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Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms

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  • Göçmen, Tuhfe
  • Giebel, Gregor

Abstract

Turbulence characteristics of the wind farm inflow have a significant impact on the energy production and the lifetime of a wind farm. The common approach is to use the meteorological mast measurements to estimate the turbulence intensity (TI) but they are not always available and the turbulence varies over the extent of the wind farm. This paper describes a method to estimate the TI at individual turbine locations by using the rotor effective wind speed calculated via high frequency turbine data.

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  • Göçmen, Tuhfe & Giebel, Gregor, 2016. "Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms," Renewable Energy, Elsevier, vol. 99(C), pages 524-532.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:524-532
    DOI: 10.1016/j.renene.2016.07.038
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    References listed on IDEAS

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    1. Göçmen, Tuhfe & Laan, Paul van der & Réthoré, Pierre-Elouan & Diaz, Alfredo Peña & Larsen, Gunner Chr. & Ott, Søren, 2016. "Wind turbine wake models developed at the technical university of Denmark: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 752-769.
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    Cited by:

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    2. Nezhad, M. Majidi & Neshat, M. & Heydari, A. & Razmjoo, A. & Piras, G. & Garcia, D. Astiaso, 2021. "A new methodology for offshore wind speed assessment integrating Sentinel-1, ERA-Interim and in-situ measurement," Renewable Energy, Elsevier, vol. 172(C), pages 1301-1313.
    3. Tian, Linlin & Song, Yilei & Wang, Zhenming & Zhao, Ning & Zhu, Chunling & Lu, Xiyun, 2024. "Predictive capability of an improved AD/RANS method for multiple wind turbines and wind farm wakes," Energy, Elsevier, vol. 297(C).
    4. Yuewei Liu & Shenghui Zhang & Xuejun Chen & Jianzhou Wang, 2018. "Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting," Sustainability, MDPI, vol. 10(12), pages 1-30, December.
    5. Besseau, Romain & Sacchi, Romain & Blanc, Isabelle & Pérez-López, Paula, 2019. "Past, present and future environmental footprint of the Danish wind turbine fleet with LCA_WIND_DK, an online interactive platform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 274-288.
    6. Asadi, Meysam & Pourhossein, Kazem, 2021. "Wind farm site selection considering turbulence intensity," Energy, Elsevier, vol. 236(C).
    7. Christos Galinos & Jonas Kazda & Wai Hou Lio & Gregor Giebel, 2020. "T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case," Energies, MDPI, vol. 13(6), pages 1-16, March.
    8. Li, Li & Wang, Bing & Ge, Mingwei & Huang, Zhi & Li, Xintao & Liu, Yongqian, 2023. "A novel superposition method for streamwise turbulence intensity of wind-turbine wakes," Energy, Elsevier, vol. 276(C).
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    10. Yazhou Wang & Xin Cai & Shifa Lin & Bofeng Xu & Yuan Zhang & Saixian Bian, 2022. "Study of Tower Clearance Safety Protection during Extreme Gust Based on Wind Turbine Monitoring Data," Energies, MDPI, vol. 15(12), pages 1-11, June.
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