Damping of power system oscillations using genetic algorithm and particle swarm optimization

In this study, the application and performance comparison of particle swarm optimization (PSO) and Genetic algorithms (GA) optimization methods, for power system stabilizer (PSS) design is presented. Recently, GA and PSO methods have attracted considerable attention among different modern heuristic optimization methods. The GA has been popular in academia and the industry, mostly because of its intuitiveness, ease of implementation, and the capability to efficiently solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO method is a relatively new heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. Since the two approaches are supposed to find a solution to a given objective function but utilize different strategies and computational effort, it is appropriate to compare their performance. The design objective is to increase the power system stability. The design problem of the PSS parameters is formulated as an optimization problem and both PSO and GA optimization methods are used to search for optimal PSS parameters. 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 both PSO and GA. The performance of both optimization methods is compared with the conventional power system stabilizer (CPSS) in terms of parameter accuracy and computational time. The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the methods in optimal tuning of PSS, to enhance power system stability.