Wind Farm Power Generation Control via Double-Network-Based Deep Reinforcement Learning

Jingjie Xie, Hongyang Dong, Xiaowei Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review


A model-free deep reinforcement learning
(DRL) method is proposed in this article to maximize the
total power generation of wind farms through the combination of induction control and yaw control. Specifically,
a novel double-network (DN)-based DRL approach is designed to generate control policies for thrust coefficients
and yaw angles simultaneously and separately. Two sets
of critic-actor networks are constructed to this end. They
are linked by a central power-related reward, providing a
coordinated control structure while inheriting the critic-actor mechanism’s advantages. Compared with conventional DRL methods, the proposed DN-based DRL strategy can adapt to the distinctive and incompatible features
of different control inputs, guaranteeing a reliable training
process and ensuring superior performance. Also, the prioritized experience replay strategy is utilized to improve
the training efficiency of deep neural networks. Simulation
tests based on a dynamic wind farm simulator show that
the proposed method can significantly increase the power
generation for wind farms with different layouts.
Original languageEnglish
Pages (from-to)2321-2330
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number4
Publication statusPublished - 2021


  • Model-free control, power generation control, reinforcement learning (RL), wind farm control.


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