07 Jul 2022
07 Jul 2022
Status: this preprint is currently under review for the journal WES.

Evaluation of lidar-assisted wind turbine control under various turbulence characteristics

Feng Guo1, David Schlipf1, and Po Wen Cheng2 Feng Guo et al.
  • 1Wind Energy Technology Institute, Flensburg University of Applied Sciences, Kanzleistraße 91-93, 24943 Flensburg, Germany
  • 2Stuttgart Wind Energy (SWE), Institute of Aircraft Design, University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany

Abstract. Lidar systems installed on the nacelle of wind turbines have the capability to provide a preview of incoming turbulent wind. Lidar-assisted wind turbine control (LAC) allows the turbine controller to react to changes in the wind before they affect the wind turbine. Currently, the most proven LAC technique is the collective pitch feed-forward control, which has been found to be beneficial for load reduction. In literature, the benefits were mainly investigated using standard turbulence parameters suggested by the IEC 61400-1 standard and assuming Taylor's frozen hypothesis (the turbulence measured by the lidar propagates unchanged to the rotor). In reality, the turbulence spectrum and the spatial coherence change by the atmospheric stability conditions. Also, Taylor's frozen hypothesis does not take into account the coherence decay of turbulence in the longitudinal direction. In this work, we consider three atmospheric stability classes: unstable, neutral, and stable, and generate four-dimensional stochastic turbulence fields based on two models: the Mann model and the Kaimal model. The generated four-dimensional stochastic turbulence fields include the longitudinal coherence thus avoiding assuming Taylor's frozen hypothesis. The Reference Open Source Controller (ROSCO) is used as the baseline feedback-only controller. A reference lidar-assisted controller (LACer) is developed and used to evaluate the benefit of LAC. Considering the NREL 5.0 MW reference wind turbine and a typical four-beam pulsed lidar system, it is found that the filter design of the LACer is not sensitive to the turbulence characteristics representative of the investigated atmospheric stability classes. The benefits of LAC are analyzed using the aeroelastic tool OpenFAST. According to the simulations, LAC's benefits are mainly the reductions in rotor speed variation (15 %  to 40 %), tower fore-aft bending moment (2 % to 18.8 %), and power variation (3 % to 20 %). This work reveals that the benefits of LAC can depend on the turbulence models, the turbulence characteristics, and the mean wind speed.

Feng Guo et al.

Status: open (until 21 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2022-62', Anonymous Referee #1, 11 Aug 2022 reply

Feng Guo et al.

Feng Guo et al.


Total article views: 230 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
114 113 3 230 1 1
  • HTML: 114
  • PDF: 113
  • XML: 3
  • Total: 230
  • BibTeX: 1
  • EndNote: 1
Views and downloads (calculated since 07 Jul 2022)
Cumulative views and downloads (calculated since 07 Jul 2022)

Viewed (geographical distribution)

Total article views: 195 (including HTML, PDF, and XML) Thereof 195 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 11 Aug 2022
Short summary
The benefits of lidar-assisted control are evaluated using both the Mann model and Kaimal model-based 4D turbulence. The simulations are performed for the mean wind speed level from 14 ms−1 to 24 ms−1, using the NREL 5.0 MW reference wind turbine and a four-beam lidar system. Using lidar-assisted control reduces the variations in rotor speed, pitch rate, and electrical power significantly.