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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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Preprints
https://doi.org/10.5194/wes-2020-50
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2020-50
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Mar 2020

17 Mar 2020

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A revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

Parameterization of Wind Evolution using Lidar

Yiyin Chen1, David Schlipf2, and Po Wen Cheng1 Yiyin Chen et al.
  • 1Stuttgart Wind Energy (SWE) at Institute of Aircraft Design, University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
  • 2Wind Energy Technology Institute, Flensburg University of Applied Sciences, Kanzleistraße 91–93, 24943 Flensburg, Germany

Abstract. Wind evolution refers to the change of the turbulence structure of the eddies over time while the eddies are advected by the main flow over space. With the development of the lidar-assisted wind turbine control, modelling of the wind evolution becomes an interesting topic, because the control system should only react to the changes in the wind field which can be predicted accurately over the distance to avoid harmful and unnecessary control action.

This paper aims to achieve a parameterization model for the wind evolution model to predict the wind evolution model parameters according to the wind field conditions. For this purpose, a two-parameter wind evolution model suggested in literature was applied to model the wind evolution and the wind evolution was estimated using lidar data. A statistical analysis was done to reveal the characteristics of wind evolution model parameters. Gaussian process regression was applied to achieve the parameterization model. The results have proven the applicability of Gaussian process regression model to predict the wind evolution model parameters with sufficient accuracy.

Yiyin Chen et al.

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Yiyin Chen et al.

Yiyin Chen et al.

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Latest update: 02 Dec 2020
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Short summary
This paper suggests a concept to develop a prediction model for wind evolution according to the wind field conditions using lidar measurement. The motivations for that are to facilitate the development of lidar-assisted wind turbine control and to provide some insights into the characteristics of wind evolution. The results have proven that the wind evolution model parameters can be predicted by a Gaussian process regression model with sufficient accuracy despite the lidar data is noisy.
This paper suggests a concept to develop a prediction model for wind evolution according to the...
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