Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor
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. B...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of the Industrial Electronics Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10330016/ |
| _version_ | 1797339240658370560 |
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| author | S. Alireza Davari Vahab Nekoukar Shirin Azadi Freddy Flores-Bahamonde Cristian Garcia Jose Rodriguez |
| author_facet | S. Alireza Davari Vahab Nekoukar Shirin Azadi Freddy Flores-Bahamonde Cristian Garcia Jose Rodriguez |
| author_sort | S. Alireza Davari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T09:43:06Z |
| format | Article |
| id | doaj.art-c657ba8a21334a008d6620413369dd6e |
| institution | Directory Open Access Journal |
| issn | 2644-1284 |
| language | English |
| last_indexed | 2024-03-08T09:43:06Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Industrial Electronics Society |
| spelling | doaj.art-c657ba8a21334a008d6620413369dd6e2024-01-30T00:07:16ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842023-01-01457358210.1109/OJIES.2023.333387310330016Discrete Optimization of Weighting Factor in Model Predictive Control of Induction MotorS. Alireza Davari0https://orcid.org/0000-0003-4337-1197Vahab Nekoukar1https://orcid.org/0000-0001-5196-1264Shirin Azadi2https://orcid.org/0009-0007-9125-7483Freddy Flores-Bahamonde3https://orcid.org/0000-0001-5043-1077Cristian Garcia4https://orcid.org/0000-0002-7939-422XJose Rodriguez5https://orcid.org/0000-0002-1410-4121Department of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, IranDepartment of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, IranFaculty of Engineering, Universidad Andres Bello, Santiago, ChileFaculty of Engineering, Universidad Andres Bello, Santiago, ChileFaculty of Engineering, Universidad de Talca, Santiago, ChileFaculty of Engineering, Universidad San Sebastian, Santiago, ChileTuning 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.https://ieeexplore.ieee.org/document/10330016/Induction motor drivesoptimizationpredictive controlweighting factor |
| spellingShingle | S. Alireza Davari Vahab Nekoukar Shirin Azadi Freddy Flores-Bahamonde Cristian Garcia Jose Rodriguez Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor IEEE Open Journal of the Industrial Electronics Society Induction motor drives optimization predictive control weighting factor |
| title | Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor |
| title_full | Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor |
| title_fullStr | Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor |
| title_full_unstemmed | Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor |
| title_short | Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor |
| title_sort | discrete optimization of weighting factor in model predictive control of induction motor |
| topic | Induction motor drives optimization predictive control weighting factor |
| url | https://ieeexplore.ieee.org/document/10330016/ |
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