Data-Driven Torque and Pitch Control of Wind Turbines via Reinforcement Learning

Jingjie Xie, Xiaowei Zhao, Hongyang Dong

Research output: Journal article publicationJournal articleAcademic researchpeer-review


This paper addresses the torque and pitch control problems of wind turbines. The main contribution of
this work is the development of an innovative reinforcement learning (RL)-based control method targeting
wind turbine applications. Our RL-based control framework synergistically combines the advantages of deep
neural networks (DNNs) and model predictive control (MPC) technologies. The proposed control strategy
is data-driven, adapting to real-time changes in system dynamics and enhancing control performance and
robustness. Additionally, the incorporation of an MPC structure within our design improves learning efficiency
and reduces the high computational complexity typically found in deep RL algorithms. Specifically, a DNN is
designed to approximate the wind turbine dynamics based on a continuously updated dataset composed of
state and action measurements taken at specified sampling intervals. The real-time control policy is generated
by integrating the online trained DNN into an MPC architecture. The proposed method iteratively updates the
DNN and control policy in real-time to optimize performance. As a primary result of this work, the proposed
method demonstrates superior robustness and control performance compared to commonly-employed MPC and
other baseline wind turbine controllers in the presence of uncertainties and unexpected actuator faults. This
effectiveness is showcased through simulations with a high-fidelity wind turbine simulator.
Original languageEnglish
Number of pages10
JournalRenewable Energy
Issue number118893
Publication statusPublished - 2023


  • Wind turbine control, Reinforcement learning, Deep neural network, Model predictive control


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