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LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models

Author

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  • Hegazy, Amr
  • Blondel, Frédéric
  • Cathelain, Marie
  • Aubrun, Sandrine

Abstract

This study is a follow up on a previous one carried out within the frame of the French project SMARTEOLE, during which, a ground-based scanning LiDAR measurement campaign was conducted in the onshore wind farm of Sole du Moulin Vieux. That previous study focused on the wakes of two wind turbines that experienced different degrees of interaction depending on the incoming wind direction, through the processing of LiDAR measurements. The measurement duration (7 months) ensured the statistical convergence of the ensemble-averaged flow fields obtained after holding a categorisation process based on the wind speed at hub height, wind direction, and atmospheric stability, where only near-neutral stability conditions were considered. The present study focuses on integrating the operational data of the wind turbines through SCADA processing to complement the LiDAR wake field observations and to be used as an input for analytical wake models. First, the correlation between the atmospheric stability, deduced from MERRA-2 dataset, and the free-stream turbulence intensity, measured by the wind turbines’ anemometers, is studied for different wind speed ranges. It is observed that the turbulence intensity tends towards a consistent value as the atmospheric stability approaches near-neutral stability conditions, giving confidence into the applied strategy of data categorisation based on MERRA-2 outputs. The influence of the degree of wake interaction on the wake added turbulence, the velocity and power deficits between both turbines is assessed. Clear trends between the wake added turbulence and both the velocity and power deficits are detected. Consequently, two fitting laws are proposed. Then, different analytical wake models and wake superposition methods are fed with the operational data deduced from the processed SCADA data, and are used for predicting the evolution of the velocity deficit within the wake. Some statistical metrics are used for error quantification of the different engineering wake models compared to the scanning LiDAR measurements, used as reference, and Blondel and Cathelain produces the closest results to the field measurements.

Suggested Citation

  • Hegazy, Amr & Blondel, Frédéric & Cathelain, Marie & Aubrun, Sandrine, 2022. "LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models," Renewable Energy, Elsevier, vol. 181(C), pages 457-471.
    • Handle: RePEc:eee:renene:v:181:y:2022:i:c:p:457-471
    DOI: 10.1016/j.renene.2021.09.019
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    References listed on IDEAS

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    2. Xu, Zongyuan & Gao, Xiaoxia & Zhang, Huanqiang & Lv, Tao & Han, Zhonghe & Zhu, Xiaoxun & Wang, Yu, 2023. "Analysis of the anisotropy aerodynamic characteristics of downstream wind turbine considering the 3D wake expansion based on coupling method," Energy, Elsevier, vol. 263(PD).
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    5. Hongtao Niu & Congxin Yang & Yin Wang, 2023. "Experimental Study on the Influence of Incoming Flow on Wind Turbine Power and Wake Based on Wavelet Analysis," Energies, MDPI, vol. 16(16), pages 1-15, August.
    6. Wen, Jiahao & Zhou, Lei & Zhang, Hongfu, 2023. "Mode interpretation of blade number effects on wake dynamics of small-scale horizontal axis wind turbine," Energy, Elsevier, vol. 263(PA).
    7. Kuichao Ma & Huanqiang Zhang & Xiaoxia Gao & Xiaodong Wang & Heng Nian & Wei Fan, 2024. "Research on Evaluation Method of Wind Farm Wake Energy Efficiency Loss Based on SCADA Data Analysis," Sustainability, MDPI, vol. 16(5), pages 1-16, February.

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