POWER SYSTEM STABILIZER DESIGN FOR MULTIMACHINE POWER SYSTEM USING POPULATION-BASED INCREMENTAL LEARNING

This paper uses a novel simplified version of GAs called Population-Based Incremental Learning (PBIL) to optimally tune the parameters of the power system stabilizers (PSSs) for a multi-machine system. The technique combines aspects of GAs and competitive learning-based artificial neural network. The issue of optimally tuning the parameters of the PSS is converted into an optimization problem that is solved via the PBIL algorithm. Simulation results are presented to show the effectiveness of the proposed approach.