WIND TURBINE GUST CONTROL USING LIDAR-ASSISTED MODEL PREDICTIVE CONTROL

Light Detection and Ranging (LIDAR)-based wind measurement system, positioned forward-facing, can gather information about the approaching wind. It proactively enables the wind turbine to adjust its operation via the feedforward (FF) loop. LIDAR technology can enhance wind turbine performance throughout its entire operational range. It can assist in torque control when wind speeds are below the rated level and in pitch control when wind speeds exceed the rated level. In this study, Model Predictive Control (MPC) is utilized. Within the field of wind turbine research, MPC has garnered significant interest in recent years due to its capability to handle both input and output constraints and leverage advanced on disturbances caused by the incoming wind, measured by the LIDAR. In this study, an FF-MPC is designed and compared with a more standard feedback (FB) MPC. A comparison is conducted in realistic gust wind conditions, considering below and above-rated wind ranges. These comparisons are performed in a realistic, high-fidelity aeroelastic simulation environment, i.e., DNV BLADED. Both controllers are designed for the DNV BLADED Supergen 5 MW wind turbine model. The control algorithm is implemented in C++, compiled into a dynamic link library (DLL), and integrated as an external controller within the DNV BLADED to enable accurate, high-fidelity simulations. Simulation results are presented to demonstrate the superiority of FF-MPC over the standard FB-MPC.