Improving the estimation of parameters in induction motors using an evolutionary computation algorithm

DIego Oliva, Salvador Hinojosa, Marcella S.R. Martins, Erick Rodriguez-Esparza, Noe Ortega-Sanchez, Marco Perez-Cisneros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Induction Motors (IM) are the most used electrical machines in the industry, where they use significant energy percentages. The control of IM requires the knowledge of their behavior; in this sense, it is necessary to accurately estimate the internal parameters that control their performance. This process involves the optimization of linear models with different constraints. Evolutionary Algorithms (EA) are proven techniques designed to obtain better results than classical optimization methods. Most of EA suffer some limitations such as slow convergence; they also have a large number of parameters that need to be set by the designer. Therefore, to solve this problem, this paper proposes an alternative method to estimate the parameters of IM using the Electromagnetism-Like Optimization (EMO) algorithm. EMO has the advantage of using a small number of iterations. The experimental results and comparisons show that the proposed approach gives better results than related methods, regarding accuracy and convergence.

Original languageEnglish
Title of host publication2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728156668
DOIs
Publication statusPublished - Nov 2019
Externally publishedYes
Event6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019 - Guayaquil, Ecuador
Duration: 11 Nov 201915 Nov 2019

Publication series

Name2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019

Conference

Conference6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
CountryEcuador
CityGuayaquil
Period11.11.1915.11.19

Keywords

  • Electromagnetism-Like Optimization
  • Evolutionary Algorithms
  • Induction motors
  • Parameter estimation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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    Oliva, DI., Hinojosa, S., Martins, M. S. R., Rodriguez-Esparza, E., Ortega-Sanchez, N., & Perez-Cisneros, M. (2019). Improving the estimation of parameters in induction motors using an evolutionary computation algorithm. In 2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019 [9037056] (2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LA-CCI47412.2019.9037056