Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network
Offshore floating wind turbine (OFWT) has been a challenging research spot because of the high-quality wind power and complex load environment. This paper focuses on the research of variable torque control of offshore wind turbine on Spar floating platform. The control objective in below-rated wind...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/903493 |
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| _version_ | 1832565598258724864 |
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| author | Lei Wang Shan Zuo Y. D. Song Zheng Zhou |
| author_facet | Lei Wang Shan Zuo Y. D. Song Zheng Zhou |
| author_sort | Lei Wang |
| collection | DOAJ |
| description | Offshore floating wind turbine (OFWT) has been a challenging research spot because of the high-quality wind power and complex load environment. This paper focuses on the research of variable torque control of offshore wind turbine on Spar floating platform. The control objective in below-rated wind speed region is to optimize the output power by tracking the optimal tip-speed ratio and ideal power curve. Aiming at the external disturbances and nonlinear uncertain dynamic systems of OFWT because of the proximity to load centers and strong wave coupling, this paper proposes an advanced radial basis function (RBF) neural network approach for torque control of OFWT system at speeds lower than rated wind speed. The robust RBF neural network weight adaptive rules are acquired based on the Lyapunov stability analysis. The proposed control approach is tested and compared with the NREL baseline controller using the “NREL offshore 5 MW wind turbine” model mounted on a Spar floating platform run on FAST and Matlab/Simulink, operating in the below-rated wind speed condition. The simulation results show a better performance in tracking the optimal output power curve, therefore, completing the maximum wind energy utilization. |
| format | Article |
| id | doaj-art-9b8aface5db446fbb6b8d9be980262b5 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-9b8aface5db446fbb6b8d9be980262b52025-02-03T01:07:16ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/903493903493Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural NetworkLei Wang0Shan Zuo1Y. D. Song2Zheng Zhou3Intelligent Systems and New Energy Technology Research Institute, Chongqing University, Chongqing 400044, ChinaInstitute of Intelligent System and Renewable Energy Technology, University of Electronic Science and Technology of China, Chengdu 611731, ChinaIntelligent Systems and New Energy Technology Research Institute, Chongqing University, Chongqing 400044, ChinaWeb Science Center, University of Electronic Science and Technology of China, Chengdu 611731, ChinaOffshore floating wind turbine (OFWT) has been a challenging research spot because of the high-quality wind power and complex load environment. This paper focuses on the research of variable torque control of offshore wind turbine on Spar floating platform. The control objective in below-rated wind speed region is to optimize the output power by tracking the optimal tip-speed ratio and ideal power curve. Aiming at the external disturbances and nonlinear uncertain dynamic systems of OFWT because of the proximity to load centers and strong wave coupling, this paper proposes an advanced radial basis function (RBF) neural network approach for torque control of OFWT system at speeds lower than rated wind speed. The robust RBF neural network weight adaptive rules are acquired based on the Lyapunov stability analysis. The proposed control approach is tested and compared with the NREL baseline controller using the “NREL offshore 5 MW wind turbine” model mounted on a Spar floating platform run on FAST and Matlab/Simulink, operating in the below-rated wind speed condition. The simulation results show a better performance in tracking the optimal output power curve, therefore, completing the maximum wind energy utilization.http://dx.doi.org/10.1155/2014/903493 |
| spellingShingle | Lei Wang Shan Zuo Y. D. Song Zheng Zhou Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network Abstract and Applied Analysis |
| title | Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network |
| title_full | Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network |
| title_fullStr | Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network |
| title_full_unstemmed | Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network |
| title_short | Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network |
| title_sort | variable torque control of offshore wind turbine on spar floating platform using advanced rbf neural network |
| url | http://dx.doi.org/10.1155/2014/903493 |
| work_keys_str_mv | AT leiwang variabletorquecontrolofoffshorewindturbineonsparfloatingplatformusingadvancedrbfneuralnetwork AT shanzuo variabletorquecontrolofoffshorewindturbineonsparfloatingplatformusingadvancedrbfneuralnetwork AT ydsong variabletorquecontrolofoffshorewindturbineonsparfloatingplatformusingadvancedrbfneuralnetwork AT zhengzhou variabletorquecontrolofoffshorewindturbineonsparfloatingplatformusingadvancedrbfneuralnetwork |