Enhanced control of electrical power system oscillations by GA optimized damping controller

Abstract

Dimirovski, Georgi M. (Dogus Author) -- Conference full title: 2010 14th International Power Electronics and Motion Control Conference (EPE/PEMC 2010) : Ohrid, Macedonia, 6 - 8 September 2010A genetic algorithm based optimization technique for optimum design of power system damping controller in a multi-machine power system has been developed. In this technique, the genetic algorithm involves an objective function that is based on the integral of squared-time- squared-error criterion. Parameters of the power system stabilizers are tuned via simulation experiments based on a non-linear model of the system. The dynamic performance has been investigated under both small and large disturbance perturbation, and compared with the case of conventional optimization using phase compensation. Simulation experiments have demonstrated the proposed genetic-algorithm optimized power system stabilizers do enhance quality dynamic performance over a wide range of operating conditions by effectively damping both local and inter-area oscillation modes.ELEM, Repub. Macedonia Chamb. Certif. Archit. Certif. Eng

Dynamic genetic algorithms for robust design of multimachine power system stabilizers

“…Genetic Algorithms (GAs), the most popular evolutionary algorithms, have been used in numerous research works concerning the optimum design of PSSs [6][7][8][9][10][11][12][13]. The authors, in [6][7][8], developed new approaches based on GAs to optimize the PSS parameters in multimachine power systems.…”

“…GAs are used to design fuzzy logic PSSs in [10] and neuro-fuzzy logic PSSs in [11]. A GA-based PSS design in a multimachine power system is presented in [12]; the PSS parameters are tuned via simulation experiments based on nonlinear model of the system. In [13], Non-dominated Sorting GA (NSGA-II) is employed to search the optimal tuning of PSS parameters.…”