Turbulent kinetic energy estimates from profiling wind LiDAR measurements and their potential for wind energy applications

被引:54
|
作者
Kumer, Valerie-M. [4 ]
Reuder, Joachim [4 ]
Dorninger, Manfred [1 ]
Zauner, Rudolf [3 ]
Grubsic, Vanda [1 ,2 ]
机构
[1] Univ Vienna, Dept Meteorol & Geophys, A-1010 Vienna, Austria
[2] Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA
[3] VERBUND Hydro Power GmbH, Vienna, Austria
[4] Univ Bergen, Inst Geophys, Bjerknes Ctr Climate Res, N-5020 Bergen, Norway
关键词
LiDAR; Turbulence; Wind turbine wakes; COMPUTATIONAL FLUID-DYNAMICS; TURBINE WAKES; TUNNEL; FLOW;
D O I
10.1016/j.renene.2016.07.014
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study shows that turbulent kinetic energy (TKE) estimates, derived from static LiDARs in Doppler Beam Swing (DBS) mode, permit a qualitative and quantitative characterization and analysis of turbulent structures as wind turbine wakes, and convective or shear generated eddies in the lower atmospheric boundary layer. The analysed data, collected by a WINDCUBE (TM) v1 in a wind park in Austria, is compared to WINDCUBE (TM) v1 and sonic data from the WINd Turbine Wake EXperiment Wieringermeer (WINT-WEX-W). Although turbulence measurements with a WINDCLJBE (TM) v1 are limited to a specific length scale, processed measurements above this threshold are in a good agreement with sonic anemometer data. In contrast to the commonly used turbulence intensity, the calculation of TKE not only provides an appropriate measure of turbulence intensities but also gives an insight into its origin. The processed data show typical wake characteristics, as flow decelerations, turbulence enhancement and wake rotation. By comparing these turbulence characteristics to other turbulent structures in the atmospheric boundary layer, we found that convection driven eddies in the surface layer have similar turbulence characteristics as turbine wakes, which makes convective weather situations relevant for wind turbine fatigue considerations. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:898 / 910
页数:13
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