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A field study of ice accretion and its effects on the power production of utility-scale wind turbines

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  • Gao, Linyue
  • Tao, Tao
  • Liu, Yongqian
  • Hu, Hui

Abstract

A field study was conducted to examine ice accretion on 50-m-long turbine blades and icing-induced power production losses to multi-megawatt wind turbines. An unmanned-aerial-system equipped with a digital camera was deployed to take images of the ice structures on turbine blades after undergoing a 30-hour-long icing event to quantify the ice thickness accreted along blade leading edges. While ice accreted over entire blade spans, more ice was found to accrete on outboard blades with the ice thickness reaching up to 0.3 m near blade tips. Based on the icing similarity concept and blade element momentum theory, a theoretical analysis was performed to predict the ice thickness distributions on turbine blades. The theoretical predictions were found to agree with the field measurements well in general. Turbine operation status during the icing event was monitored by using turbine supervisory control and data acquisition (SCADA) systems. Despite the high wind, iced wind turbines were found to rotate much slower and even shut down frequently during the icing event, with the icing-induced power loss being up to 80%. The present study aims to fill in the knowledge gaps between the fundamental icing physics studies conducted under idealized lab conditions and complex wind turbine icing phenomena under realistic, natural icing conditions.

Suggested Citation

  • Gao, Linyue & Tao, Tao & Liu, Yongqian & Hu, Hui, 2021. "A field study of ice accretion and its effects on the power production of utility-scale wind turbines," Renewable Energy, Elsevier, vol. 167(C), pages 917-928.
    • Handle: RePEc:eee:renene:v:167:y:2021:i:c:p:917-928
    DOI: 10.1016/j.renene.2020.12.014
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    2. Tao, Tao & Liu, Yongqian & Qiao, Yanhui & Gao, Linyue & Lu, Jiaoyang & Zhang, Ce & Wang, Yu, 2021. "Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 1004-1013.
    3. Sergio Campobasso, M. & Castorrini, Alessio & Ortolani, Andrea & Minisci, Edmondo, 2023. "Probabilistic analysis of wind turbine performance degradation due to blade erosion accounting for uncertainty of damage geometry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    4. Bai, Xinjian & Tao, Tao & Gao, Linyue & Tao, Cheng & Liu, Yongqian, 2023. "Wind turbine blade icing diagnosis using RFECV-TSVM pseudo-sample processing," Renewable Energy, Elsevier, vol. 211(C), pages 412-419.
    5. Kashif Sohail & Hooman Farzaneh, 2022. "Model for Optimal Power Coefficient Tracking and Loss Reduction of the Wind Turbine Systems," Energies, MDPI, vol. 15(11), pages 1-19, June.
    6. Lin, Jianing & Bao, Minglei & Liang, Ziyang & Sang, Maosheng & Ding, Yi, 2022. "Spatio-temporal evaluation of electricity price risk considering multiple uncertainties under extreme cold weather," Applied Energy, Elsevier, vol. 328(C).
    7. Hacıefendioğlu, Kemal & Başağa, Hasan Basri & Yavuz, Zafer & Karimi, Mohammad Tordi, 2022. "Intelligent ice detection on wind turbine blades using semantic segmentation and class activation map approaches based on deep learning method," Renewable Energy, Elsevier, vol. 182(C), pages 1-16.
    8. Ziemowit Malecha, 2022. "Risks for a Successful Transition to a Net-Zero Emissions Energy System," Energies, MDPI, vol. 15(11), pages 1-4, June.

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