Data-Driven Wind Farm Control via Multiplayer Deep Reinforcement Learning

• Published in: IEEE transactions on control systems technology Vol. 31; no. 3; pp. 1468 - 1475
• Main Authors: Dong, Hongyang, Zhao, Xiaowei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerodynamics; Artificial neural networks; Case studies; Control methods; Deep learning; Machine learning; Optimal control; Optimization; reinforcement learning; Robustness; Steady state models; Steady-state; Task analysis; Time measurement; wind energy; wind farm control; Wind farms; Wind power; Wind speed; wind turbine control; Wind turbines
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2022.3223185

This brief proposes a novel data-driven control scheme to maximize the total power output of wind farms subject to strong aerodynamic interactions among wind turbines. The proposed method is model-free and has strong robustness, adaptability, and applicability. Particularly, distinct from the state-of-the-art data-driven wind farm control methods that commonly use the steady-state or time-averaged data (such as turbines' power outputs under steady wind conditions or from steady-state models) to carry out learning, the proposed method directly mines in-depth the time-series data measured at turbine rotors under time-varying wind conditions to achieve farm-level power maximization. The control scheme is built on a novel multiplayer deep reinforcement learning method (MPDRL), in which a special critic-actor-distractor structure, along with deep neural networks (DNNs), is designed to handle the stochastic feature of wind speeds and learn optimal control policies subject to a user-defined performance metric. The effectiveness, robustness, and scalability of the proposed MPDRL-based wind farm control method are tested by prototypical case studies with a dynamic wind farm simulator (WFSim). Compared with the commonly used greedy strategy, the proposed method leads to clear increases in farm-level power generation in case studies.