Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning. (October 2022)

• Record Type: Journal Article
• Authors: Fang, Jianhao; Hu, Weifei; Liu, Zhenyu; Chen, Weiyi; Tan, Jianrong; Jiang, Zhiyu; Verma, Amrit Shankar
• Is Part Of: Renewable & sustainable energy reviews. Volume 168(2022)
• Publication Date: 2022-10
• Subjects: Wind turbine blade -- Rain erosion -- Rotor speed -- Design optimization -- Deep reinforcement learning -- Deep deterministic policy gradient; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2022.112788
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Rain erosion is one of the most detrimental factors contributing to wind turbine blade (WTB) coating fatigue damage especially for utility-scale wind turbines (WTs). To prevent rain erosion induced WTB coating fatigue damage, this paper proposes a deep reinforcement learning (DRL)-based optimization method for finding the optimal rotor speed under different rain intensities and wind speeds. First, an efficient physics-based model for predicting WTB coating fatigue damage considering the comprehensive blade coating fatigue mechanism, rain intensity distribution, and wind speed distribution is presented. Then, a WT rotor speed design optimization problem is constructed to search for the optimal rotor speed under different rain intensity and wind speed conditions. To address the challenge of optimizing the efficiency, the original design optimization problem is converted into a DRL-based design optimization model. A hybrid reward is proposed to enhance the DRL agent trained by a deep deterministic policy gradient algorithm. Finally, the proposed DRL-based design optimization method is utilized to guide the optimal rotor speed scheduling of a 5-MW WT under given wind speed and rain intensity conditions. The results show that the proposed method could extend the predicted WTB blade coating fatigue life by 2.55 times with a minor reduction in the energy yield (0.027%) compared to the original rotor speed schedule that only considers maximum power capture. The computational time of the proposed method is reduced significantly compared to that of the traditional gradient and evolutional design optimization methods. Highlights: A fast and accurate physics-based surrogate model is created for predicting the wind turbine blade coating fatigue. A deep reinforcement learning-based optimization method is proposed for designing high-dimensional wind turbine rotor speeds. A new hybrid reward is proposed to facilitate the agent training by the deep deterministic policy gradient algorithm.