Articles | Volume 12, issue 6
https://doi.org/10.5194/amt-12-3463-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-3463-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jun 2019
Research article |
| 28 Jun 2019
| 28 Jun 2019
Automated wind turbine wake characterization in complex terrain
Sibley School of Mechanical and Aerospace Engineering, Cornell
University, Ithaca, New York, USA
Sara C. Pryor
Department of Earth and Atmospheric Sciences, Cornell University,
Ithaca, New York, USA
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Cited
21 citations as recorded by crossref.
- An experimental and analytical study of wind turbine wakes under pressure gradient A. Dar et al. 10.1063/5.0145043
- From fossil-dependent energy to a clean, non-polluting energy: Wind farms in Maluti-A-Phofung municipality, South Africa M. Mapuru et al. 10.1080/0376835X.2022.2051437
- Inter-comparison study of wind measurement between the three-lidar-based virtual tower and four lidars using VAD techniques X. Liu et al. 10.1080/10095020.2024.2307930
- Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains R. Barthelmie et al. 10.3390/en13164269
- Long-range Doppler lidar measurements of wind turbine wakes and their interaction with turbulent atmospheric boundary-layer flow at Perdigao 2017 N. Wildmann et al. 10.1088/1742-6596/1618/3/032034
- Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain A. Wise et al. 10.5194/wes-7-367-2022
- Effects of atmospheric stability on the performance of a wind turbine located behind a three-dimensional hill L. Liu & R. Stevens 10.1016/j.renene.2021.05.035
- Region-based convolutional neural network for wind turbine wake characterization from scanning lidars J. Aird et al. 10.1088/1742-6596/2265/3/032077
- Diagnosing systematic differences in predicted wind turbine array-array interactions S. Pryor et al. 10.1088/1742-6596/1618/6/062023
- Effects of the steepness on the evolution of turbine wakes above continuous hilly terrain H. Yang et al. 10.1049/rpg2.12420
- Deploying Taller Turbines in Complex Terrain: A Hill Flow Study (HilFlowS) Perspective S. Wharton & K. Foster 10.3390/en15072672
- Turbulence Measurements with Dual-Doppler Scanning Lidars A. Peña & J. Mann 10.3390/rs11202444
- Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain R. Robey & J. Lundquist 10.5194/wes-9-1905-2024
- WindsPT e-Science platform for wind measurement campaigns D. Gomes et al. 10.1088/1742-6596/2265/2/022081
- Effects of Two-Dimensional Steep Hills on the Performance of Wind Turbines and Wind Farms L. Liu & R. Stevens 10.1007/s10546-020-00522-z
- Impact of atmospheric turbulence on wind farms sited over complex terrain J. Singh & J. Alam 10.1063/5.0222245
- Microscale modelling of wind turbines in the New York offshore lease area R. Barthelmie et al. 10.1088/1742-6596/2265/2/022040
- Wind turbine wakes on escarpments: A wind-tunnel study A. Dar & F. Porté-Agel 10.1016/j.renene.2021.09.102
- Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain J. Aird et al. 10.3390/rs13214438
- Implications of complex terrain topography on the performance of a real wind farm F. Bernardoni et al. 10.1088/1742-6596/2505/1/012052
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. 10.1063/5.0141683
21 citations as recorded by crossref.
- An experimental and analytical study of wind turbine wakes under pressure gradient A. Dar et al. 10.1063/5.0145043
- From fossil-dependent energy to a clean, non-polluting energy: Wind farms in Maluti-A-Phofung municipality, South Africa M. Mapuru et al. 10.1080/0376835X.2022.2051437
- Inter-comparison study of wind measurement between the three-lidar-based virtual tower and four lidars using VAD techniques X. Liu et al. 10.1080/10095020.2024.2307930
- Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains R. Barthelmie et al. 10.3390/en13164269
- Long-range Doppler lidar measurements of wind turbine wakes and their interaction with turbulent atmospheric boundary-layer flow at Perdigao 2017 N. Wildmann et al. 10.1088/1742-6596/1618/3/032034
- Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain A. Wise et al. 10.5194/wes-7-367-2022
- Effects of atmospheric stability on the performance of a wind turbine located behind a three-dimensional hill L. Liu & R. Stevens 10.1016/j.renene.2021.05.035
- Region-based convolutional neural network for wind turbine wake characterization from scanning lidars J. Aird et al. 10.1088/1742-6596/2265/3/032077
- Diagnosing systematic differences in predicted wind turbine array-array interactions S. Pryor et al. 10.1088/1742-6596/1618/6/062023
- Effects of the steepness on the evolution of turbine wakes above continuous hilly terrain H. Yang et al. 10.1049/rpg2.12420
- Deploying Taller Turbines in Complex Terrain: A Hill Flow Study (HilFlowS) Perspective S. Wharton & K. Foster 10.3390/en15072672
- Turbulence Measurements with Dual-Doppler Scanning Lidars A. Peña & J. Mann 10.3390/rs11202444
- Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain R. Robey & J. Lundquist 10.5194/wes-9-1905-2024
- WindsPT e-Science platform for wind measurement campaigns D. Gomes et al. 10.1088/1742-6596/2265/2/022081
- Effects of Two-Dimensional Steep Hills on the Performance of Wind Turbines and Wind Farms L. Liu & R. Stevens 10.1007/s10546-020-00522-z
- Impact of atmospheric turbulence on wind farms sited over complex terrain J. Singh & J. Alam 10.1063/5.0222245
- Microscale modelling of wind turbines in the New York offshore lease area R. Barthelmie et al. 10.1088/1742-6596/2265/2/022040
- Wind turbine wakes on escarpments: A wind-tunnel study A. Dar & F. Porté-Agel 10.1016/j.renene.2021.09.102
- Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain J. Aird et al. 10.3390/rs13214438
- Implications of complex terrain topography on the performance of a real wind farm F. Bernardoni et al. 10.1088/1742-6596/2505/1/012052
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. 10.1063/5.0141683
Latest update: 22 Nov 2024
Short summary
Wakes are volumes of air with low wind speed that form downwind of wind turbines. Their properties and behaviour determine optimal turbine spacing in wind farms. We use scanning Doppler lidar to accurately and precisely measure wake characteristics at a complex terrain site in Portugal. We develop and apply an automatic processing algorithm to detect wakes and quantify their characteristics. In higher wind speeds, the wake centres are lower. Wake centres are also lower in convective conditions.
Wakes are volumes of air with low wind speed that form downwind of wind turbines. Their...
