Comparative analysis of control methods for a wind turbine in normal and gusty conditions

Abstract

This paper provides a thorough evaluation of well-known control algorithms, including proportional–integral (PI), model predictive control (MPC), and H-infinity ($H_{\infty }$) controllers, by implementing them in a full nonlinear wind turbine model under normal wind conditions in below and above-rated wind speeds. The simulation results show that all the controllers perform satisfactorily. This study extends MPC to include a feedforward (FF) loop (FF-MPC) that uses the wind speed information provided by a light detection and ranging (LiDAR) sensor, which measures the upcoming wind (in advance), to improve the overall control performance. The FF-MPC was tested under both normal and anomalous (i.e. gusty) wind conditions. The results were compared with those of the standard feedback MPC (FB-MPC). The results show that the incorporation of the FF loop into the standard FB controller can improve the control performance, which can result in improved reliability and lifespan of the turbine. Furthermore, MPC was augmented with an FF loop over PI and $H_{\infty }$ controllers owing to its versatility in handling constraints, nonlinearities, and multiple objectives, along with its inherent capability to incorporate preview wind data. All the controllers are tested using a high-fidelity aeroelastic model (i.e. Bladed by DNV). The use of a Bladed model is common in wind turbine controller design before the application to the real-life wind turbine, and Bladed also allows more realistic simulation when incorporating a LiDAR.