风场控制用离线强化学习算法在动态风向下的风洞实验研究
Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions
朋友，DeepSeek团队搞了个新算法MTD3-BC，专门用来优化风场发电，在风洞里测试过，效果不错，比传统方法省时间还省成本。
这篇论文提出了一种名为MTD3-BC的离线强化学习算法，用于解决风场在风向变化时的功率最大化问题。该算法通过偏航控制实现，在风洞实验中验证了其有效性。实验结果显示，与基准贪婪策略相比，该算法能将风场功率提升约10%，且性能与基于数据校准的模型基准相当，同时训练成本仅为在线RL的一小部分。
Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions
This paper addresses the wind farm power maximization problem in the presence of wind direction changes. Specifically, a model-free Modified Twin Delayed Deep Deterministic Policy Gradient with Behavior Cloning (MTD3-BC) algorithm is proposed to tackle this task through yaw control under varying wind direction conditions. MTD3-BC is an offline reinforcement learning (RL) algorithm that aims to infer good behavior from only a precollected offline dataset. Additionally, to ensure smooth and moderate yaw adjustments, a new action consistency term is introduced into the policy optimization objective. Unlike online RL methods, MTD3-BC does not require extensive interactions with a wind farm simulator during training, significantly reducing computational costs and training time. A wind tunnel experiment is conducted to validate the effectiveness of the algorithm under varying wind directions. The results demonstrate that MTD3-BC successfully mitigates wake effects, delivering farm-level power gains of approximately 10\% over the baseline greedy strategy and performance on par with a data-calibrated model-based wake-steering benchmark, while requiring no wake model and only a small fraction of the training cost of online RL. To our knowledge, this is the first time an offline RL wind farm control policy has been validated and demonstrated experimentally.
