English(EN) Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations

人工智能利用强化学习加速风力发电场控制

作者 PulseAugur 编辑部 · [2 个来源] · 2026-04-28 04:00

研究人员开发了新的强化学习技术来提高风力发电场的控制效率。一种方法利用稳态模型的专家演示来加速训练并提高初始性能，显著缩短了昂贵的学习阶段。另一种方法采用多智能体强化学习来平衡发电量与涡轮机的结构载荷约束，使用代理模型来估计损伤等效载荷并相应地塑造奖励。 AI

影响这些方法可以通过优化涡轮机运行和减少结构应力，从而提高风能生产效率。

排序理由该集群包含两篇 arXiv 论文，详细介绍了风力发电场控制强化学习方面的新研究。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Marcus Binder Nilsen, Julian Quick, Tuhfe G\"o\c{c}men, Nikolay Dimitrov, Pierre-Elouan R\'ethor\'e · 2026-04-28 04:00

Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations

arXiv:2604.22794v1 Announce Type: cross Abstract: Reinforcement learning (RL) offers a promising approach for adaptive wind farm flow control, yet its practical deployment is hindered by slow training convergence and poor initial performance, factors that could translate to years…
arXiv cs.LG TIER_1 English(EN) · Teodor {\AA}strand, Marcus Binder Nilsen, Iasonas Tsaklis, Tuhfe G\"o\c{c}men, Pierre-Elouan R\'ethor\'e, Nikolay Dimitrov · 2026-04-28 04:00

Load constrained wind farm flow control through multi-objective multi-agent reinforcement learning

arXiv:2604.22795v1 Announce Type: cross Abstract: This study presents a multi-agent reinforcement learning (MARL) framework for load-constrained wind farm flow control (WFFC). While wake steering can enhance total wind farm power, it often introduces increased structural loads on…