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Model of the Correlation between Lidar Systems and Wind Turbines for Lidar-Assisted Control

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  • 1 Stuttgart Wind Energy, Universität Stuttgart, Stuttgart, Germany
  • | 2 DTU Wind Energy, Technical University of Denmark, Risø Campus, Denmark
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Abstract

Investigations of lidar-assisted control to optimize the energy yield and to reduce loads of wind turbines have increased significantly in recent years. For this kind of control, it is crucial to know the correlation between the rotor effective wind speed and the wind preview provided by a nacelle- or spinner-based lidar system. If on the one hand, the assumed correlation is overestimated, then the uncorrelated frequencies of the preview will cause unnecessary control action, inducing undesired loads. On the other hand, the benefits of the lidar-assisted controller will not be fully exhausted, if correlated frequencies are filtered out. To avoid these miscalculations, this work presents a method to model the correlation between lidar systems and wind turbines using Kaimal wind spectra. The derived model accounts for different measurement configurations and spatial averaging of the lidar system, different rotor sizes, and wind evolution. The method is compared to real measurement data with promising results. In addition, examples depict how this model can be used to design an optimal controller and how the configuration of a lidar system is optimized for a given turbine to improve the correlation.

Corresponding author address: David Schlipf, SWE, Universität Stuttgart, Allmandring 5B, 70569 Stuttgart, Germany. E-mail: schlipf@ifb.uni-stuttgart.de

This article is included in the ISARS 2012 special collection.

Abstract

Investigations of lidar-assisted control to optimize the energy yield and to reduce loads of wind turbines have increased significantly in recent years. For this kind of control, it is crucial to know the correlation between the rotor effective wind speed and the wind preview provided by a nacelle- or spinner-based lidar system. If on the one hand, the assumed correlation is overestimated, then the uncorrelated frequencies of the preview will cause unnecessary control action, inducing undesired loads. On the other hand, the benefits of the lidar-assisted controller will not be fully exhausted, if correlated frequencies are filtered out. To avoid these miscalculations, this work presents a method to model the correlation between lidar systems and wind turbines using Kaimal wind spectra. The derived model accounts for different measurement configurations and spatial averaging of the lidar system, different rotor sizes, and wind evolution. The method is compared to real measurement data with promising results. In addition, examples depict how this model can be used to design an optimal controller and how the configuration of a lidar system is optimized for a given turbine to improve the correlation.

Corresponding author address: David Schlipf, SWE, Universität Stuttgart, Allmandring 5B, 70569 Stuttgart, Germany. E-mail: schlipf@ifb.uni-stuttgart.de

This article is included in the ISARS 2012 special collection.

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