Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor
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Archivos
Fecha
2023
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
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Título del volumen
Editor
Institute of Electrical and Electronics Engineers Inc.
Nombre de Curso
Licencia CC
CC BY-NC-ND 4.0 ATTRIBUTION-NONCOMMERCIAL-NODERIVATIVES 4.0 INTERNATIONAL Deed
Licencia CC
https://creativecommons.org/licenses/by-nc-nd/4.0/
Resumen
Tuning the weighting factor is crucial to model predictive torque and flux control. A finite set of discrete weighting factors is utilized in this research to determine the optimum solution. The Pareto line optimization technique is implemented to prevent the occurrence of local optimum solutions. By conducting an accuracy analysis, the number of discrete weighting factors is optimized, and the number of iterations is reduced. The stator current distortion minimization criterion is used to obtain the ultimate global optimal solution from the Pareto line. This study compares the results of the proposed optimization method and the particle swarm optimization method based on experimental data from a 4 kW induction motor drive test bench. The proposed technique can achieve the global optimum weighting factor in a shorter computational duration while maintaining a slightly lower total harmonics distortion and torque ripple. © 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see.
Notas
Indexación: Scopus
Palabras clave
Induction Motor Drives, Optimization, Predictive Control, Weighting Factor
Citación
IEEE Open Journal of the Industrial Electronics Society. Volume 4, Pages 573 - 582. 2023
DOI
10.1109/OJIES.2023.3333873