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A review of integrating ice detection and mitigation for wind turbine blades

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  • Madi, Ezieddin
  • Pope, Kevin
  • Huang, Weimin
  • Iqbal, Tariq

Abstract

The capacity of installed wind power is growing rapidly in cold climate regions; however, turbine blades are susceptible to ice accumulation. The aerodynamic properties of turbine blades are highly sensitive to ice accretion, which can significantly impair aerodynamic performance. Ice accretion on the blades of a wind turbine can lead to turbine shutdown, power loss and damage to turbine components. To prevent ice formation on wind turbine blades, an ice sensor integrated with an ice mitigation system is required. The ice sensor can be used with a de-icer on the blade surface. However, the current ice sensing and de-icing technologies are inefficient and integrated systems need appreciable improvement. This paper reviews ice sensing and active mitigation techniques for a wind turbine blade surface, which are categorized based on several key parameters. Furthermore, this paper investigates the conceptual design of integrating ice sensing and mitigation systems. The advantages and disadvantages of the integrated systems are presented to provide valuable insights on ice prevention for wind turbines operating in ice prone locations.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:103:y:2019:i:c:p:269-281
    DOI: 10.1016/j.rser.2018.12.019
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    References listed on IDEAS

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    1. Wang, Yibing & Xu, Yuanming & Lei, Yuyong, 2018. "An effect assessment and prediction method of ultrasonic de-icing for composite wind turbine blades," Renewable Energy, Elsevier, vol. 118(C), pages 1015-1023.
    2. Fakorede, Oloufemi & Feger, Zoé & Ibrahim, Hussein & Ilinca, Adrian & Perron, Jean & Masson, Christian, 2016. "Ice protection systems for wind turbines in cold climate: characteristics, comparisons and analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 662-675.
    3. Habibi, Hossein & Cheng, Liang & Zheng, Haitao & Kappatos, Vassilios & Selcuk, Cem & Gan, Tat-Hean, 2015. "A dual de-icing system for wind turbine blades combining high-power ultrasonic guided waves and low-frequency forced vibrations," Renewable Energy, Elsevier, vol. 83(C), pages 859-870.
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    Cited by:

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    2. 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.
    3. Sun, Haoyang & Lin, Guiping & Jin, Haichuan & Guo, Jinghui & Ge, Kun & Wang, Jiaqi & He, Xi & Wen, Dongsheng, 2023. "2D Numerical investigation of surface wettability induced liquid water flow on the surface of the NACA0012 airfoil," Renewable Energy, Elsevier, vol. 205(C), pages 326-339.
    4. Chen, Wanqiu & Qiu, Yingning & Feng, Yanhui & Li, Ye & Kusiak, Andrew, 2021. "Diagnosis of wind turbine faults with transfer learning algorithms," Renewable Energy, Elsevier, vol. 163(C), pages 2053-2067.
    5. Stoyanov, D.B. & Nixon, J.D. & Sarlak, H., 2021. "Analysis of derating and anti-icing strategies for wind turbines in cold climates," Applied Energy, Elsevier, vol. 288(C).
    6. 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.
    7. Cheng Tao & Tao Tao & Xinjian Bai & Yongqian Liu, 2023. "Wind Turbine Blade Icing Prediction Using Focal Loss Function and CNN-Attention-GRU Algorithm," Energies, MDPI, vol. 16(15), pages 1-15, July.
    8. Olabi, A.G. & Abdelkareem, Mohammad Ali, 2022. "Renewable energy and climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    9. Wenjie Wang & Yu Xue & Chengkuan He & Yongnian Zhao, 2022. "Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades," Energies, MDPI, vol. 15(15), pages 1-31, August.
    10. Wei Li & Shinai Xu & Baiyun Qian & Xiaoxia Gao & Xiaoxun Zhu & Zeqi Shi & Wei Liu & Qiaoliang Hu, 2022. "Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review," Sustainability, MDPI, vol. 14(24), pages 1-29, December.
    11. Thanh-Cao Le & Tran-Huu-Tin Luu & Huu-Phuong Nguyen & Trung-Hau Nguyen & Duc-Duy Ho & Thanh-Canh Huynh, 2022. "Piezoelectric Impedance-Based Structural Health Monitoring of Wind Turbine Structures: Current Status and Future Perspectives," Energies, MDPI, vol. 15(15), pages 1-31, July.
    12. 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.
    13. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    14. Sun, Haoyang & Lin, Guiping & Jin, Haichuan & Bu, Xueqin & Cai, Chujiang & Jia, Qi & Ma, Kuiyuan & Wen, Dongsheng, 2021. "Experimental investigation of surface wettability induced anti-icing characteristics in an ice wind tunnel," Renewable Energy, Elsevier, vol. 179(C), pages 1179-1190.
    15. Valery Okulov & Ivan Kabardin & Dmitry Mukhin & Konstantin Stepanov & Nastasia Okulova, 2021. "Physical De-Icing Techniques for Wind Turbine Blades," Energies, MDPI, vol. 14(20), pages 1-16, October.

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