Koopman Model Predictive Control for Wind Farm Yield Optimization with Combined Thrust and Yaw Control

Abstract

Two novel approaches to data-driven wind farm control via Koopman model predictive control are presented, both combining thrust and yaw control for yield optimization and power reference tracking. The Koopman framework is used to build prediction models to predict wake effects of upwind on downwind turbines. This paper extends previous work by using yaw in addition to thrust control. The test case is a wind farm consisting of two turbines and wind with constant speed and direction parallel to the main axis of the farm. In closed-loop simulation, the two Koopman model predictive control designs reduce the tracking error considerably with regards to a previously published baseline controller, which used solely axial induction control. It is also demonstrated that this can be achieved with relatively small yaw angles, avoiding mechanical loads acting on turbines operating misaligned to the wind, making this a promising approach for further investigations in 3D medium and high fidelity simulation environments.