Optimization of Switched Reluctance Machine Drives Using Multi-Task Learning Approach
One of the major challenges for controlling the drive of switched reluctance machines (SRMs) is to have a proper conduction angle with the working point of the motor. This is due to the non-linear relationship of the flux-linkage with the position of the rotor. The optimization problem in SRM motors...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682200309X |
| _version_ | 1797978789644337152 |
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| author | Kasra Abolfathi Mojtaba Babaei Mohammad Tabrizian Mohsen Alizadeh Bidgoli |
| author_facet | Kasra Abolfathi Mojtaba Babaei Mohammad Tabrizian Mohsen Alizadeh Bidgoli |
| author_sort | Kasra Abolfathi |
| collection | DOAJ |
| description | One of the major challenges for controlling the drive of switched reluctance machines (SRMs) is to have a proper conduction angle with the working point of the motor. This is due to the non-linear relationship of the flux-linkage with the position of the rotor. The optimization problem in SRM motors should be solved using multi-objective optimization methods because the objective functions are constantly in competition and a compromise should be established between them. In this study, we propose a multi-task learning (MTL) method to optimize this problem. The obtained results of the introduced algorithm were compared with the NSGA-II algorithm. This comparison was focused on two aspects of discipline and quality. Moreover, the covering rate of Pareto front for these two algorithms was evaluated. The accuracy of the proposed method was evaluated and the results showed that the proposed solution is efficient for the optimization problem of SRMs. |
| first_indexed | 2024-04-11T05:29:45Z |
| format | Article |
| id | doaj.art-cf1356ece52243f39080b99813c51ad3 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-11T05:29:45Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-cf1356ece52243f39080b99813c51ad32022-12-23T04:38:54ZengElsevierAlexandria Engineering Journal1110-01682022-12-0161121112911138Optimization of Switched Reluctance Machine Drives Using Multi-Task Learning ApproachKasra Abolfathi0Mojtaba Babaei1Mohammad Tabrizian2Mohsen Alizadeh Bidgoli3Department of Electrical and Computer Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, IranCorresponding author.; Department of Electrical and Computer Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, IranDepartment of Electrical and Computer Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, IranDepartment of Electrical and Computer Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, IranOne of the major challenges for controlling the drive of switched reluctance machines (SRMs) is to have a proper conduction angle with the working point of the motor. This is due to the non-linear relationship of the flux-linkage with the position of the rotor. The optimization problem in SRM motors should be solved using multi-objective optimization methods because the objective functions are constantly in competition and a compromise should be established between them. In this study, we propose a multi-task learning (MTL) method to optimize this problem. The obtained results of the introduced algorithm were compared with the NSGA-II algorithm. This comparison was focused on two aspects of discipline and quality. Moreover, the covering rate of Pareto front for these two algorithms was evaluated. The accuracy of the proposed method was evaluated and the results showed that the proposed solution is efficient for the optimization problem of SRMs.http://www.sciencedirect.com/science/article/pii/S111001682200309XSwitched Reluctance Motor (SRM)Torque rippleMulti-objective optimizationOptimizationMulti-task learningMachine learning |
| spellingShingle | Kasra Abolfathi Mojtaba Babaei Mohammad Tabrizian Mohsen Alizadeh Bidgoli Optimization of Switched Reluctance Machine Drives Using Multi-Task Learning Approach Alexandria Engineering Journal Switched Reluctance Motor (SRM) Torque ripple Multi-objective optimization Optimization Multi-task learning Machine learning |
| title | Optimization of Switched Reluctance Machine Drives Using Multi-Task Learning Approach |
| title_full | Optimization of Switched Reluctance Machine Drives Using Multi-Task Learning Approach |
| title_fullStr | Optimization of Switched Reluctance Machine Drives Using Multi-Task Learning Approach |
| title_full_unstemmed | Optimization of Switched Reluctance Machine Drives Using Multi-Task Learning Approach |
| title_short | Optimization of Switched Reluctance Machine Drives Using Multi-Task Learning Approach |
| title_sort | optimization of switched reluctance machine drives using multi task learning approach |
| topic | Switched Reluctance Motor (SRM) Torque ripple Multi-objective optimization Optimization Multi-task learning Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S111001682200309X |
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