Improved Genetic Algorithm Identification of the Squirrel-Cage Induction Machine Parameters


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


In this paper we propose a parameter identification technique of induction machine, based on optimization techniques by genetic algorithms. This technique allows us an accurate determination of the model parameters of the IM used in static regime as well as dynamic regime and offering a better representation of the IM. Genetic algorithms have proven their interest in solving various optimization problems. In this paper we express and formulate the identification problem in the form of optimization problem by correctly choosing the fitness function and constraints adapted to our case. The validation of the genetic algorithm identification, by simulation in Matlab, has proven its accuracy and effectiveness.
Copyright © 2013 Praise Worthy Prize - All rights reserved.

Keywords


Induction Machine; Identification; Genetic Algorithm; Optimization

Full Text:

PDF


References


S.J. Chapman, Electric machinery fundamentals (McGraw-Hill, 1991).

R. Krishnan, Electric motor drives – modelling, analysis and control (Prentice-Hall, 2001).

K.S. Huang, Q.H. Wu & D.R. Turner, “Effective identification of induction motor parameters based on fewer measurements”, IEEE Trans. On Energy Conversion, 17(Issue 1), pp. 55–60, 2002.

V. Horga, A. Onea, and M. Ratoi “Parameter Estimation of Induction Motor Based on Continuous Time Model,” in Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, Lisbon, Portugal, September 2006, pp. 513-518.

Moutchou, M., Abbou, A., Mahmoudi, H., Induction machine speed and flux control, using vector-sliding mode control, with rotor resistance adaptation, (2012) International Review of Automatic Control (IREACO), 5 (6), pp. 804-814.

Moutchou, M., Mahmoudi, H., Abbou, A., Sensorless sliding mode-backstepping control of the induction machine, using sliding mode-MRAS observer, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 387-395.

Rashtchi, V., Rahimpour, E., Fazli, S., Genetic algorithm application to detect broken rotor bar in three phase squirrel cage induction motors, (2011) International Review of Electrical Engineering (IREE), 6 (5), pp. 2286-2292.

Hosseini, M., Ershad, N.F., Moghani, J.S., A new method for parameters identification of permanent magnet synchronous machines, (2012) International Review of Electrical Engineering (IREE), 7 (4), pp. 4808-4813.

P. Nangsue, P. Pillay, and S. E. Conry, Evolutionary Algorithm for Industrial Motor Parameter Determination, Energy Conversion, IEEE Transaction on, vol. 14-3, pp, 447-453, 1999.

F. Allonge, F. D´Ippolito, G. Ferrante, F. M. Raimondi, Parameter identification of induction motor model using genetic algorithms. IEE Proc. Control Theory, vol.145, pp.587-593, 1998.

H. Razik, A. Rezzoug, An application of genetic algorithm to the identification of electrical parameters of an induction motor. PEMC, Košice, vol. 6, pp.1-5, 2000.

K. S. Huang, W. Kent, Q. H. Wu, and D.R. Turner, Parameter Identification of an induction Machine Using Genetic Algorithms, in Proceeding of the 1999 IEEE International Symposium on Computer Aid Control System Design, Hawai, USA, 1999.

Amezquita-Brooks, L., Liceaga-Castro, J., Liceaga-Castro, E., Induction motor identification for high performance control design, (2009) International Review of Electrical Engineering (IREE), 4 (5), pp. 825-836.

A. Coello Coello, Carlos, D.A. Van Veldhuizen et G.B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York 576p. 2000.

D. E Goldberg, Genetic Algorithm in Search, Optimization, and Machine Learning (Addison- Wesley, 1989).

J. Holland, Adaptation in Natural and Artificial Systems. (University of Michigan Press : Ann Arbor, 1975).

L. Davis, Handbook of Genetic Algorithms (Van Nostrand Reinhold : New York, 1991).

Z. Michalewicz, Genetic Algorithm (Springer-Verlag : New York, 1992).

A. Wright, Genetic Algorithms for Real Parameter Optimization, pp. 205–218. Morgan Kaufmann: San Mateo, CA, 1991.


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize