Department of Electrical Engineering, University of Cape Town, Private Bag, Rondebosch 7701, South Africa
Folly, K.A., Department of Electrical Engineering, University of Cape Town, Private Bag, Rondebosch 7701, South Africa
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. © 2011 Elsevier Ltd. All rights reserved.
Artificial Neural Network; Crossover operator; Electromechanical modes; Large disturbance; Multi machine power system; Operating condition; Performance evaluation; Population-based incremental learning; Power System Stabilizer; Power system stabilizers; Probability vector; Problem representation; Simulation result; Electric power systems; Genetic algorithms; Mathematical operators; Neural networks; Power transmission; Standby power systems; Learning algorithms
