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Optical Methods for Measuring Icing of Wind Turbine Blades

Author

Listed:
  • Ivan Kabardin

    (Kutateladze Institute of Thermophysics SB RAS, 630090 Novosibirsk, Russia)

  • Sergey Dvoynishnikov

    (Kutateladze Institute of Thermophysics SB RAS, 630090 Novosibirsk, Russia)

  • Maxim Gordienko

    (Kutateladze Institute of Thermophysics SB RAS, 630090 Novosibirsk, Russia)

  • Sergey Kakaulin

    (Kutateladze Institute of Thermophysics SB RAS, 630090 Novosibirsk, Russia)

  • Vadim Ledovsky

    (Kutateladze Institute of Thermophysics SB RAS, 630090 Novosibirsk, Russia)

  • Grigoriy Gusev

    (Kutateladze Institute of Thermophysics SB RAS, 630090 Novosibirsk, Russia)

  • Vladislav Zuev

    (Kutateladze Institute of Thermophysics SB RAS, 630090 Novosibirsk, Russia)

  • Valery Okulov

    (Kutateladze Institute of Thermophysics SB RAS, 630090 Novosibirsk, Russia)

Abstract

The development of wind-power engineering in the Arctic has led to increasing wind turbines in cold climatic zones. A problem operating wind turbines in cold conditions is the icing of blades. The icing of the blades leads to a change in rotor aerodynamics, a decrease in energy production, the additional weight of blades, and load on the rotor, which increase wear and reduce the lifetime of the turbines. The growth of icing on the blades threatens the uncontrollable separation of ice pieces from the blade edges, and the operation is unsafe. Non-contact methods for detecting icing on the blades need to prevent critical operating modes with ice formation on the blades. This review analyzes methods for detecting icing. The advantages and disadvantages of various optical methods are presented to give valuable insights on ice prevention for wind turbines operating in cold regions.

Suggested Citation

  • Ivan Kabardin & Sergey Dvoynishnikov & Maxim Gordienko & Sergey Kakaulin & Vadim Ledovsky & Grigoriy Gusev & Vladislav Zuev & Valery Okulov, 2021. "Optical Methods for Measuring Icing of Wind Turbine Blades," Energies, MDPI, vol. 14(20), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6485-:d:653020
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    References listed on IDEAS

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    1. Sima Rastayesh & Lijia Long & John Dalsgaard Sørensen & Sebastian Thöns, 2019. "Risk Assessment and Value of Action Analysis for Icing Conditions of Wind Turbines Close to Highways," Energies, MDPI, vol. 12(14), pages 1-15, July.
    2. Yong Liu & Qiran Li & Masoud Farzaneh & B. X. Du, 2020. "Image Characteristic Extraction of Ice-Covered Outdoor Insulator for Monitoring Icing Degree," Energies, MDPI, vol. 13(20), pages 1-12, October.
    3. Lijun Zhang & Kai Liu & Yufeng Wang & Zachary Bosire Omariba, 2018. "Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier," Energies, MDPI, vol. 11(10), pages 1-15, September.
    4. Jingjing Wang & Junhua Wang & Jianwei Shao & Jiangui Li, 2017. "Image Recognition of Icing Thickness on Power Transmission Lines Based on a Least Squares Hough Transform," Energies, MDPI, vol. 10(4), pages 1-15, March.
    5. Madi, Ezieddin & Pope, Kevin & Huang, Weimin & Iqbal, Tariq, 2019. "A review of integrating ice detection and mitigation for wind turbine blades," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 269-281.
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    Cited by:

    1. Hongmei Cui & Zhongyang Li & Bingchuan Sun & Teng Fan & Yonghao Li & Lida Luo & Yong Zhang & Jian Wang, 2022. "A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network," Energies, MDPI, vol. 15(22), pages 1-18, November.

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