IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v103y2019icp269-281.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032118308141
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2018.12.019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Guo, Peng & Infield, David, 2021. "Wind turbine blade icing detection with multi-model collaborative monitoring method," Renewable Energy, Elsevier, vol. 179(C), pages 1098-1105.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Wang, Qiang & Yi, Xian & Liu, Yu & Ren, Jinghao & Yang, Jianjun & Chen, Ningli, 2024. "Numerical investigation of dynamic icing of wind turbine blades under wind shear conditions," Renewable Energy, Elsevier, vol. 227(C).
    8. 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).
    9. 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.
    10. 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.
    11. 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).
    12. 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.
    13. Olabi, A.G. & Abdelkareem, Mohammad Ali, 2022. "Renewable energy and climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    14. 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.
    15. 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.
    16. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dong, Xinghui & Gao, Di & Li, Jia & Jincao, Zhang & Zheng, Kai, 2020. "Blades icing identification model of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 162(C), pages 575-586.
    2. Yang Zhao & Xi Wang & Qibin Zhou & Zhenxing Wang & Xiaoyan Bian, 2020. "Numerical Study of Lightning Protection of Wind Turbine Blade with De-Icing Electrical Heating System," Energies, MDPI, vol. 13(3), pages 1-11, February.
    3. Wang, Yibing & Xu, Yuanming & Su, Fei, 2020. "Damage accumulation model of ice detach behavior in ultrasonic de-icing technology," Renewable Energy, Elsevier, vol. 153(C), pages 1396-1405.
    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. Albara M. Mustafa & Abbas Barabadi, 2022. "Criteria-Based Fuzzy Logic Risk Analysis of Wind Farms Operation in Cold Climate Regions," Energies, MDPI, vol. 15(4), pages 1-17, February.
    6. Liu, Wenyi, 2016. "Design and kinetic analysis of wind turbine blade-hub-tower coupled system," Renewable Energy, Elsevier, vol. 94(C), pages 547-557.
    7. 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).
    8. 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.
    9. Swenson, Lauren & Gao, Linyue & Hong, Jiarong & Shen, Lian, 2022. "An efficacious model for predicting icing-induced energy loss for wind turbines," Applied Energy, Elsevier, vol. 305(C).
    10. Anbarsooz, M. & Amiri, M. & Rashidi, I., 2019. "A novel curtain design to enhance the aerodynamic performance of Invelox: A steady-RANS numerical simulation," Energy, Elsevier, vol. 168(C), pages 207-221.
    11. Ma, Liqun & Zhang, Zichen & Gao, Linyue & Liu, Yang & Hu, Hui, 2020. "An exploratory study on using Slippery-Liquid-Infused-Porous-Surface (SLIPS) for wind turbine icing mitigation," Renewable Energy, Elsevier, vol. 162(C), pages 2344-2360.
    12. Jiawei Jiang & Yizhou Shen & Yangjiangshan Xu & Zhen Wang & Jie Tao & Senyun Liu & Weilan Liu & Haifeng Chen, 2024. "An energy-free strategy to elevate anti-icing performance of superhydrophobic materials through interfacial airflow manipulation," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    13. Dimitris Al. Katsaprakakis & Nikos Papadakis & Ioannis Ntintakis, 2021. "A Comprehensive Analysis of Wind Turbine Blade Damage," Energies, MDPI, vol. 14(18), pages 1-31, September.
    14. Ruqaya Khammas & Heli Koivuluoto, 2022. "Durable Icephobic Slippery Liquid-Infused Porous Surfaces (SLIPS) Using Flame- and Cold-Spraying," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    15. Wang, Yibing & Xu, Yuanming & Huang, Qi, 2017. "Progress on ultrasonic guided waves de-icing techniques in improving aviation energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 638-645.
    16. Sudhakar Gantasala & Jean-Claude Luneno & Jan-Olov Aidanpää, 2017. "Investigating How an Artificial Neural Network Model Can Be Used to Detect Added Mass on a Non-Rotating Beam Using Its Natural Frequencies: A Possible Application for Wind Turbine Blade Ice Detection," Energies, MDPI, vol. 10(2), pages 1-21, February.
    17. Fahed Martini & Leidy Tatiana Contreras Montoya & Adrian Ilinca, 2021. "Review of Wind Turbine Icing Modelling Approaches," Energies, MDPI, vol. 14(16), pages 1-26, August.
    18. Zeng, Jing & Song, Bingliang, 2017. "Research on experiment and numerical simulation of ultrasonic de-icing for wind turbine blades," Renewable Energy, Elsevier, vol. 113(C), pages 706-712.
    19. 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.
    20. Chen, Bin & Yu, Songhao & Yu, Yang & Zhou, Yilin, 2020. "Acoustical damage detection of wind turbine blade using the improved incremental support vector data description," Renewable Energy, Elsevier, vol. 156(C), pages 548-557.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:103:y:2019:i:c:p:269-281. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.