Turbulence intensity estimation from lidar data

2020 ◽  
Keyword(s):  
Turbulence Intensity ◽  
Lidar Data ◽  
Intensity Estimation

Analysis of wind shear and turbulence intensity influence on wind power performance using SCADA and LiDAR data

2017 ◽  
Author(s):  
Fernando Oliveira de Andrade ◽  
Alexandre Kolodynskie Guetter ◽  
Leandro Jose Lemes Stival
Keyword(s):  
Wind Power ◽  
Turbulence Intensity ◽  
Wind Shear ◽  
Lidar Data ◽  
Power Performance

scholarly journals LiSBOA: LiDAR Statistical Barnes Objective Analysis for optimal design of LiDAR scans and retrieval of wind statistics. Part II: Applications to synthetic and real LiDAR data of wind turbine wakes

2020 ◽  
Author(s):  
Stefano Letizia ◽  
Lu Zhan ◽  
Giacomo Valerio Iungo
Keyword(s):  
Optimal Design ◽  
Velocity Field ◽  
Turbulence Intensity ◽  
High Speed ◽  
Cartesian Grid ◽  
Objective Analysis ◽  
Turbulent Shear ◽  
Lidar Data ◽  
Mean Velocity ◽  
First Case

Abstract. The LiDAR Statistical Barnes Objective Analysis (LiSBOA), presented in Letizia et al., is a procedure for the optimal design of LiDAR scans and calculation over a Cartesian grid of the statistical moments of the velocity field. The LiSBOA is applied to LiDAR data collected in the wake of wind turbines to reconstruct mean and turbulence intensity of the wind velocity field. The proposed procedure is firstly tested for a numerical dataset obtained by means of the virtual LiDAR technique applied to the data obtained from a large eddy simulation (LES). The optimal sampling parameters for a scanning Doppler pulsed wind LiDAR are retrieved from the LiSBOA, then the estimated statistics are calculated showing a maximum error of about 4 % for both the normalized mean velocity and the turbulence intensity. Subsequently, LiDAR data collected during a field campaign conducted at a wind farm in complex terrain are analyzed through the LiSBOA for two different configurations. In the first case, the wake velocity fields of four utility-scale turbines are reconstructed on a 3D grid, showing the capability of the LiSBOA to capture complex flow features, such as high-speed jet around the nacelle and the wake turbulent shear layers. For the second case, the statistics of the wakes generated by four interacting turbines are calculated over a 2D Cartesian grid and compared to the measurements provided by the nacelle-mounted anemometers. Maximum discrepancies as low as 3 % for the normalized mean velocity and turbulence intensity endorse the application of the LiSBOA for LiDAR-based wind resource assessment and diagnostic surveys for wind farms.

Turbulence intensity estimation using advanced radar methods

2015 ◽  
Author(s):  
Felix J. Yanovsky ◽  
A.C.P. Oude Nijhuis ◽  
O.A. Krasnov ◽  
C.M.H. Unal ◽  
H.W.J. Russchenberg ◽  
...  
Keyword(s):  
Turbulence Intensity ◽  
Intensity Estimation

Atmospheric Turbulence Intensity Estimation Based on Deep Convolutional Neural Networks

2021 ◽  
Vol 48 (4) ◽  
pp. 0401018
Author(s):  
马圣杰 Ma Shengjie ◽  
郝士琦 Hao Shiqi ◽  
赵青松 Zhao Qingsong ◽  
王勇 Wang Yong ◽  
王磊 Wang Lei
Keyword(s):  
Neural Networks ◽  
Atmospheric Turbulence ◽  
Convolutional Neural Networks ◽  
Turbulence Intensity ◽  
Deep Convolutional Neural Networks ◽  
Intensity Estimation

Determining Highly Erodible Land Utilizing LIDAR Data

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
McDermott McDermott ◽  
Michael Michael ◽  
Megan Baker
Keyword(s):  
Lidar Data
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