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

Carmenza Moreno Roa, Adolfo Andrés Jaramillo Matta and Juan David Bastidas Rodríguez
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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

Energies, 2020, vol. 13, issue 1, 1-21

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.

Keywords: induction motor; optimization; parameter determination; single cage model; stochastic search; variable domain constraints (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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