Self-Learning Data-Driven Wind Farm Control Strategy Using Field Measurements

This paper presents a novel data-driven wind farm control strategy to steer the wake. The approach uses the turbine power output and standard turbine measurement equipment as input, such as the nacelle anemometer and the wind vane. By designing the control strategy based on the measured data, sub-optimal yaw angle estimates from an analytical model can be compensated and inaccuracies induced by the turbine sensors can be corrected. Thereby advancing wake steering controllers to be independent of external sensors, such as a met mast or lidars. Measurements acquired from a wake steering field campaign are used to train the data-driven model and to evaluate the predictions of the model. The proposed data-driven approach highlights a consistent increase in the power gain compared to an analytical approach with a potential improvement ranging from 3.4 % up to 8.0 %.