3 papers
eess.SY2026
Load constrained wind farm flow control through multi-objective multi-agent reinforcement learning
Teodor à strand, Marcus Binder Nilsen, Iasonas Tsaklis +3
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,…
eess.SY2026
Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms
Marcus Binder Nilsen, Teodor Olof Benedict à strand, Tuhfe Göçmen +1
Wind farm wake steering optimization is challenging due to complex flow physics and changing conditions. This paper presents a hierarchical framework that combines reinforcement le…
eess.SY2026
Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations
Marcus Binder Nilsen, Julian Quick, Tuhfe Göçmen +2
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…