Open Access
2014 Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network
Lei Wang, Shan Zuo, Y. D. Song, Zheng Zhou
Abstr. Appl. Anal. 2014(SI02): 1-7 (2014). DOI: 10.1155/2014/903493


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.


Download Citation

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." Abstr. Appl. Anal. 2014 (SI02) 1 - 7, 2014.


Published: 2014
First available in Project Euclid: 6 October 2014

zbMATH: 07023280
Digital Object Identifier: 10.1155/2014/903493

Rights: Copyright © 2014 Hindawi

Vol.2014 • No. SI02 • 2014
Back to Top