Data-Driven LIDAR Feedforward Predictive Wind Turbine Control

Light Detection and Ranging (LIDAR)-assisted Model Predictive Control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive Control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework.