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Reseach Article

An Improved Particle Swarm Optimization for Induction Motor Parameter Determination

by V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 2
Year of Publication: 2010
Authors: V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian
10.5120/44-150

V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian . An Improved Particle Swarm Optimization for Induction Motor Parameter Determination. International Journal of Computer Applications. 1, 2 ( February 2010), 62-67. DOI=10.5120/44-150

@article{ 10.5120/44-150,
author = { V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian },
title = { An Improved Particle Swarm Optimization for Induction Motor Parameter Determination },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 2 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 62-67 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number2/44-150/ },
doi = { 10.5120/44-150 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:43:49.915981+05:30
%A V.P. Sakthivel
%A R. Bhuvaneswari
%A S. Subramanian
%T An Improved Particle Swarm Optimization for Induction Motor Parameter Determination
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 2
%P 62-67
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a novel and efficient method to estimate the equivalent circuit parameters of three-phase induction motor from its manufacturer data for steady state analysis using improved particle swarm optimization (IPSO). The IPSO integrates the particle swarm optimization (PSO) with the chaotic sequences. The optimization problem is based on minimizing the error between the computed performance of the equivalent circuit and the manufacturer data. The application of chaotic sequences in PSO is an efficient strategy to improve the global searching capability and escape from local minima. The feasibility of the proposed method is demonstrated for two test motors, and the test results are compared with the simple PSO and classical parameter estimation methods. The simulation results show that the proposed method is capable of obtaining higher quality solutions.

References
  1. Say, M.G. 1983. Alternating Current Machines, Pitman.
  2. Koubaa, Y. 2004. Recursive Identification of Induction Motor Parameters. Journal of Simulation Modeling Practice and Theory. 12, 5, 363-381.
  3. Stephan, J., Bodson, M., and Chiasson, J. 1994. Real Time Estimation of Induction Motor Parameters. IEEE Trans. Industrial Applications. 30, 3, 746-759.
  4. Wang, K., Chiasson, J., Bodson, M., and Tolbert, L.M. 2005. A Nonlinear Least Squares Approach for Identification of The Induction Motor Parameters. IEEE Trans. Automatic Control. 50, 10, 1622-1628.
  5. Toliyat, H.A., Levi, E., and Raina, M. 2003. A Review of RFO Induction Motor Parameter Estimation Techniques. IEEE Trans. Energy Conversion. 18, 3, 271-283.
  6. Pedra, J., and Sainz, L. 2006. Parameter Estimation of Squirrel-Cage Induction Motors without Torque Measurements. IEE Proc. Electric Power Applications. 153, 2, 263-269.
  7. Ansuj, S., Shokooh, F., and Schinzinger, R. 1989. Parameter Estimation for Induction Machines Based on Sensitivity Analysis. IEEE Trans. Industry Applications. 25, 6, 1035-1040.
  8. Pedra, J., and Corcoles, F. 2004. Estimation of Induction Motor Double-Cage Model Parameters from Manufacturer Data. IEEE. Trans. Energy Conversion, 19, 2.
  9. Lindenmeyer, D., Dommel, H.W., Moshref, A., and Kundur, P. 2001. An Induction Motor Parameter Estimation Method. Electrical Power and Energy Systems. 23, 251-262.
  10. Nangsue, P., Pillay, P., and Conry, S. 1999. Evolutionary Algorithms for Induction Motor Parameter Determination. IEEE. Trans. Energy Conversion. 14, 3, 447-453.
  11. Bishop, R.R., and Richards, G.G. 1990. Identifying Induction Machine Parameters Using a Genetic Optimization Algorithm. Proceedings. Southeastcon IEEE. 2, 476-479.
  12. Rahimpour, E., Rashtchi, V., and Pesaran, M. 2007. Parameter Identification of Deep-Bar Induction Motors Using Genetic Algorithm. Electrical Engineering. 89, 547-552.
  13. Huang, K.S., Kent, W., Wu, Q.H., and Turner, D.R. 2001. Parameter Identification for Induction Motors Using Genetic Algorithm with Improved Mathematical Model. Electric Power Components and Systems. 29, 3, 247-258.
  14. Nollan, R., Pillay, P., and Haque, T. 1994. Application of Genetic Algorithms to Motor Parameter Determination. Proc. Of 1994 IEEE-IAS conference, Denvar. 47-54.
  15. Orlowska Kowalska, T., Lis, J., and Szabat, K. 2006. Identification of the Induction Motor Parameters using Soft Computing Methods. COMPEL, 25, 1, 181-192.
  16. Abdelhadi, B., Benoudjit, A., and Nait Said, N. 2004. Identification of Induction Machine Parameters Using an Adaptive Genetic Algorithm. Electric Power Components and Systems. 32, 767-784.
  17. Michael, T.W., and Ronald, G.H. 1995. Identification and Control of Induction Machines Using Artificial Neural Networks. IEEE Trans. Industry Applications Proc.
  18. Bae, D. 1997. Determination of Induction Motor Parameters by Using Neural Network Based on FEM Results. IEEE Trans. Magnetics, 33, 1924-1927.
  19. Ursem, R.K., and Vadstrup, P. 2003. Parameter Identification of Induction Motors Using Differential Evolution. The 2993 Congress on Evolutionary Computation CEC 03. 2, 790-796.
  20. Kennedy, J., and Eberhart, R.C. 1995. Particle Swarm Optimization. Proc. IEEE Int. Conf. Neural Networks. 4, 1942-48.
  21. Lee, K.Y., and EI-Sharkawi. 2002. Modern Heuristic Optimization Techniques with Applications to Power Systems. IEEE Power Engineering Society (02TP160).
  22. Ying Song, Zengqiang Chen and Zhuzhi Yuan. 2007. New Chaotic PSO Based Neural Network Predictive Control for Non Linear Process. IEEE Transactions on Neural Networks. 18, 2, 595-600.
  23. Cai Jiejin,Ma Xiaoqian, Li xiang and Peng Haipeng. 2007. Chaotic Particle Swarm Optimization for Economic Dispatch Considering the Generator Constraints. Energy Conversion and Management. 48, 645 – 653.
Index Terms

Computer Science
Information Sciences

Keywords

Chaotic sequences Improved Particle SwarmOptimization Induction Motor Parameter Estimation Particle Swarm Optimization