Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance
In this paper, a thorough framework for multiobjective design optimization of switched reluctance motor (SRM) is proposed. Selection of stator and rotor pole embrace coefficients is an essential step in the SRM design process since it influences torque output and torque ripple in SRM. The problem of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/16/6086 |
| _version_ | 1797432220645851136 |
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| author | Chiweta Emmanuel Abunike Ogbonnaya Inya Okoro Sumeet S. Aphale |
| author_facet | Chiweta Emmanuel Abunike Ogbonnaya Inya Okoro Sumeet S. Aphale |
| author_sort | Chiweta Emmanuel Abunike |
| collection | DOAJ |
| description | In this paper, a thorough framework for multiobjective design optimization of switched reluctance motor (SRM) is proposed. Selection of stator and rotor pole embrace coefficients is an essential step in the SRM design process since it influences torque output and torque ripple in SRM. The problem of determining optimal pole embrace is formulated as a multi-objective optimization problem with the objective of optimizing average torque, efficiency and torque ripple, and response surface models were obtained based on the genetic aggregation method. The results obtained by genetic aggregation response surface (GARS) and the non-dominated genetic algorithm (NSGA-II) were validated with the finite element method (FEM) model of the initial SRM. The optimized model displayed better efficiency profile over a wide speed range. The initial and optimized models recorded maximum efficiencies of 85% and 94.05%, respectively, at 2000 rpm. The efficiency values of 93.97–94.05% were achieved for the three pareto optimal candidates. The findings indicate the viability of the suggested strategy and support the use of GARS and NSGA-II as useful methods for addressing SRM key challenges. |
| first_indexed | 2024-03-09T09:57:10Z |
| format | Article |
| id | doaj.art-453c548114364462b1266efb301e63c9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T09:57:10Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-453c548114364462b1266efb301e63c92023-12-01T23:40:41ZengMDPI AGEnergies1996-10732022-08-011516608610.3390/en15166086Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved PerformanceChiweta Emmanuel Abunike0Ogbonnaya Inya Okoro1Sumeet S. Aphale2School of Engineering, University of Aberdeen, Aberdeen AB24 3UE, UKDepartment of Electrical/Electronic Engineering, Michael Okpara University of Agriculture, Umudike 440101, Abia State, NigeriaSchool of Engineering, University of Aberdeen, Aberdeen AB24 3UE, UKIn this paper, a thorough framework for multiobjective design optimization of switched reluctance motor (SRM) is proposed. Selection of stator and rotor pole embrace coefficients is an essential step in the SRM design process since it influences torque output and torque ripple in SRM. The problem of determining optimal pole embrace is formulated as a multi-objective optimization problem with the objective of optimizing average torque, efficiency and torque ripple, and response surface models were obtained based on the genetic aggregation method. The results obtained by genetic aggregation response surface (GARS) and the non-dominated genetic algorithm (NSGA-II) were validated with the finite element method (FEM) model of the initial SRM. The optimized model displayed better efficiency profile over a wide speed range. The initial and optimized models recorded maximum efficiencies of 85% and 94.05%, respectively, at 2000 rpm. The efficiency values of 93.97–94.05% were achieved for the three pareto optimal candidates. The findings indicate the viability of the suggested strategy and support the use of GARS and NSGA-II as useful methods for addressing SRM key challenges.https://www.mdpi.com/1996-1073/15/16/6086efficiencygenetic aggregation response surfacegenetic algorithmpole embrace coefficientsswitched reluctance motortorque ripple |
| spellingShingle | Chiweta Emmanuel Abunike Ogbonnaya Inya Okoro Sumeet S. Aphale Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance Energies efficiency genetic aggregation response surface genetic algorithm pole embrace coefficients switched reluctance motor torque ripple |
| title | Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance |
| title_full | Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance |
| title_fullStr | Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance |
| title_full_unstemmed | Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance |
| title_short | Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance |
| title_sort | intelligent optimization of switched reluctance motor using genetic aggregation response surface and multi objective genetic algorithm for improved performance |
| topic | efficiency genetic aggregation response surface genetic algorithm pole embrace coefficients switched reluctance motor torque ripple |
| url | https://www.mdpi.com/1996-1073/15/16/6086 |
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