Title :
Robust Controller Design Based on a Combination of Genetic Algorithms and Competitive Learning
Author_Institution :
Univ. of Cape Town, Rondebosch
Abstract :
This paper investigates the robustness of power system stabilizer designs based on an evolutionary algorithm called Population-Based Incremental Learning (PBIL). PBIL combines Genetic Algorithms (GAs) and simple competitive learning derived from Artificial Neural Networks (ANN). The controller design issue is formulated as an optimization problem that is solved via PBIL algorithm. The resulting controllers (PBIL-PSSs) are tested over a wide range of operating conditions for robustness. Simulation results show that PBIL-PSSs are able to stabilize the system adequately over the entire range of operating conditions considered. PBIL-PSSs perform comparably to GA-PSSs under small disturbances but outperform GA-PSSs under large disturbances.
Keywords :
control engineering computing; control system synthesis; genetic algorithms; learning (artificial intelligence); neural nets; power system analysis computing; power system stability; robust control; artificial neural network; competitive learning; evolutionary algorithm; genetic algorithms; population-based incremental learning; power system stabilizer design; robust controller design; Algorithm design and analysis; Artificial neural networks; Control systems; Design optimization; Genetic algorithms; Power system modeling; Power systems; Robust control; Robustness; Testing;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371446