Optimal tuning of System Stabilizer parameters using PBIL with adaptive learning rate

This paper presents an optimal tuning of Power System Stabilizers (PSS) for a multi-machine power system using Population-Based Incremental Learning (PBIL) algorithm with adaptive learning rate. The proposed (APBIL) algorithm can adjust the learning rate automatically according to the degree of evolution of the search. The objective of the design is to achieve adequate stability over a wide range of operating conditions. The proposed controller is compared with traditional PBIL with fixed learning rate (PBIL) and tested under various operating conditions. Simulation results show that the APBIL based PSS provides a more efficient search capability and gives a better damping and adequate dynamic performance of the system than the traditional PBIL based PSS.