A Distributed Reinforcement Learning Yaw Control Approach for Wind Farm Energy Capture Maximization
In this paper, we present a reinforcement-learning-based distributed approach to wind farm energy capture maximization using yaw-based wake steering. In order to maximize the power output of a wind farm, individual turbines can use yaw misalignment to deflect their wakes away from downstream turbines. Although using model-based methods to achieve yaw misalignment is one option, a model-free method might be better suited to incorporate changing conditions and uncertainty. We propose an algorithm that adapts concepts of temporal difference reinforcement learning distributed to a multiagent environment that empowers individual turbines to optimize overall wind farm output and react to unforeseen disturbances.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind Energy Technologies Office (EE-4W)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1669574
- Report Number(s):
- NREL/CP-5000-77744; MainId:30659; UUID:3c9caa66-690e-454f-bae1-ff68e76e7c98; MainAdminID:17378
- Country of Publication:
- United States
- Language:
- English
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