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Remote Sens. 2016, 8(11), 939;

Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements

Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA
SgurrEnergy Ltd., Vancouver, BC V6C 2X6, Canada
Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA
National Renewable Energy Laboratory, Golden, CO 80401, USA
Author to whom correspondence should be addressed.
Academic Editors: Charlotte Bay Hasager, Alfredo Peña, Xiaofeng Li and Prasad S. Thenkabail
Received: 27 September 2016 / Revised: 27 October 2016 / Accepted: 7 November 2016 / Published: 10 November 2016
(This article belongs to the Special Issue Remote Sensing of Wind Energy)
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Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. We find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution. View Full-Text
Keywords: wind; energy; turbine; wakes; LiDAR wind; energy; turbine; wakes; LiDAR

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Doubrawa, P.; Barthelmie, R.J.; Wang, H.; Pryor, S.C.; Churchfield, M.J. Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements. Remote Sens. 2016, 8, 939.

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