Preprints
https://doi.org/10.5194/wes-2021-90
https://doi.org/10.5194/wes-2021-90

  06 Sep 2021

06 Sep 2021

Review status: this preprint is currently under review for the journal WES.

Development of an image processing method for wake meandering studies and its application on data sets from scanning wind lidar and large-eddy simulation

Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, and Finn Gunnar Nielsen Maria Krutova et al.
  • Geophysical institute and Bergen Offshore Wind Centre, University of Bergen, Allégaten 70, 5007 Bergen, Norway

Abstract. Wake meandering studies require knowledge of the instantaneous wake shape and its evolution. Scanning lidar data are used to identify the wake pattern behind offshore wind turbines but do not immediately reveal the wake shape. The precise detection of the wake shape and centerline helps to build models predicting wake behavior. The conventional Gaussian fit methods are reliable in the near-wake area but lose precision with the distance from the rotor and require good data resolution for an accurate fit. The thresholding methods usually imply a fixed value or manual selection of a threshold, which hinders the wake detection on a large data set. We propose an automatic thresholding method for the wake shape and centerline detection, which is less dependent on the data resolution and can also be applied to the image data.

We show that the method performs reasonably well on large-eddy simulation data and apply it to the data set containing lidar measurements of the two wakes. Along with the wake detection method, we use image processing statistics, such as entropy analysis, to filter and classify lidar scans. The image processing method is developed to reduce dependency on the supplementary reference data such as wind speed and direction. We show that the centerline found with the image processing is in a good agreement with the manually detected centerline and the Gaussian fit method. We also discuss a potential application of the method to separate the near and far wakes and to estimate the wake direction.

Maria Krutova et al.

Status: open (until 22 Oct 2021)

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Maria Krutova et al.

Video supplement

Automatic thresholding method for the wake detection Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, Finn Gunnar Nielsen https://doi.org/10.5446/54055

Maria Krutova et al.

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Short summary
We described a new automated method to separate the wind turbine wake from the undisturbed flow. The method relies on the wind speed distribution in the measured wind field to select one specific threshold value and split the measurements into wake and background points. The purpose of the method is to reduce the amount of data required – the proposed algorithm does not need precise information on the wind speed or direction and can run on the image instead of the measured data.