JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Parameter Identification of Induction Motors using Variable-weighted Cost Function of Genetic Algorithms
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Parameter Identification of Induction Motors using Variable-weighted Cost Function of Genetic Algorithms
Megherbi, A.C.; Megherbi, H.; Benmahamed, K.; Aissaoui, A.G.; Tahour, A.;
  PDF(new window)
 Abstract
This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.
 Keywords
Genetic algorithm;Induction motor;Parameter identification;Cost function;
 Language
English
 Cited by
1.
A Study on Fast Maximum Efficiency Control of Stator-Flux-oriented Induction Motor Drives,;

Journal of Electrical Engineering and Technology, 2011. vol.6. 5, pp.626-633 crossref(new window)
1.
A Study on Fast Maximum Efficiency Control of Stator-Flux-oriented Induction Motor Drives, Journal of Electrical Engineering and Technology, 2011, 6, 5, 626  crossref(new windwow)
2.
Optimum Design Criteria for Maximum Torque and Efficiency of a Line-Start Permanent-Magnet Motor Using Response Surface Methodology and Finite Element Method, IEEE Transactions on Magnetics, 2012, 48, 2, 863  crossref(new windwow)
 References
1.
P. Vas, Sensorless vector and direct torque control: Oxford University Press, 1998.

2.
Tahir Sag and Mehmet Cunkas, “Multiobjective Genetic Estimation to Induction Motor Parameters”, in Proceedings of ACEMP’07 & electromotion’07, international Conference, Bodrum, Turkey, September 2007.

3.
J. Stephan, M. Bodson, and J. Chiasson, “Real-time estimation of the parameters and fluxes of induction motors”, IEEE Trans. Ind. Applicat, Vol. 30, No.1, pp. 746-759, June 1994. crossref(new window)

4.
Y. Koubaa, "Asynchronous machine parameters estimation using recursive method", Simulation Modelling Practice and Theory, Elsevier Vol. 14, pp 1010-1021, 2006 crossref(new window)

5.
L.-C. Zai, C. L. De Marco, and T. A. Lipo, "An extended Kalman filter approach to rotor time constant measurement in PWM induction motor drives", IEEE Trans. Ind. Applicat., Vol. 28,. pp. 96-104, February 1992. crossref(new window)

6.
M. Menaa, O. Touhami, R. Ibtiouen, "Estimation of the rotor resistance in induction motor by application of the spiral vector theory associate to extended Kalman filter", IECON conference of the IEEE, Vol. 1, 2003.

7.
H. Chai and P. Acarnley, "Induction motor parameter estimation algorithm using spectral analysis", Proc. Inst Elect. Eng., Elect. Powe Applicat., Vol. 139, No. 3, pp. 165-174, May 1992.

8.
F. Alonge, F. D’lppolito, G. Ferrante, and F. M. Raimondi, "Parameter identification of induction Motor model using genetic algorithms", Proc. Inst. Elect. Eng., contr. Theory Applicat., Vol. 145, No. 6, pp. 587-593, November 1998. crossref(new window)

9.
F. Alonge, F. D’lppolito, G. Ferrante, and F. M. Raimondi, "Least squares and genetic algorithms for Parameter identification of induction motor", Control engineering practice, Elsevier, 2001, pp. 647-657.

10.
D. E. Goldberg, "Genetic Algorithms in search optimization, and Machine Learning", Addison Wesley. Reading, MA, 1989.

11.
J. H. Holland, "Adaptation in natural and artificial system" The University of Michigan Press, Ann Arbor, 1975.

12.
D. Jong, "Analysis of the behavior of a class of genetic adaptive systems". Ph.D. dissertation, The University of Michigan Press, 1975.

13.
Z. Michalewicz, Genetic Algorithms+ Data Structures =Evolution Programs, Springer-extended edition, Berlin, 1996.

14.
R. K. Ursem and P. Vadstrup, "Parameter identification of Induction motors using stochastic optimization algorithms", Applied soft computing journal, Vol. 4, pp. 49-64. Elsevier 2004. crossref(new window)