In this paper, the performance of the proposed PSOPC is compared to the Standard Particle Swarm Optimization (SPSO) and Genetic Algorithm (GA) in terms of parameter accuracy and computational time. The results verify that, the PSOPC is a much better optimization technique, in terms of accuracy and convergence, compared to PSO and GA. Furthermore, nonlinear simulation and eigenvalue analysis based results also confirm the efficiency of the proposed technique.