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In this paper, new metaheuristic technique cuckoo search optimization (CSO) is explored for an optimal and robust power system stabilizer (PSS) designing for multimachine power systems and performance is evaluated by comparing with well-established techniques, i.e., genetic algorithm (GA), particle swarm optimization (PSO), and new harmony search optimization (HSO). An eigenvalue-based multiobjective function is implemented for simultaneous control of damping factor and damping ratio to damp out low-frequency local and interarea modes of oscillations. The PSS parameters are so intended that unstable and/or lightly damped mode eigenvalues are shifted to a specified D-shape zone in the left half of the s-plane. The selected metaheuristic techniques GA, PSO, HSO, and CSO are used to obtain PSS parameters for 10-machines, 39-bus New England Power System and the controllers designed are named as GAPSS, PSOPSS, HSOPSS, and CSOPSS, respectively. The performance of all designed PSSs controllers is evaluated by eigenvalues analysis, nonlinear simulations, and performance indices for various operating conditions under different scenarios of severe disturbances and their comparative analysis is observed. The robustness of all designed PSSs controllers is observed by testing them on unseen operating conditions and results are compared for the same. The performance of CSOPSS is found to be better than other controllers designed.
Chitara et al. (Thu,) studied this question.