Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares - Jaime Liew; Tuhfe Göçmen; Wai Hou Lio; Gunner Chr. Larsen (Wind Energy, 2024)
Abstract Wind farms experience significant power losses due to wake interactions between turbines. Research shows that wake steering can alleviate these losses by redirecting the flow through the farm. However, dynamic closed‐loop implementations of wake steering are rarely presented. We present a model‐free closed‐loop control method using reinforcement learning methodology known as policy gradients in combination with recursive least squares to perform real‐time wake steering in a wind farm. We present dynamic simulations of a four‐turbine wind farm row using HAWC2Farm, implementing the reinforcement learning control method for various inflow conditions and controller configurations. By controlling the three most upstream turbines, mean power gains of 11.6±3.0% and 1.4±0.5% (95% confidence interval) are observed in partial wake and full wake conditions respectively at 7.5% turbulence intensity. The study helps to bridge the gap between theoretical wind farm control and real‐world wind farm systems.