Title :
Optimal tuning of power system stabilizer parameters using Population-Based Incremental Learning
Author_Institution :
Univ. of Cape Town, Cape Town
Abstract :
This paper uses a 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 PBIL based PSSs.
Keywords :
learning (artificial intelligence); neural nets; power engineering computing; power system stability; competitive learning-based artificial neural network; multimachine system; population-based incremental learning; power system stabilizer; Artificial neural networks; Control systems; Genetic algorithms; Genetic mutations; Nuclear power generation; Power system modeling; Power system simulation; Power system stability; Power systems; Tuning; Genetic Algorithms; Low-frequency oscillations; Parameter optimization; Population-based incremental learning; Power system Stabilizer;
Conference_Titel :
Power Tech, 2005 IEEE Russia
Conference_Location :
St. Petersburg
Print_ISBN :
978-5-93208-034-4
Electronic_ISBN :
978-5-93208-034-4
DOI :
10.1109/PTC.2005.4524679
