Lidar-assisted model predictive control of wind turbine fatigue via online rainflow counting considering stress history

Abstract

Abstract. The formulation of parametric online rainflow counting implements the standard fatigue estimation process and a stress history in the cost function of a model predictive controller. The formulation is tested in realistic simulation scenarios in which the states are estimated by a moving horizon estimator and the wind is predicted by a lidar simulator. The tuning procedure for the controller toolchain is carefully explained. In comparison to a conventional model predictive controller (MPC) in a turbulent wind setting, the novel formulation is especially superior with low lidar quality, benefits more from the availability of wind prediction, and exhibits a more robust performance with shorter prediction horizons. A simulation excerpt with the novel formulation provides deeper insight into the update of the stress history and the fatigue cost parameters. Finally, in a deterministic gust setting, both the conventional and the novel MPC – despite their completely different fatigue costs – exhibit similar pitch behavior and tower oscillations.