Load constrained wind farm flow control through multi-objective multi-agent reinforcement learning
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 ba...
| Authors | Teodor Åstrand, Marcus Binder Nilsen, Iasonas Tsaklis et al. |
|---|---|
| Primary category | eess.SY |
| Published | 13 April 2026 |
| Last updated at source | 6 May 2026 |
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