Enhancing Wind Turbine Sustainability Through LiDAR Configuration Analysis and Evaluation of Two Reference LiDAR-Assisted Control Strategies

Cedric D. Steinmann Perez, Alan W. H. Lio () and Fanzhong Meng
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Cedric D. Steinmann Perez: Department of Wind and Energy Systems, Technical University of Denmark, DK-4000 Roskilde, Denmark
Alan W. H. Lio: Department of Wind and Energy Systems, Technical University of Denmark, DK-4000 Roskilde, Denmark
Fanzhong Meng: Department of Wind and Energy Systems, Technical University of Denmark, DK-4000 Roskilde, Denmark

Sustainability, 2025, vol. 17, issue 13, 1-16

Abstract: LiDAR-assisted wind turbine control holds strong potential for reducing structural loads and improving rotor speed regulation, thereby contributing to more sustainable wind energy generation. However, key research gaps remain: (i) the practical limitations of commercially available fixed beam LiDARs for large turbines, and (ii) the performance assessment of commonly used LiDAR assisted feedforward control methods. This study addresses these gaps by (i) analysing how the coherence of LiDAR estimated rotor effective wind speed is influenced by the number of beams, measurement locations, and turbulence box resolution, and (ii) comparing two established control strategies. Numerical simulations show that applying a low cut-off frequency in the low-pass filter can impair preview time compensation. This is particularly critical for large turbines, where reduced coherence due to fewer beams undermines the effectiveness of LiDAR assisted control compared to the smaller turbines. The subsequent evaluation of control strategies shows that the Schlipf method offers greater robustness and consistent load reduction, regardless of the feedback control design. In contrast, the Bossanyi method, which uses the current blade pitch measurements, performs well when paired with carefully tuned baseline controllers. However, using the actual pitch angle in the feedforward pitch rate calculation can lead to increased excitation at certain frequencies, particularly if the feedback controller is not well tuned to avoid dynamics in those ranges.

Keywords: LiDAR; control; wind turbines; feedforward control; control; wind energy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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