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

Результат исследований: Материалы для книги/типы отчетовМатериалы для конференции

Аннотация

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.

Язык оригиналаАнглийский
Название основной публикации2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
ИздательInstitute of Electrical and Electronics Engineers Inc.
ISBN (электронное издание)9781728156668
DOI
СостояниеОпубликовано - ноя 2019
Опубликовано для внешнего пользованияДа
Событие6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019 - Guayaquil, Эквадор
Продолжительность: 11 ноя 201915 ноя 2019

Серия публикаций

Название2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019

Конференция

Конференция6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
СтранаЭквадор
ГородGuayaquil
Период11.11.1915.11.19

ASJC Scopus subject areas

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

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