Nouri BENAIDJA

1312

Softcomputing identification techniques of asyncronous machine parameters: evolutionary strategy and chemotaxis algorithm

Softcomputing identification techniques of asyncronous machine parameters: evolutionary strategy and chemotaxis algorithm

Softcomputing techniques are receiving attention as optimisation techniques for many industrial applications. Although these techniques eliminate the need for derivatives computation, they require much work to adjust their parameters at the stage of research and development. Issues such as speed, stability, and parameters convergence remain much to be investigated. This paper discusses the application of the method of reference model to determine parameters of asynchronous machines using two optimisation techniques. Softcomputing techniques used in this paper are evolutionary strategy and the chemotaxis algorithm. Identification results using the two techniques are presented and compared with respect to the conventional simplex technique of Nelder and Mead. Discussion about the chemotaxis algorithm as the most promising optimisation technique is presented, giving its advantages and disadvantages.
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  • [1] R. Krishnan and A. S. Baharwaj, “A review of parameter sensitivity and adaptation in indirect vector controlled induction motor drive system”, IEEE Transactions on Power Electronics, Vol. 6, No. 4, pp. 695–703, October 1991.
  • [2] D. J. McKinnon, D. Seyoum and C. Grantham, “Improved parameter characteristics for induction machine analysis and control”, Proceedings of the Australasian Universities Power Engineering Conference, AUPEC2003, 28 Septembre-1 Octobre 2003, Christchurch, New Zealand.
  • [3] K. B. Bose, “fuzzy logic and neural networks”, IEEE Industry Applications Magazine, pp. 57–63, May/June 2000.
  • [4] K. B. Bose and N. R. Patel, “Quazy fuzzy estimation of stator r´esistance of induction motor”, IEEE Transactions on Power Electronics, Vol. 13, No. 3, pp. 401–409, May 1998.
  • [5] S. Min, M. E. Elbuluk, and D. S. Zinger, “PI and fuzzy estimators for tuning the stator resistance in direct torque control of induction machines”, IEEE Transactions on Power Electronics, Vol. 13, No. 2, pp. 279–287, March 1998.
  • [6] P. Idowu, “Robust neural net-based inverse model identification of an induction motor”, Electric Machines and Power Systems, Vol. 27, No. 5, pp. 513–525, May 1999.
  • [7] N. Bena¨ıdja, D. Slimani, and N. Khenfer, “Genetic algorithms identification of induction motor parameters”, Proceedings of the International Aegean Conference on Electrical Machines and Power Electronics (ACEMP2001), IEEE Industry Applications Society, Ku¸sadasi, Turkey, pp. 295–298, June 2001.
  • [8] R. K. Ursem and P. Vadstrup, “Parameter Identification of Induction Motors using Stochastic Search Algorithms”. Applied Soft Computing, Vol. 4, No. 1, pp. 49–64, 2004.
  • [9] D. B. Fogel, Handbook of evolutionary computation, IOP Publishing LTD and Oxford University Press, 1997.
  • [10] J. A. Nelder and R. Mead, “A simplex method for function minimization”, computer journal, 7, pp. 308–313, 1965.
  • [11] F. C´orcoles, J. Pedra, and M. Salichs, “Numerical identifiability of the parameters of induction machines”, Proceedings of the International Conference on electrical Machines (ICEM2000), ESPOO, Finland, pp. 9–13, August 2000.
  • [12] M. Biriescu, V. Groza, V. Cretu, M. Mot, and G. Modescu, “Testing of electrical machines using a data acquisition and processing system”, Proceedings of the Workshop of electrical machines parameters, Napoca, Romania, pp. 31–34, May 2001.
  • [13] H. Razik and A. Rezzoug, “Identification of electrical parameters of an induction motor, a comparison between R.L.S and genetic algorithm”, International Conference on electrical Machines (ICEM2000), ESPOO, Finland, pp. 1433–1437, August 2000.
  • [14] S. Moon, A. Keyhani, and S. Pillutla, “Nonlinear neural network modelling of induction machine”, IEEE Transactions on Control Systems Technology, Vol.7, pp. 203–211, March 1999.
  • [15] A. B. Proca and A. Keyhani, “Induction motor parameter identification from operating data for electric drive operation”, Proceedings of the18th DASC, St. Louis, Missouri, October 1999.
  • [16] F. Alonge, F. D’ippolito, and F. M. Raimondi, “Parameter identification of induction motors: least squares vs. genetic algorithms”, International Workshop on Motion Control, Grenoble, France, pp. 1112–1122, September 1998.
  • [17] T. P. Runarsson, “An Asynchronous Parallel Evolution Strategy”, International Journal of Computational Intelligence and Applications, Vol. 3, No. 4, pp. 381-394, 2003.
  • [18] S. D. M¨uller, J. Marchetto, S. Airaghi, and P. Koumoutsakos, “Optimisation based on bacterial chemotaxis”, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, pp. 16–29, February 2002.
  • [19] J. P. Da Cunha, G. Champenois, and J. D. Gabano, “Comparison of parameter estimation algorithms for continuous time models applied to D. C. motor thermal supervision”, Electric Machines and Power Systems 1998, Vol. 26, pp. 839–854, 1998.
  • [20] ˇZ. Ferkov and L. Zboray, “Contribution to parameter identification of induction motor by genetic algorithm” Acta Electrotechnica et Informatica, Vol. 5, No. 2, 2005.
  • [21] S. D. M¨uller, N. Hansen, and P. Koumoutsakos, “Increasing the serial and the parallel performance of the CMAevolution strategy with large populations”, Proceedings of the Seventh International Conference on Parallel Problem Solving from Nature (PPSN VII), Berlin, Germany, pp. 422–431, 2002.
  • [22] K. M. Passino, “Biomimicry of bacterial foraging for distributed optimisation and control”, IEEE Control Systems Magazine, Vol. 22, No. 3, pp. 52–67, June 2002.
  • [23] V. Gazi and K. M. Passino, “Stability analysis of swarms”, IEEE Transactions on Automatic Control, Vol. 48, No. 4, pp. 692–697, April 2003.
  • [24] C. A. Gaizares and W. Rosehart, “Bufirication analysis of induction motor loads for voltage collapse studies”, Proceedings of the north American power symposium (NAPS), MIT, pp. 559–565, November 1996.
  • [25] L. Yanfei and K. M. Passino, “Biomimicry of social foraging behavior for distributed optimization: models, principles, and emergent behaviors”, Journal of Optimization Theory and Applications, Vol. 115, No. 3, pp. 603–628, December 2002.
Turkish Journal of Electrical Engineering and Computer Sciences-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK
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Softcomputing identification techniques of asyncronous machine parameters: evolutionary strategy and chemotaxis algorithm

Nouri BENAIDJA