Optimal Adaptive Robust Pitch Control with Load Mitigation for Uncertain Variable Speed Wind Turbines
In this paper, an optimal adaptive robust pitch controller is proposed for variable speed wind turbines (VSWTs). The proposed pitch controller has stability analysis, while it simultaneously keeps the generated power of the wind turbine at the rated power and mitigates the mechanical loads on the ge...
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Amirkabir University of Technology
2021-06-01
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| Series: | AUT Journal of Electrical Engineering |
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| Online Access: | https://eej.aut.ac.ir/article_4145_1d18fe7035009a83cc816c83dcd1a5ae.pdf |
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| author | Sara Majidi Reza Shahnazi |
| author_facet | Sara Majidi Reza Shahnazi |
| author_sort | Sara Majidi |
| collection | DOAJ |
| description | In this paper, an optimal adaptive robust pitch controller is proposed for variable speed wind turbines (VSWTs). The proposed pitch controller has stability analysis, while it simultaneously keeps the generated power of the wind turbine at the rated power and mitigates the mechanical loads on the gearbox. The proposed pitch controller in this paper has two terms. The first term is a radial basis function neural network (RBFNN), to approximate unknown nonlinear functions of the wind turbine. Another term is a chattering-free continuous robust structure, which can cope with the approximation error. The weights of RBFNN and the gain of the robust structure are derived via the Lyapunov synthesis approach. It is proved that the closed-loop signals are semi-globally uniformed and ultimately bounded. The optimal parameters of the proposed controller are derived by solving a proposed multi-objective optimization problem using non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm. The effectiveness of the proposed controller is compared to the baseline PI controller designed by NREL. First, both the proposed and the baseline PI controllers are applied to the general model (2-mass model) of the wind turbine, and then they are validated via a highly reliable simulator called FAST. The results demonstrate the effectiveness and applicability of the proposed pitch controller. |
| format | Article |
| id | doaj-art-7b9b71c3b00d43d590cccafe55cb3cc0 |
| institution | Kabale University |
| issn | 2588-2910 2588-2929 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Amirkabir University of Technology |
| record_format | Article |
| series | AUT Journal of Electrical Engineering |
| spelling | doaj-art-7b9b71c3b00d43d590cccafe55cb3cc02025-08-20T03:31:52ZengAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-29102588-29292021-06-0153131610.22060/eej.2020.18344.53504145Optimal Adaptive Robust Pitch Control with Load Mitigation for Uncertain Variable Speed Wind TurbinesSara Majidi0Reza Shahnazi1Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, IranDepartment of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, IranIn this paper, an optimal adaptive robust pitch controller is proposed for variable speed wind turbines (VSWTs). The proposed pitch controller has stability analysis, while it simultaneously keeps the generated power of the wind turbine at the rated power and mitigates the mechanical loads on the gearbox. The proposed pitch controller in this paper has two terms. The first term is a radial basis function neural network (RBFNN), to approximate unknown nonlinear functions of the wind turbine. Another term is a chattering-free continuous robust structure, which can cope with the approximation error. The weights of RBFNN and the gain of the robust structure are derived via the Lyapunov synthesis approach. It is proved that the closed-loop signals are semi-globally uniformed and ultimately bounded. The optimal parameters of the proposed controller are derived by solving a proposed multi-objective optimization problem using non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm. The effectiveness of the proposed controller is compared to the baseline PI controller designed by NREL. First, both the proposed and the baseline PI controllers are applied to the general model (2-mass model) of the wind turbine, and then they are validated via a highly reliable simulator called FAST. The results demonstrate the effectiveness and applicability of the proposed pitch controller.https://eej.aut.ac.ir/article_4145_1d18fe7035009a83cc816c83dcd1a5ae.pdfwind turbineadaptive robust pitch controlleroptimizationnsga-iimopsofast simulator |
| spellingShingle | Sara Majidi Reza Shahnazi Optimal Adaptive Robust Pitch Control with Load Mitigation for Uncertain Variable Speed Wind Turbines AUT Journal of Electrical Engineering wind turbine adaptive robust pitch controller optimization nsga-ii mopso fast simulator |
| title | Optimal Adaptive Robust Pitch Control with Load Mitigation for Uncertain Variable Speed Wind Turbines |
| title_full | Optimal Adaptive Robust Pitch Control with Load Mitigation for Uncertain Variable Speed Wind Turbines |
| title_fullStr | Optimal Adaptive Robust Pitch Control with Load Mitigation for Uncertain Variable Speed Wind Turbines |
| title_full_unstemmed | Optimal Adaptive Robust Pitch Control with Load Mitigation for Uncertain Variable Speed Wind Turbines |
| title_short | Optimal Adaptive Robust Pitch Control with Load Mitigation for Uncertain Variable Speed Wind Turbines |
| title_sort | optimal adaptive robust pitch control with load mitigation for uncertain variable speed wind turbines |
| topic | wind turbine adaptive robust pitch controller optimization nsga-ii mopso fast simulator |
| url | https://eej.aut.ac.ir/article_4145_1d18fe7035009a83cc816c83dcd1a5ae.pdf |
| work_keys_str_mv | AT saramajidi optimaladaptiverobustpitchcontrolwithloadmitigationforuncertainvariablespeedwindturbines AT rezashahnazi optimaladaptiverobustpitchcontrolwithloadmitigationforuncertainvariablespeedwindturbines |