用多智能体强化学习实现风机阵列的功率与载荷协同控制。
Load constrained wind farm flow control through multi-objective multi-agent reinforcement learning
- 基于独立软演员-评论家算法，结合流场代理模型实时估算疲劳载荷。
- 在载荷增量不超过30%的约束下，实现功率提升且避免高载荷控制策略。
- 适合关注风电场智能控制与结构安全的工程师和研究人员。
本研究提出一种多智能体强化学习（MARL）框架，用于负载约束下的风力发电场流控（WFFC）。尽管尾流转向可提升整体发电量，但常导致下游风机结构载荷增加。为此，我们采用独立软演员-评论家（I-SAC）架构，并融合数据驱动的局部流入扇区平均代理模型，实现实时损伤等效载荷（DELs）估计。通过将这些估计值纳入奖励函数设计，训练各风机智能体在满足最大载荷增量阈值（Δ_max）为10%、20%和30%的前提下，最大化发电量。该框架在使用DYNAMIKS流场求解器与动态尾流脉动（DWM）模型的WindGym环境中实现，能够捕捉非稳态尾流物理特性。结果表明，MARL智能体成功学习到协作策略，在优先获取功率增益的同时，主动规避高载荷控制方案。
原文摘要 · Abstract (English)
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 downstream turbines. To address this, we integrate an Independent Soft Actor-Critic (I-SAC) architecture with a data-driven, local inflow sector-averaged surrogate model to provide real-time estimates of Damage Equivalent Loads (DELs). By incorporating these estimates into a shaped reward function, turbine-specific agents are trained to maximize power production while adhering to specific load-increase thresholds ($Δ_{max}$) of 10%, 20%, and 30% relative to a baseline controller. The framework is implemented within the WindGym environment using the DYNAMIKS flow solver with Dynamic Wake Meandering (DWM) model to capture non-stationary wake physics. Results indicate that the MARL agents successfully learn collaborative policies that prioritise power gain while actively retreating from high-DEL control strategies.
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