Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind [electronic resource]

• Ngôn ngữ: eng
• Ký hiệu phân loại: 333.8 Subsurface resources
• Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2021
• Mô tả vật lý: Size: Article No. 043305 : , digital, PDF file.
• Bộ sưu tập: Metadata
• ID: 257086
• Chủ đề: 1. 17 wind energy; 2. Reinforcement learning; 3. Wind energy; 4. Wind plant controls; 5. Wind turbine controls

Tóm tắt

Here, we present a proof-of-concept distributed reinforcement learning framework for wind farm energy capture maximization. The algorithm we propose uses Q-Learning in a wake-delayed wind farm environment and considers time-varying, though not yet fully turbulent, wind inflow conditions. These algorithm modifications are used to create the Gradient Approximation with Reinforcement Learning and Incremental Comparison (GARLIC) framework for optimizing wind farm energy capture in time-varying conditions, which is then compared to the FLOw Redirection and Induction in Steady State (FLORIS) static lookup table wind farm controller baseline.