Deep reinforcement learning for PMSG wind turbine control via twin delayed deep deterministic policy gradient (TD3)
Zholtayev D. Rubagotti M. Do T.D.
July/August 2024John Wiley and Sons Ltd
Optimal Control Applications and Methods
2024#45Issue 41889 - 1906 pp.
This article proposes the use of a deep reinforcement learning method—and precisely a variant of the deep deterministic policy gradient (DDPG) method known as twin delayed DDPG, or TD3—for maximum power point tracking in wind energy conversion systems that use permanent magnet synchronous generators (PMSGs). An overview of the TD3 algorithm is provided, together with a detailed description of its implementation and training for the considered application. Simulation results are provided, also including a comparison with a model-based control method based on feedback linearization and linear-quadratic regulation. The proposed TD3-based controller achieves a satisfactory control performance and is more robust to PMSG parameter variations as compared to the presented model-based method. To the best of the authors knowledge, this article presents for the first time an approach for generating both speed and current control loops using DRL for wind energy conversion systems.
data-driven control , deep reinforcement learning (DRL) , maximum power point tracking (MPPT) , model-free control , permanent magnet synchronous generator (PMSG) , twin delayed deep deterministic policy gradient (TD3) , wind energy conversion system (WECS)
Text of the article Перейти на текст статьи
Department of Computer Engineering, Astana IT University, Astana, Kazakhstan
Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
Department of Computer Engineering
Department of Robotics and Mechatronics
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026