Damping Controller Design Using Self-Adaptive DE

Abstract Differential Evolution (DE) is one of the most powerful stochastic real parameter, population-based optimization algorithms. It is similar to Genetic Algorithms (GAs), except that it uses differential mutation technique as the main operator to arrive at the best trial solution. The performance of DE is sensitive to the choice of the mutation and crossover strategies and their associated control parameters such as the amplification factor F and the crossover rate CR. Inappropriate choice of control parameter values may result in significant deterioration of the performance of the algorithm. In this paper, a self-adaptive DE is applied to design a controller for damping low frequency oscillations in a power system. In self-adaptive DE, the control parameters such as the mutation scale factor F and crossover rate CR are adapted as the population evolves. The performance of the proposed self-adaptive DE-PSS (jDE-PSS) is compared with that of the classical DE-PSS (CDE-PSS). Simulation results show that for damping low-frequency oscillations, jDE-PSS gives the best performance.