Preprints
https://doi.org/10.5194/wes-2021-119
https://doi.org/10.5194/wes-2021-119
 
25 Oct 2021
25 Oct 2021
Status: this preprint is currently under review for the journal WES.

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

Stefan Loew and Carlo L. Bottasso Stefan Loew and Carlo L. Bottasso
  • Wind Energy Institute, Technical University of Munich, 85748 Garching b. München, Germany

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.

Stefan Loew and Carlo L. Bottasso

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Stefan Loew and Carlo L. Bottasso

Stefan Loew and Carlo L. Bottasso

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Short summary
This publication presents methods to improve the awareness and control of material fatigue for wind turbines. This is achieved by enhancing a sophisticated control algorithm, which utilizes wind prediction information from a laser measurement device. The simulation results indicate that the novel algorithm significantly improves the economic performance of a wind turbine. This benefit is particularly high for situations where the prediction quality is low or the prediction time frame is short.