Multi-Machine Power System Stabilizer Design Based on Population Based Incremental Learning

Population Based Incremental Learning (PBIL) is one of the Evolutionary Algorithms that has received increasing attention in recent years in solving optimization problems and it has been found to be very effective. However recent studies have shown that PBIL with fixed learning rate may suffer from loss of diversity which can lead to premature convergence. In this paper, Population Based Incremental Learning with adaptive learning rate (APBIL) is used to overcome the issues of premature convergence in PBIL. Frequency and time domain simulation results are presented to show the effectiveness of the APBIL algorithm.