Automatic Control and Computer Sciences

, Volume 53, Issue 5, pp 408–418 | Cite as

Parameter Identification of Induction Motor by Using Cooperative-Coevolution and a Nonlinear Estimator

  • Alireza RezaeeEmail author
  • S. M. Mehdi Hoseini


Induction motors are one of the critical industrial drivers due to its simplicity, inexpensiveness, and high resistance. Such motors have a nonlinear model divided into two electrical and mechanical equations in terms of modeling. Knowing the values of electric parameters and mechanical moment of inertia is critically important for speed controlling and induction motors’ position. In many algorithms, electric parameters can be obtained by the locked rotor and unloaded tests, conducting these methods in laboratory would probably cost a lot of time and money. In this paper electrical parameters and moment of inertia are used approximately, without doing the above test by currents, voltages, and motor speed sampling in motor normal operation. This paper applies cooperative co-evolution method to remove certain costly tests that are required for induction motors. Two identification algorithms are suggested for all electrical parameters and moment of inertia. All inductances and resistances which are the two input parameters measured in electric equations using Cooperative-Coevolution algorithm. Mechanical model estimated the moment of inertia and load torque by using a nonlinear method based on Lyapunov. Computerized numerical simulations show that electric parameters, moment of inertia, and load torque were properly estimated by integrating the two smart and classic methods. The results show that the stator inductance error is about 1% and rotor inductance error is around 20%. Rotor and stator resistance error and self-Inductance is also less than one percent.


induction motors parameter identification cooperative-coevolution nonlinear evaluator 



The authors declare that they have no conflicts of interest.


  1. 1.
    Wang, L. and Yongqiang, L., Application of simulated annealing particle swarm optimization based on correlation in parameter identification of induction motor, Math. Probl. Eng., 2018, no. 12, pp. 9–18.Google Scholar
  2. 2.
    Abbondanti, A. and Brennen, M.B., Variable speed induction motor drives use electronic slip calculator based on motor voltages and currents, IEEE Trans. Ind. Appl., 1975, 5, pp. 483–488.CrossRefGoogle Scholar
  3. 3.
    Finch, J., Scalar and vector: a simplified treatment of induction motor control performance, IEE Colloquium on Vector Control Revisited (Digest No. 1998/199), 1998.Google Scholar
  4. 4.
    Lindenmeyer, D., et al., An induction motor parameter estimation method, Int. J. Electr. Power Energy Syst., 2001, no. 4, pp. 251–262.CrossRefGoogle Scholar
  5. 5.
    Sharma, A., Panchal, T.H., and Amrolia, H., Simulation and Analysis of Parameter Identification Techniques for Induction Motor Drive, 2014.Google Scholar
  6. 6.
    Peresada, S., et al., Identification of induction motor parameters for self-commissioning procedure: A new algorithm and experimental verification, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), 2014.Google Scholar
  7. 7.
    Pawlus, W., Choux, M., and Hovland, G., Parameters identification of induction motor dynamic model for offshore applications, 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA), 2014.Google Scholar
  8. 8.
    Alonge, F., et al., Parameter identification of induction motor model using genetic algorithms, IEE Proc. Control Theory Appl., 1998, vol. 145, no. 6, pp. 587–593.CrossRefGoogle Scholar
  9. 9.
    Pillay, P., et al., In-situ induction motor efficiency determination using the genetic algorithm, IEEE Trans. Energy Convers., 1998, vol. 13, no. 4, pp. 326–333.CrossRefGoogle Scholar
  10. 10.
    Nolan, R., Pillay, P., and Haque, T., Application of genetic algorithms to motor parameter determination, Conference Record of the 1994 IEEE Industry Applications Society Annual Meeting, 1994.Google Scholar
  11. 11.
    Haque, T., et al., Parameter determination for induction motors, Southeastcon'94. Proceedings of the 1994 IEEE Creative Technology Transfer—A Global Affair, 1994.Google Scholar
  12. 12.
    Abdelhadi, B., Benoudjit, A., and Nait-Said, N., Application of genetic algorithm with a novel adaptive scheme for the identification of induction machine parameters, IEEE Trans. Energy Convers., 2005, vol. 20, no. 2, pp. 284–291.CrossRefGoogle Scholar
  13. 13.
    Kim, J.-W. and Kim, S.W., Parameter identification of induction motors using dynamic encoding algorithm for searches (DEAS), IEEE Trans. Energy Convers., 2005, vol. 20, no. 1, pp. 16–24.CrossRefGoogle Scholar
  14. 14.
    Huang, K., et al., Parameter identification of an induction machine using genetic algorithms, Proceedings of the 1999 IEEE International Symposium on Computer Aided Control System Design, 1999.Google Scholar
  15. 15.
    Bongard, J.C. and Lipson, H., Nonlinear system identification using coevolution of models and tests, IEEE Trans. Evol. Comput., 2005, vol. 9, no. 4, pp. 361–384.CrossRefGoogle Scholar
  16. 16.
    Handa, H., et al., Genetic algorithm involving coevolution mechanism to search for effective genetic information, IEEE International Conference on Evolutionary Computation, 1997.Google Scholar
  17. 17.
    Bavi, O., Bavi, N., and Shishesaz, M., Geometrical optimization of the overlap in mixed adhesive lap joints, J. Adhes., 2013, vol. 89, no. 12, pp. 948–972.Google Scholar
  18. 18.
    Assareh, E., et al., Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran, Energy, 2010, vol. 35, no. 12, pp. 5223–5229.CrossRefGoogle Scholar
  19. 19.
    Dasgupta, J., Sikder, J., and Mandal, D., Modeling and optimization of polymer enhanced ultrafiltration using hybrid neural-genetic algorithm based evolutionary approach, Appl. Soft Comput., 2017, vol. 55, pp. 108–126.CrossRefGoogle Scholar
  20. 20.
    Yu, W., et al., Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design, Energy Build., 2015, vol. 88, pp. 135–143.CrossRefGoogle Scholar
  21. 21.
    Rezaee, A., Using genetic algorithms for designing of FIR digital filters, ICTACT J. Soft Comput., 2010, vol. 1, no. 1, pp. 18–22.CrossRefGoogle Scholar
  22. 22.
    Rezaee, A., Using coevolutionary genetic algorithms for estimation of blind FIR channel, Wireless Pers. Commun., 2015, vol. 83, no. 1, pp. 191–201.CrossRefGoogle Scholar
  23. 23.
    Haupt, R.L., Haupt, S.E., and Haupt, S.E., Practical Genetic Algorithms, New York: Wiley, 1998, vol. 2.zbMATHGoogle Scholar
  24. 24.
    Pagie, L. and Mitchell, M., A comparison of evolutionary and coevolutionary search, Int. J. Comput. Intell. Appl., 2002, vol. 2, no. 1, pp. 53–69.CrossRefGoogle Scholar
  25. 25.
    Potter, M.A., The Design and Analysis of a Computational Model of Cooperative Coevolution, Citeseer, 1997.Google Scholar
  26. 26.
    Wallin, D., Ryan, C., and Azad, R.M.A., Symbiogenetic coevolution, The 2005 IEEE Congress on Evolutionary Computation, 2005.Google Scholar
  27. 27.
    Seshadri, M., Comprehensibility, Overfitting and Co-Evolution in Genetic Programming for Technical Trading Rules, Worcester Polytechnic Institute, 2003.Google Scholar
  28. 28.
    Rezaee, A., Genetic symbiosis algorithm generating test data for constraint automata, Appl. Comput. Math., 2008, vol. 6, no. 1, pp. 126–137.MathSciNetzbMATHGoogle Scholar
  29. 29.
    Alonge, F., D’Ippolito, F., and Raimondi, F.M., Least squares and genetic algorithms for parameter identification of induction motors, Control Eng. Pract., 2001, vol. 9, no. 6, pp. 647–657.CrossRefGoogle Scholar
  30. 30.
    Pillay, P., Nolan, R., and Haque, T., Application of genetic algorithms to motor parameter determination for transient torque calculations, IEEE Trans. Ind. Appl., 1997, vol. 33, no. 5, pp. 1273–1282.CrossRefGoogle Scholar

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© Allerton Press, Inc. 2019

Authors and Affiliations

  1. 1.Department of Mechatronics and Systems Engineering, Faculty of New Sciences and Technologies, University of TehranTehranIran

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