Computationally inexpensive approach for pitch control of offshore wind turbine on barge floating platform.
Offshore floating wind turbine (OFWT) has gained increasing attention during the past decade because of the offshore high-quality wind power and complex load environment. The control system is a tradeoff between power tracking and fatigue load reduction in the above-rated wind speed area. In allusion to the external disturbances and uncertain system parameters of OFWT due to the proximity to load centers and strong wave coupling, this paper proposes a computationally inexpensive robust adaptive control approach with memory-based compensation for blade pitch control. The method is tested and compared with a baseline controller and a conventional individual blade pitch controller with the "NREL offshore 5 MW baseline wind turbine" being mounted on a barge platform run on FAST and Matlab/Simulink, operating in the above-rated condition. It is shown that the advanced control approach is not only robust to complex wind and wave disturbances but adaptive to varying and uncertain system parameters as well. The simulation results demonstrate that the proposed method performs better in reducing power fluctuations, fatigue loads and platform vibration as compared to the conventional individual blade pitch control.
Institute of Intelligent System and Renewable Energy Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
Shan
S
Zuo
Institute of Intelligent System and Renewable Energy Technology, University of Electronic Science and Technology of China, Chengdu 611731, China ; Intelligent Systems and New Energy Technology Research Institute, Chongqing University, Chongqing 400044, China.
Y D
YD
Song
Institute of Intelligent System and Renewable Energy Technology, University of Electronic Science and Technology of China, Chengdu 611731, China ; Intelligent Systems and New Energy Technology Research Institute, Chongqing University, Chongqing 400044, China.
Lei
L
Wang
Institute of Intelligent System and Renewable Energy Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
Qing-wang
QW
Song
D008962:Models, Theoretical; D014919:Wind