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Journal of Control Science and Engineering
Volume 2012, Article ID 736586, 8 pages
Research Article

Neural Network Compensation Control for Output Power Optimization of Wind Energy Conversion System Based on Data-Driven Control

1Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China
2Department of Mechanical and Electrical Engineering, Shan Dong Water Polytechnic, Rizhao 276826, China

Received 29 March 2012; Revised 8 May 2012; Accepted 17 May 2012

Academic Editor: Wen Yu

Copyright © 2012 T. Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Due to the uncertainty of wind and because wind energy conversion systems (WECSs) have strong nonlinear characteristics, accurate model of the WECS is difficult to be built. To solve this problem, data-driven control technology is selected and data-driven controller for the WECS is designed based on the Markov model. The neural networks are designed to optimize the output of the system based on the data-driven control system model. In order to improve the efficiency of the neural network training, three different learning rules are compared. Analysis results and SCADA data of the wind farm are compared, and it is shown that the method effectively reduces fluctuations of the generator speed, the safety of the wind turbines can be enhanced, the accuracy of the WECS output is improved, and more wind energy is captured.