NDLI: Reinforcement learning controller for variable-speedwind energy conversion systems

Content Provider	IEEE Xplore Digital Library
Author	Meng Wenchao Yang Qinmin Sun Youxian
Copyright Year	2014
Description	Author affiliation: Dept. of Control Sci. & Eng., Zhejiang Univ., Hangzhou, China (Meng Wenchao; Yang Qinmin; Sun Youxian)
Abstract	In this paper, a reinforcement learning based adaptive critic controller is proposed for the power capture control of variable-speed wind energy conversion systems (WECSs). The control objective is to optimize the power capture from wind by tracking the maximum power curve and minimize a predefined long-term cost function in the mean time. By minimizing the long-term cost function, both the power capture and the life time of mechanical part of a wind turbine are considered as opposed to most of existing literatures. The developed controller consists of an action network and a critic network. The critic network is introduced to evaluate the performance of the action network, and learn the cost-to-go function in an online manner. The estimate of cost-to-go function is then transmitted to the action network. The action network is utilized to provide the optimal generator torque rate with the help of the estimate of cost-to-go function. Here, a two-layer neural network structure is employed for both the action and critic network. Finally, the performance of the proposed controller is evaluated on a 1.5MW three-blade wind turbine in simulating environment.
Starting Page	8877
Ending Page	8882
File Size	455530
Page Count	6
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6896494
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Torque Wind energy Rotors Artificial neural networks nonlinear uncertain systems Cost function adaptive control Generators Wind turbines Wind energy conversion systems reinforcement learning
Content Type	Text
Resource Type	Article

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