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.
Highlights
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 highest profit gain is achieved by Parametric Online Rainflow Counting (PORFC)-2R at maximum prediction horizon, which surpasses conventional controller (CC) by 30% and the best Tower Tip Velocity Penalization (TTVP) by 2.5%
400 For all Model Predictive Controllers (MPC), the profit benefit with respect to CC via fatigue reduction comes at the price of a higher pitch travel
Summary
Fatigue is damage of a material caused by cyclic application of mechanical stress. For wind turbines, fatigue has a large impact on lifetime e.g. of tower, blades and drivetrain, and is a main design driver. Model Predictive Controllers (MPC) enable optimal control of turbines by utilizing predictions of the incoming wind by a light detection and ranging (lidar) device (Bottasso et al, 2014; Schlipf et al, 2013). Based on these input predictions, stress time series at crucial spots in the turbine structure can be 15 predicted. In Loew et al (2020a), a MPC formulation was presented that allows for the externalization of the RFC evaluation from the MPC algorithm, and the inclusion of its results into the MPC via time-varying parameters This formulation is referred to as Parametric Online Rainflow Counting (PORFC). PORFC allows for 20 the direct incorporation of monetary fatigue in the cost function of MPC, and for a true economic balancing with revenue from generated electricity
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