English(EN) Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions

离线强化学习算法在风洞测试中将风力发电场功率提升10%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了一种新颖的离线强化学习算法MTD3-BC，通过控制偏航系统来优化风力发电场的发电量。该算法通过风洞实验进行了验证，与贪婪策略相比，能有效减少尾流效应，并将发电场整体功率提高约10%。MTD3-BC无需尾流模型或与模拟器进行大量交互即可实现此目标，显著降低了计算成本和训练时间。 AI

影响这项研究展示了一种使用AI优化可再生能源发电的计算效率更高的方法。

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arXiv cs.LG TIER_1 English(EN) · Yuhan Su, Hongyang Dong, Simone Tamaro, Filippo Campagnolo, Carlo L. Bottasso, Xiaowei Zhao · 2026-09-14 04:00

面向风电场控制的离线强化学习：动态风向下的风洞研究

arXiv:2609.12905v1 Announce Type: new Abstract: 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 i…