CFP last date
20 December 2024
Reseach Article

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

by M. Pourmahmood Aghababa, A.M. Shotorbani, R. M. Shotorbani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 10
Year of Publication: 2010
Authors: M. Pourmahmood Aghababa, A.M. Shotorbani, R. M. Shotorbani
10.5120/1618-2176

M. Pourmahmood Aghababa, A.M. Shotorbani, R. M. Shotorbani . Article:An Adaptive Particle Swarm Optimization Applied to Optimum Controller Design for AVR Power Systems. International Journal of Computer Applications. 11, 10 ( December 2010), 22-29. DOI=10.5120/1618-2176

@article{ 10.5120/1618-2176,
author = { M. Pourmahmood Aghababa, A.M. Shotorbani, R. M. Shotorbani },
title = { Article:An Adaptive Particle Swarm Optimization Applied to Optimum Controller Design for AVR Power Systems },
journal = { International Journal of Computer Applications },
issue_date = { December 2010 },
volume = { 11 },
number = { 10 },
month = { December },
year = { 2010 },
issn = { 0975-8887 },
pages = { 22-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume11/number10/1618-2176/ },
doi = { 10.5120/1618-2176 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:00:12.516213+05:30
%A M. Pourmahmood Aghababa
%A A.M. Shotorbani
%A R. M. Shotorbani
%T Article:An Adaptive Particle Swarm Optimization Applied to Optimum Controller Design for AVR Power Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 11
%N 10
%P 22-29
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This 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.

References
  1. Angeline, P. 1998. Using Selection to Improve Particle Swarm Optimization, In Optimization Conference on Evolutionary Computation, Piscataway, New Jersey, USA, pp. 84-89, IEEE service center.
  2. Chu, S.Y., and Teng, C.C. 1999. “Tuning of PID controllers based on gain and phase margin specifications using fuzzy neural network,” Fuzzy Sets and Systems, 101(1), pp. 21-30.
  3. Fogel, L. 1994. Evolutionary Programming in Perspective: Top-Down View. Computational Intelligence: Imitating Life, Piscataway, New Jersey, USA, IEEE.
  4. Gaing, Z. L. 2004. “A Particle Swarm Optimization Approach For Optimum Design of PID controller in AVR system,” IEEE Transactions on Energy Conversion, 9(2):384-391.
  5. Holland, J. H. 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, Internal Report.
  6. Hsiao, Y. T., Chuang, C. L., and Chien, C. C. 2004. “Ant colony optimization for designing of PID controllers,” in proceedings of the 2004 IEEE Conference on Control Applications/International Symposium on Intelligent Control/International Symposium on Computer Aided Control Systems Design, Taipei, Taiwan.
  7. Kennedy, J., and Eberhart, R. 1995. Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, vol.4, pp.1942-1948.
  8. Kennedi, J., and Eberhart, R. 1997. A Discrete Binary Version of the Particle Swarm Algorithm. In Proceedings of the Conference on Systems, Man, and Cybernetics, pp. 4104-4109.
  9. Kennedy, J. Eberhart, R. C., and Shi, Y. 2001. Swarm Intelligence, Morgan Kaufmann Publishers, San Francisco.
  10. Kim, D. H. 2001. “Tuning of a PID controller using a artificial immune network model and local fuzzy set”, in Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, vol.5, pp. 2698 – 2703.
  11. Krohling, R. A., and Rey, J. P. 2001. “Design of optimal disturbance rejection PID controllers using genetic algorithm,” IEEE Trans. Evol. Comput., vol. 5, pp. 78–82.
  12. Li, L. L., Wang, L., and Liu, L. H. 2005. An effective hybrid PSOSA strategy for optimization and its application to parameter estimation, Applied Mathematics and Computation.
  13. Lovberg, M., and Krink, T. 2002. Extending Particle Swarm Optimizers with Self-Organized Criticality. In Proceeding of Forth Congress on Evolutionary Computation, vol. 2, pp.1588- 1593.
  14. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller A., and Teller, E. 1953, Journal of Chemical Physics, vol. 21, pp. 1087–1092.
  15. Riget, J., and Vesterstrom, J. 2002. A Diversity-Guided Particle Swarm Optimizer- The ARPSO, EVALife Technical Report, no 2002-2.
  16. Shi, Y., and Eberhart, R. 1998. A modified particle swarm optimizer. Proceedings of the IEEE international conference on evolutionary computation. Piscataway, NJ: IEEE Press; p. 69–73.
  17. Van den Bergh, F. and Engelbrecht, A. P. 2002. A New Locally Convergent Particle Swarm Optimization, In Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, Hammamet, Tunisia.
  18. Visioli, A. 1999. “Fuzzy logic based set-point weight tuning of PID controllers”, IEEE Trans. System, Man, and Cybernetics – Part A: System and Humans, vol. 29, no. 6, pp. 587-592.
  19. Wang, P., and Kwok, D.P. 1994. “Optimal design of PID process controllers based on genetic algorithms”, Control Engineer Practice, vol. 2, no. 4, pp.641-648. VII: Proc. EP98, New York, pp. 591–600.
  20. Zhou, G., and Birdwell, J. D. 1994. “Fuzzy logic-based PID autotuner design using simulated annealing, in Proceedings of the IEEE/IFAC Joint Symposium on Computer-Aided Control System Design, 7-9, pp. 67 – 72.
  21. Zhao, Z.Y., Tomizuka, M. and Isaka, S. 1993. “Fuzzy gain scheduling of PID controllers”, IEEE Trans. System, Man, and Cybernetics, vol. 23, no. 5, pp. 1392-1398.
  22. Ziegler, J.G., and Nichols, N.B. 1942. “Optimum settlings for automatic controllers”, Trans. On ASME., vol. 64, pp.759-768.
Index Terms

Computer Science
Information Sciences

Keywords

Particle Swarm Optimization Fast Convergence Local Optimum PID Controller AVR Power System