Self-adaptive differential evolution based power system stabilizers

In this paper, a self-adaptive differential evolution (DE) which adapts its crossover and mutation rates is used to design power system controllers for a multi-machine power system. To show the effectiveness of the approach, the performances of the proposed controllers are compared with the ones designed using Population-Based Incremental Learning (PBIL) algorithm and the classical Differential Evolution (DE). It is shown that the controllers based on self-adaptive DE performed better than the ones based on DE and PBIL.