• EL GHALIA BOUDISSA Laboratoire de Système Electrique et Télécommande, Université BLIDA 1, Algeria Author
  • FATIHA HABBI Laboratoire de Système Electrique et Télécommande, Université BLIDA 1, Algeria Author
  • NOUR EL HOUDA GABOUR Laboratoire de Système Electrique et Télécommande, Université BLIDA 1, Algeria Author
  • M’HAMED BOUNEKHLA Laboratoire de Système Electrique et Télécommande, Université BLIDA 1, Algeria Author


Genetic algorithm, Selection pressure, Power ranking selection, Induction machine, Identification


Premature convergence is known as a serious failure mode for genetic algorithms (GAs). This paper presents a new dynamic selection based on power ranking by varying gradually the selection pressure versus generations, in order to maintain a trade-off between exploitation and exploration in genetic algorithm and avoid premature convergence. The proposed dynamic genetic selection algorithm’s performance was proven by identifying an induction machine’s (IM) parameters, both electrical and mechanical, using only the starting current and the corresponding phase voltage. A comparison is established between the proposed dynamic genetic selection algorithms with other genetic selections algorithms. The evaluation is carried out on IM’s (1.5 kW) parameters estimation by measured data. The matching in the transient and in steady state of computed currents with the measured ones confirms the accuracy of the identified parameters. The experimental results obtained indicate the superiority of the proposed dynamic genetic selection algorithm versus the other algorithms in terms of computing time and convergence speed.


(1) A. Accetta, F. Alonge, M. Cirrincione, M. Pucci, A. Sferlazza, Parameter identification of induction motor model by means of state space-vector model output error minimization, XXI Int. conference on Electrical Machines (ICEM), pp. 843-849, 2014.

(2) J.A. De Kock, F.S. Van Der Merwe, H.J. Vermenlen, Induction motor parameter estimation through an output error technique, IEEE Transactions on Energy conversion, 9, 1, pp. 69-76, 1994.

(3) T. Tudorache, I.D. Ilina, L. Melcescu, Parameters estimation of an induction motor using optimization algorithms, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 61, 2, pp. 121–125, 2016.

(4) M. Urech, F. Jenni, Software based parameter identification method for induction machines, PCIM Europe 2015, Germany, pp. 749-757, 2015.

(5) G. Tamàs, P. Àron Attila, B. Kàroly-Àgoston, Optimization of a three-phase induction machine using genetic algorithm, Multiscience-XXX micro CAD international multidisciplinary scientific conference, Hungary, 21-22 April 2016.

(6) A. Trentin, P. Zanchetta, P. Wheeler, J. Clare, R. Wood, D. Katsis, A new method for induction motors parameters estimation using GA and transient speed measurements, Industry Application Conference. 41st lAS annual meeting, Conference record of the 2006 IEEE, 5, pp. 2435-2440, 2006.

(7) M.A. Awadallah, Parameter estimation of induction machines from nameplate data using particle swarm optimization and genetic algorithm techniques, EPCS, 36, 8, pp. 801-814, 2008.

(8) V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian, Bacterial foraging technique-based parameter estimation of induction motor from manufacturer data, Electric Power Components and Systems, 38, 6, pp. 657- 674, 2010.

(9) M. Mihalache, Equivalent circuit parameters and operating performances of the three-phase asynchronous motor, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 55, 1, pp. 32–41, 2010.

(10) F. Chich-Hsing, L. Shir-Kuan, W. Shyh-Jier, On-line parmeter estimator of an induction motor at standstill, Control Engineering Practice 13, pp. 535-540, 2005.

(11) A. Simion, L. Livadaru, D. Lucache, E. Romola, Third Order Harmonics Evaluation by Finite Element Analysis of a two-phase Induction Machine, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 52, 1, pp. 23–32, 2007.

(12) J.H. Holland, Adaptation in natural and artificial systems, Ann Arbor, MI, Univ. Mich. Press., 1975.

(13) D.E. Goldberg, Genetic Algorithms in search, optimization and machine learning, Reading, MA: Addison-Wesley, 1989.

(14) J.E. Baker, Adaptive selection methods for genetic algorithm, in Proc. First Int Conf. on Genetic Algorithms and their Applications. Hillsdale, NJ: Lawrence Erlbaum, pp. 101-111, 1985.

(15) D.E. Goldberg, K. Deb, A comparative analysis of selection schemes used in genetic algorithm, In Foundations of Genetic Algorithms, G.J.E. Rawlins, Ed. San Mateo, CA, Morgan Kaufmann, pp. 69-93, 1991.

(16) R. Sivaraj, T. Ravichandran, A review of selection methods in genetic algorithm, (IJEST), 3, 5, pp. 3792-3797, 2011.

(17) A. Shukla, H.M. Pandey, D. Mehrotra, Comparative review of selection techniques in genetic algorithm, IEEE conference, February 2015.

(18) Th. Bäck, F. Hoffmeister, Extended selection mechanisms in genetic algorithms, R.K. Belew and L.B. Booker (Eds.), Proceedings of the 4th International Conference on Genetic Algorithms and Their Applications, University of California, San Diego, Morgan Kauffmann Publishers, pp. 92-99, 1991.

(19) E. Boudissa, M. Bounekhla, A real-coded genetic algorithm applied to induction machine parametric identification trough an output error, MJMC, 6 3, pp. 109-119, 2010.

(20) E. Boudissa and M. Bounekhla, Genetic algorithm with dynamic selection based on quadratic ranking applied to induction machine parameters estimation, EPCS, 40, 10, pp. 1089-1104, 2012.

(21) K. Jebari, M. Madiafi, Selection methods for genetic algorithms, International Journal Emergence Science, 3, 4, pp. 333-344, 2013.

(22) N.V. Cardoso, J. Palma, J. Santana, Induction motor parameters identification from Ben Tests using a Newton-Raphson method, Proceedings of the 2008 International conference on Electrical Machines, 2008.

(23) C.Z. Janikow, Z. Michalewicz, An experimental comparison of binary and floting point representation in genetic algorithms, In Proceeding of the fourth international conference of Genetic Algorithms, San Diego, pp. 31-36, 1991.

(24) J. Chatelain, Machines électriques, Traité d’électricité, X, Presses Polytechniques Romandes, Lausanne, 1983.



— Updated on 09.12.2021



Électrotechnique et électroénergétique | Electrical and Power Engineering

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