Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor

Vista sencilla de ítem
dc.contributor.authorAlireza Davari S.
dc.contributor.authorNekoukar, Vahab
dc.contributor.authorAzadi, Shirin
dc.contributor.authorFlores-Bahamonde, Freddy
dc.contributor.authorGarcia, Cristian
dc.contributor.authorRodriguez, Jose
dc.date.accessioned2024-07-26T19:42:07Z
dc.date.available2024-07-26T19:42:07Z
dc.date.issued2023
dc.descriptionIndexación: Scopus
dc.description.abstractTuning 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.
dc.description.urihttps://ieeexplore-ieee-org.recursosbiblioteca.unab.cl/document/10330016
dc.identifier.citationIEEE Open Journal of the Industrial Electronics Society. Volume 4, Pages 573 - 582. 2023
dc.identifier.doi10.1109/OJIES.2023.3333873
dc.identifier.issn2644-1284
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/58769
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.licenseCC BY-NC-ND 4.0 ATTRIBUTION-NONCOMMERCIAL-NODERIVATIVES 4.0 INTERNATIONAL Deed
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectInduction Motor Drives
dc.subjectOptimization
dc.subjectPredictive Control
dc.subjectWeighting Factor
dc.titleDiscrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor
dc.typeArtículo
Archivos
Bloque original
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
Alireza_Davari_Discrete_Optimization_of_Weighting_Factor_in_Model_Predictive_2023.pdf
Tamaño:
3.66 MB
Formato:
Adobe Portable Document Format
Descripción:
TEXTO COMPLETO EN INGLÉS
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar
Colecciones
FIng - Artículos de Revista