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Stochastic Search Technique with Variable Deterministic Constraints for the Estimation of Induction Motor Parameters

Author

Listed:
  • Carmenza Moreno Roa

    (Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 110221, Colombia)

  • Adolfo Andrés Jaramillo Matta

    (Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 110221, Colombia)

  • Juan David Bastidas Rodríguez

    (Facultad de Ingeniería y Arquitectura, Departamento Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia, Manizales 170003, Colombia)

Abstract

This paper deals with the implementation of a new technique of stochastic search to find the best set of parameters in a mathematical model, applied to the single cage (SC) model of the induction motor (IM). The technique includes a new strategy to generate variable constraints of the domain, seven error functions, weight for the operating zones of the IM, and multi-objective functions. The results are validated with experimental data of the torque and current in an IM, and show better fitting to the experimental curves compared with the results of two different techniques, one deterministic and the other one stochastic. The results obtained allow us to conclude that the best set of parameters for the model depends on the weights assigned to the objective functions and to the operating zones.

Suggested Citation

  • Carmenza Moreno Roa & Adolfo Andrés Jaramillo Matta & Juan David Bastidas Rodríguez, 2020. "Stochastic Search Technique with Variable Deterministic Constraints for the Estimation of Induction Motor Parameters," Energies, MDPI, vol. 13(1), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:273-:d:305520
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    References listed on IDEAS

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    1. Jing Tang & Yongheng Yang & Frede Blaabjerg & Jie Chen & Lijun Diao & Zhigang Liu, 2018. "Parameter Identification of Inverter-Fed Induction Motors: A Review," Energies, MDPI, vol. 11(9), pages 1-21, August.
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