Performance evaluation of power system stabilizers based on Population-Based Incremental Learning (PBIL) algorithm

This paper proposes a method of optimally tuning the parameters of power system stabilizers (PSSs) for a multi-machine power system using Population-Based Incremental Learning (PBIL). PBIL is a technique that combines aspects of GAs and competitive learning-based on Artificial Neural Network. The main features of PBIL are that it is simple, transparent, and robust with respect to problem representation. PBIL has no crossover operator, but works with a probability vector (PV). The probability vector is used to create better individuals through learning. Simulation results based on small and large disturbances show that overall, PBIL-PSS gives better performances than GA-PSS over the range of operating conditions considered.

► Optimal tuning of PSSs parameters using Population-Based Incremental Learning (PBIL) is presented. ► PBIL is a technique that combines aspects of GAs and competitive learning-based on Artificial Neural Network. ► PBIL-PSS was tested on a multi-machine power system and its performance compared to GA-PSS. ► Although PBIL algorithm is much simpler and easier to implement than GAs, it is as effective as GAs. ► Simulation results show that PBIL-PSS gives better performances than GA-PSS.