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Mathematical Problems in Engineering
Volume 2014, Article ID 412027, 9 pages
Research Article

A Neural Network Controller for Variable-Speed Variable-Pitch Wind Energy Conversion Systems Using Generalized Minimum Entropy Criterion

1State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
2School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
3DaTang Technology Industry Group Co., Ltd., Beijing 100097, China

Received 10 April 2014; Revised 20 July 2014; Accepted 28 July 2014; Published 12 August 2014

Academic Editor: M. I. Herreros

Copyright © 2014 Mifeng Ren 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.


This paper considers the neural network controller design problem for variable pitch wind energy conversion systems (WECS) with non-Gaussian wind speed disturbances in the stochastic distribution control framework. The approach here is used to directly model the unknown control law based on a fixed neural network (the number of layers and nodes in a neural network is fixed) without the need to construct a separate model for the WECS. In order to characterize the randomness of the WECS, a generalized minimum entropy criterion is established to train connection weights of the neural network. For the train purpose, both kernel density estimation method and sliding window technique are adopted to estimate the PDF of tracking error and entropies. Due to the unknown process dynamics, the gradient of the objective function in a gradient-descent-type algorithm is estimated using an incremental perturbation method. The proposed approach is illustrated on a simulated WECS with non-Gaussian wind speed.