Reinforcement Learning Controller for Variable-speed Wind Energy Conversion Systems

摘要：

　　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.

关键词： Wind energy conversion systems adaptive control nonlinear uncertain systems reinforcement learning

作者: Meng Wenchao Yang Qinmin Youxian Sun

作者单位: State Key Laboratory of Industrial Control Technology,Department of Control Science and Engineering,Zhejiang University,Hangzhou Zhejiang 310027,P.R.China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 8877-8882

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）