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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2018-461
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2018-461
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Jan 2019

Research article | 10 Jan 2019

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.

Automated Wind Turbine Wake Characterization in Complex Terrain

Rebecca J. Barthelmie1 and Sara C. Pryor2 Rebecca J. Barthelmie and Sara C. Pryor
  • 1Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York
  • 2Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York

Abstract. An automated wind turbine wake characterization algorithm has been developed and applied to a dataset of over 19,000 scans measured by scanning Doppler lidar at Perdigão over the period January to June 2017. The algorithm correctly identifies the wake centre position in 62 % of possible wake cases, 46 % having a clear and well-defined wake centre while 16 % have split centres or multiple lobes. Only 5 % of cases are not detected, the remaining 33 % could not be categorized either by the algorithm or subjectively, mainly due to the complexity of the background flow. Average wake centre heights categorized by inflow wind speeds are shown to be initially lofted (to 2 rotor diameters, D) except when the inflow wind speeds exceed 12 ms−1. Even under low wind speeds, by 3.5 D downstream of the wind turbine, the mean wake centre position is below the initial wind turbine hub-height and descends broadly following the terrain slope. However, this behaviour is strongly linked to hour of the day and atmospheric stability. Overnight and in stable conditions the average height of the wake centre is 10 m higher than in unstable conditions at 2 D and 17 m higher at 4.5 D downstream of the wind turbine.

Rebecca J. Barthelmie and Sara C. Pryor
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Rebecca J. Barthelmie and Sara C. Pryor
Data sets

Scanning lidar Cornell Perdigao R. J. Barthelmie and S. C. Pryor https://doi.org/10.26023/74K3-7KYB-3G03

Vertical lidar Cornell Perdigao R. J. Barthelmie and S. C. Pryor https://doi.org/10.5065/D6K35SHC

Rebecca J. Barthelmie and Sara C. Pryor
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Latest update: 16 Jun 2019
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Short summary
Wakes are low wind speed volumes of air downwind of wind turbines. Wake properties/behaviour determine optimal turbine spacing in wind farms. The objective is to develop an automated system to measure and detect wind turbine wakes. The paper presents measurements and analyses of six months of measurements with a scanning Doppler lidar at a complex terrain site in Portugal. Wakes are successfully detected and characterized and their behavior linked to wind speed and other atmospheric conditions.
Wakes are low wind speed volumes of air downwind of wind turbines. Wake properties/behaviour...
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