Support Vector Regression Inverse System Control for Small Wind Turbine MPPT with Parameters’ Robustness Improvement
With the increasing penetration of the permanent-magnet direct-drive wind power system, the maximum wind-energy capture and the generation speed control are more and more important. In the literature, the dynamic performance of the generator speed is well documented by the inverse system method. How...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2978380 |
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| _version_ | 1849305469339828224 |
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| author | Hongru Wang Zhigang Zhang Wenjuan Zhang Mengdi Li Yang Zhang |
| author_facet | Hongru Wang Zhigang Zhang Wenjuan Zhang Mengdi Li Yang Zhang |
| author_sort | Hongru Wang |
| collection | DOAJ |
| description | With the increasing penetration of the permanent-magnet direct-drive wind power system, the maximum wind-energy capture and the generation speed control are more and more important. In the literature, the dynamic performance of the generator speed is well documented by the inverse system method. However, conventional inverse system methods have parameter dependency that is not sufficient to meet the dynamic requirements for permanent magnet synchronous generator (PMSG) speed tracking. Therefore, this paper introduces a support vector regression machine (SVR) method, especially for the inverse system model, which could solve the inaccurate parameters problems. As the SVR has the nonlinear approximation ability to identify and adjust the parameters online, thus, the system robustness could be improved. Finally, the dynamic performance of generator speed is evaluated by using the SVR method. Proposed theoretical developments are verified by the Simulink Test and experimental test. |
| format | Article |
| id | doaj-art-0a283309da984417bd2e4da09125ebc3 |
| institution | Kabale University |
| issn | 1687-5257 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-0a283309da984417bd2e4da09125ebc32025-08-20T03:55:27ZengWileyJournal of Control Science and Engineering1687-52572022-01-01202210.1155/2022/2978380Support Vector Regression Inverse System Control for Small Wind Turbine MPPT with Parameters’ Robustness ImprovementHongru Wang0Zhigang Zhang1Wenjuan Zhang2Mengdi Li3Yang Zhang4School of Electronic Information and Electrical EngineeringSchool of Electronic Information and Electrical EngineeringSchool of Electronic Information and Electrical EngineeringSchool of Electronic Information and Electrical EngineeringSchool of Electronic Information and Electrical EngineeringWith the increasing penetration of the permanent-magnet direct-drive wind power system, the maximum wind-energy capture and the generation speed control are more and more important. In the literature, the dynamic performance of the generator speed is well documented by the inverse system method. However, conventional inverse system methods have parameter dependency that is not sufficient to meet the dynamic requirements for permanent magnet synchronous generator (PMSG) speed tracking. Therefore, this paper introduces a support vector regression machine (SVR) method, especially for the inverse system model, which could solve the inaccurate parameters problems. As the SVR has the nonlinear approximation ability to identify and adjust the parameters online, thus, the system robustness could be improved. Finally, the dynamic performance of generator speed is evaluated by using the SVR method. Proposed theoretical developments are verified by the Simulink Test and experimental test.http://dx.doi.org/10.1155/2022/2978380 |
| spellingShingle | Hongru Wang Zhigang Zhang Wenjuan Zhang Mengdi Li Yang Zhang Support Vector Regression Inverse System Control for Small Wind Turbine MPPT with Parameters’ Robustness Improvement Journal of Control Science and Engineering |
| title | Support Vector Regression Inverse System Control for Small Wind Turbine MPPT with Parameters’ Robustness Improvement |
| title_full | Support Vector Regression Inverse System Control for Small Wind Turbine MPPT with Parameters’ Robustness Improvement |
| title_fullStr | Support Vector Regression Inverse System Control for Small Wind Turbine MPPT with Parameters’ Robustness Improvement |
| title_full_unstemmed | Support Vector Regression Inverse System Control for Small Wind Turbine MPPT with Parameters’ Robustness Improvement |
| title_short | Support Vector Regression Inverse System Control for Small Wind Turbine MPPT with Parameters’ Robustness Improvement |
| title_sort | support vector regression inverse system control for small wind turbine mppt with parameters robustness improvement |
| url | http://dx.doi.org/10.1155/2022/2978380 |
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