Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network

Abstract and Applied Analysis

Research Article

Zheng Zhou³  Y. D. Song²  Shan Zuo¹  Lei Wang² 

[1] Institute of Intelligent System and Renewable Energy Technology, University of Electronic Science and Technology of China, Chengdu 611731, China, uestc.edu.cn;Intelligent Systems and New Energy Technology Research Institute, Chongqing University, Chongqing 400044, China, cqu.edu.cn;Institute of Intelligent System and Renewable Energy Technology, University of Electronic Science and Technology of China, Chengdu 611731, China, uestc.edu.cn;Web Science Center, University of Electronic Science and Technology of China, Chengdu 611731, China, uestc.edu.cn

Others  :  1320892
DOI  :  10.1155/2014/903493

 received in 2014-01-02, accepted in 2014-01-15,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

CC BY   
Copyright © 2014 Lei Wang et al. 2014

【 图 表 】

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