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

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 where 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 MPC in a turbulent wind setting, the novel formulation is especially superior with low lidar quality, benefits more from the availability of a 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 cost - exhibit similar pitch behavior and tower oscillation.