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

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  • Kumer, Valerie-M.
  • Reuder, Joachim
  • Dorninger, Manfred
  • Zauner, Rudolf
  • Grubišić, Vanda

Abstract

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™ v1 in a wind park in Austria, is compared to WINDCUBE™ v1 and sonic data from the WINd Turbine Wake EXperiment Wieringermeer (WINTWEX-W). Although turbulence measurements with a WINDCUBE™ 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.

Suggested Citation

  • Kumer, Valerie-M. & Reuder, Joachim & Dorninger, Manfred & Zauner, Rudolf & Grubišić, Vanda, 2016. "Turbulent kinetic energy estimates from profiling wind LiDAR measurements and their potential for wind energy applications," Renewable Energy, Elsevier, vol. 99(C), pages 898-910.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:898-910
    DOI: 10.1016/j.renene.2016.07.014
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    References listed on IDEAS

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    1. Leonardo P. Chamorro & Fernando Porté-Agel, 2011. "Turbulent Flow Inside and Above a Wind Farm: A Wind-Tunnel Study," Energies, MDPI, vol. 4(11), pages 1-21, November.
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    7. Li, Jiale & Yu, Xiong (Bill), 2018. "Onshore and offshore wind energy potential assessment near Lake Erie shoreline: A spatial and temporal analysis," Energy, Elsevier, vol. 147(C), pages 1092-1107.
    8. Fei Zhao & Yihan Gao & Tengyuan Wang & Jinsha Yuan & Xiaoxia Gao, 2020. "Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    9. Marinić-Kragić, Ivo & Vučina, Damir & Milas, Zoran, 2018. "Numerical workflow for 3D shape optimization and synthesis of vertical-axis wind turbines for specified operating regimes," Renewable Energy, Elsevier, vol. 115(C), pages 113-127.
    10. Gao, Xiaoxia & Zhang, Shaohai & Li, Luqing & Xu, Shinai & Chen, Yao & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu & Lu, Hao, 2022. "Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test," Energy, Elsevier, vol. 254(PA).

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