Advertisement

Metaheuristic Schemes for Parameter Estimation in Induction Motors

  • Erik CuevasEmail author
  • Emilio Barocio Espejo
  • Arturo Conde Enríquez
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 822)

Abstract

Induction motors represent the main component in most of the industries. They use the biggest energy percentages in industrial facilities. This consume depends on the operation conditions of the induction motor imposed by its internal parameters. In this approach, the parameter estimation process is transformed into a multidimensional optimization problem where the internal parameters of the induction motor are considered as decision variables. Thus, the complexity of the optimization problem tends to produce multimodal error surfaces in which their cost functions are significantly difficult to minimize.

References

  1. 1.
    H. Çaliş, A. Çakir, E. Dandil, Artificial immunity-based induction motor bearing fault diagnosis. Turk J. Elec. Eng. Comp. Sci. 21(1), 1–25 (2013)Google Scholar
  2. 2.
    V. Prakash, S. Baskar, S. Sivakumar, K.S. Krishna, A novel efficiency improvement measure in three-phase induction motors, its conservation potential and economic analysis. Energy Sustain. Dev. 12(2), 78–87 (2008)CrossRefGoogle Scholar
  3. 3.
    S.S. Waters, R.D. Willoughby, Modeling induction motors for system studies. IEEE Trans. Ind. Appl. IA-19(5), 875–878 (1983)CrossRefGoogle Scholar
  4. 4.
    S. Ansuj, F. Shokooh, R. Schinzinger, Parameter estimation for induction machines based on sensitivity\nanalysis. IEEE Trans. Ind. Appl. 25(6), 1035–1040 (1989)CrossRefGoogle Scholar
  5. 5.
    J. De Kock, F. Van der Merwe, H. Vermeulen, Induction motor parameter estimation through an output error technique. IEEE Trans. Energy Conver. 9(1), 69–76 (1994)CrossRefGoogle Scholar
  6. 6.
    H.R. Mohammadi, A. Akhavan, Parameter estimation of three-phase induction motor using hybrid of genetic algorithm and particle swarm optimization, J Eng, 2014(148204), 6 (2014)CrossRefGoogle Scholar
  7. 7.
    R.R. Bishop, G.G. Richards, Identifying induction machine parameters using a genetic optimization algorithm, IEEE Proc Southeastcon, New Orleans, LA, USA, 476–479 (1990)Google Scholar
  8. 8.
    D. Lindenmeyer, H.W. Dommel, A. Moshref, P. Kundur, An induction motor parameter estimation method. Int. J. Elec. Power Energy Syst. 23(4), 251–262 (2001)CrossRefGoogle Scholar
  9. 9.
    V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian, An improved particle swarm optimization for induction motor parameter determination. Int. J. Comp. Appl. 1(2), 71–76 (2010)Google Scholar
  10. 10.
    V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian, Artificial immune system for parameter estimation of induction motor. Expert Syst. Appl. 37(8), 6109–6115 (2010)CrossRefGoogle Scholar
  11. 11.
    V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian, An accurate and economical approach for induction motor field efficiency estimation using bacterial foraging algorithm. Meas. J. Int. Meas. Confed. 44(4), 674–684 (2011)CrossRefGoogle Scholar
  12. 12.
    I. Perez, M. Gomez-Gonzalez, F. Jurado, Estimation of induction motor parameters using shuffled frog-leaping algorithm. Elec. Eng. 95(3), 267–275 (2013)CrossRefGoogle Scholar
  13. 13.
    A.G. Abro, J. Mohamad-Saleh, Multiple-global-best guided artificial bee colony algorithm for induction motor parameter estimation. Turk. J. Elec. Eng. Comp. Sci. 22, 620–636 (2014)CrossRefGoogle Scholar
  14. 14.
    E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: a gravitational search algorithm. Inf. Sci. (Ny). 179(13), 2232–2248 (2009)CrossRefGoogle Scholar
  15. 15.
    F. Farivar, M.A. Shoorehdeli, Stability analysis of particle dynamics in gravitational search optimization algorithm. Inf. Sci. 337–338 (April), 25–43 (2016)CrossRefGoogle Scholar
  16. 16.
    S. Yazdani, H. Nezamabadi-pour, S. Kamyab, A gravitational search algorithm for multimodal optimization. Swarm Evol. Comput. 14, 1–14 (2014)CrossRefGoogle Scholar
  17. 17.
    S.D. Beigvand, H. Abdi, M. La Scala, Combined heat and power economic dispatch problem using gravitational search algorithm. Elect. Power Syst. Res. 133 (April), 160–172 (2016)CrossRefGoogle Scholar
  18. 18.
    V. Kumar, J.K. Chhabra, D. Kumar, Automatic cluster evolution using gravitational search algorithm and its application on image segmentation. Eng. Appl. Artif. Intell. 29 (March), 93–103 (2014)CrossRefGoogle Scholar
  19. 19.
    W. Zhang, P. Niu, G. Li, P. Li, Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm. Knowl-Based Syst. 39, 34–44 (2013)CrossRefGoogle Scholar
  20. 20.
    D. Karaboga, An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, no. TR06, Erciyes University, p. 10, 2005Google Scholar
  21. 21.
    R. Storn, K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 341–359 (1997)Google Scholar
  22. 22.
    J. Kennedy, R. Eberhart, Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 1995 (ICNN’95), vol. 4 (1995), pp. 1942–1948Google Scholar
  23. 23.
    V.P. Sakthivel, S. Subramanian, On-site efficiency evaluation of three-phase induction motor based on particle swarm optimization. Energy 36(3), 1713–1720 (2011)CrossRefGoogle Scholar
  24. 24.
    M. Jamadi, F. Merrikh-bayat, New method for accurate parameter estimation of induction motors based on artificial bee colony algorithm, Cornell Univ. Library, New York, NY, USA, Tech. Rep. (2014)Google Scholar
  25. 25.
    R.K. Ursem, P. Vadstrup, Parameter identification of induction motors using differential evolution. Congr. Evol. Comp. 2003 (CEC’03) 2, 790–796 (2003)Google Scholar
  26. 26.
    F. Wilcoxon, in Breakthroughs in Statistics: Methodology and Distribution, ed. by S. Kotz, N.L. Johnson (Springer, New York, NY, 1992), pp. 196–202Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Erik Cuevas
    • 1
    Email author
  • Emilio Barocio Espejo
    • 2
  • Arturo Conde Enríquez
    • 3
  1. 1.Departamento de Electrónica, CUCEIUniversidad de GuadalajaraGuadalajaraMexico
  2. 2.CUCEIUniversidad de GuadalajaraGuadalajaraMexico
  3. 3.Universidad Autónoma de Nuevo LeónSan Nicolás de los GarzaMexico

Personalised recommendations