An Adaptive Particle Swarm Optimization Applied to Optimum Controller Design for AVR Power Systems

paper describes an improved version of particle swarm optimization (PSO) method, called adaptive particle swarm optimization (APSO), for solving engineering optimization problems especially in power system fields. This algorithm uses a novel PSO algorithm to increase convergence rate and avoid being trapped in local optimum. The APSO algorithm efficiency is verified using some benchmark functions. Numerical simulation results demonstrate that the APSO is fast and has much less computational cost. Then, the proposed APSO method is used for determining the parameters of the optimal proportional-integral- derivative (PID) controller for an AVR power system. The proposed approach has superior features including easy implementation, stable and fast convergence characteristics and good computational efficiency. Also, the proposed method is indeed more efficient and robust in improving the step response of the AVR system.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[5]  Ying-Tung Hsiao,et al.  Ant colony optimization for designing of PID controllers , 2004, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508).

[6]  Antonio Visioli,et al.  Fuzzy logic based set-point weight tuning of PID controllers , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[7]  Jacques Riget,et al.  A Diversity-Guided Particle Swarm Optimizer - the ARPSO , 2002 .

[8]  Renato A. Krohling,et al.  Design of optimal disturbance rejection PID controllers using genetic algorithms , 2001, IEEE Trans. Evol. Comput..

[9]  Dong Hwa Kim.  Tuning of a PID controller using an artificial immune network model and local fuzzy set , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[10]  John Douglas Birdwell,et al.  Fuzzy logic-based PID autotuner design using simulated annealing , 1994, Proceedings of IEEE Symposium on Computer-Aided Control Systems Design (CACSD).

[11]  P. Wang,et al.  Optimal Design of PID Process Controllers based on Genetic Algorithms , 1993 .

[12]  Zwe-Lee Gaing.  A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004, IEEE Transactions on Energy Conversion.

[13]  Ching-Cheng Teng,et al.  Tuning of PID controllers based on gain and phase margin specifications using fuzzy neural network , 1999, Fuzzy Sets Syst..

[14]  T. Krink,et al.  Extending particle swarm optimisers with self-organized criticality , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[15]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[16]  Masayoshi Tomizuka,et al.  Fuzzy gain scheduling of PID controllers , 1993, IEEE Trans. Syst. Man Cybern..

[17]  Ling Wang,et al.  An effective hybrid PSOSA strategy for optimization and its application to parameter estimation , 2006, Appl. Math. Comput..