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.
Highlights
A wake is a complex dynamic structure forming behind a wind turbine due to the kinetic energy extraction from the incoming wind flow
The Adaptive Thresholding Segmentation method (ATS) method detects a continuous structure in the near wake, while the far wake is represented as series of small dis360 connected structures (Fig. 11c)
We proposed an automatic thresholding method for the wake detection based on the image processing method for the whitecaps detection on the ocean surface
Summary
A wake is a complex dynamic structure forming behind a wind turbine due to the kinetic energy extraction from the incoming wind flow. The wake region is characterized by the decreased wind speed and the increased turbulence intensity. The relative velocity deficit rapidly decreases to 20% at the downstream distance of five rotor diameters (5D). The recovery to the free flow is considerably slowed down; at the same time, the wake width increases up to 3D according to in situ 20 observations (Aitken et al (2014)). The typical turbine spacing in the wind farms is usually 8D, the optimal spacing is estimated to be higher in order to reduce the wake effect on downstream turbines (Meyers and Meneveau (2012); Stevens (2016)).
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