IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i24p8246-d697327.html
   My bibliography  Save this article

Simulation and Experimental Study on the Ultrasonic Micro-Vibration De-Icing Method for Wind Turbine Blades

Author

Listed:
  • Yan Li

    (Engineering College, Northeast Agricultural University, Harbin 150030, China)

  • He Shen

    (Engineering College, Northeast Agricultural University, Harbin 150030, China)

  • Wenfeng Guo

    (Engineering College, Northeast Agricultural University, Harbin 150030, China)

Abstract

In cold and humid regions, ice accretion sometimes develops on the blades of wind turbines. Blade icing reduces the power generation of the wind turbine and affects the safe operation of the wind farm. For this paper, ultrasonic micro-vibration was researched as an effective de-icing method to remove ice from the wind turbine blade surface and improve the efficiency of wind turbine power generation. A blade segment with NACA0018 airfoil and the hollow structure at the leading edge was designed. The modal analysis of the blade was simulated by ANSYS, and the de-icing vibration mode was selected. Based on the simulation results, the blade segment sample with PZT patches was machined, and its natural frequencies were measured with an impedance analyzer. A return-flow icing wind tunnel system, and a device used to measure the adhesive strength of ice covering the airfoil blade, were designed and manufactured. The experiments on the adhesive strength of the ice were carried out under the excitation of the ultrasonic vibration. The experimental results show that the adhesive strength of the ice, which was generated under the dynamic flow field condition, was lower than the ice generated by water under the static flow field condition. Under the excitation of the ultrasonic vibration, the adhesive strength of the ice decreased. When the excitation frequency was 21.228 kHz, the adhesive strength was the lowest, which was 0.084 MPa. These research findings lay the theoretical and experimental foundations for researching in-depth the application of the ultrasonic de-icing technology to wind turbines.

Suggested Citation

  • Yan Li & He Shen & Wenfeng Guo, 2021. "Simulation and Experimental Study on the Ultrasonic Micro-Vibration De-Icing Method for Wind Turbine Blades," Energies, MDPI, vol. 14(24), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8246-:d:697327
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/24/8246/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/24/8246/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. del Río, Pablo & Calvo Silvosa, Anxo & Iglesias Gómez, Guillermo, 2011. "Policies and design elements for the repowering of wind farms: A qualitative analysis of different options," Energy Policy, Elsevier, vol. 39(4), pages 1897-1908, April.
    2. Guo, Wenfeng & Shen, He & Li, Yan & Feng, Fang & Tagawa, Kotaro, 2021. "Wind tunnel tests of the rime icing characteristics of a straight-bladed vertical axis wind turbine," Renewable Energy, Elsevier, vol. 179(C), pages 116-132.
    3. 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.
    4. 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.
    5. Dalili, N. & Edrisy, A. & Carriveau, R., 2009. "A review of surface engineering issues critical to wind turbine performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 428-438, February.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    3. 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.
    4. Cheng, Xu & Shi, Fan & Liu, Yongping & Liu, Xiufeng & Huang, Lizhen, 2022. "Wind turbine blade icing detection: a federated learning approach," Energy, Elsevier, vol. 254(PC).
    5. 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.
    6. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
    7. Tang, Baoping & Liu, Wenyi & Song, Tao, 2010. "Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution," Renewable Energy, Elsevier, vol. 35(12), pages 2862-2866.
    8. Zhongqiu Mu & Guoqiang Tong & Zhenjun Xiao & Qingyue Deng & Fang Feng & Yan Li & Garrel Van Arne, 2022. "Study on Aerodynamic Characteristics of a Savonius Wind Turbine with a Modified Blade," Energies, MDPI, vol. 15(18), pages 1-13, September.
    9. Moura Carneiro, F.O. & Barbosa Rocha, H.H. & Costa Rocha, P.A., 2013. "Investigation of possible societal risk associated with wind power generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 30-36.
    10. 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.
    11. Juliana Subtil Lacerda & Jeroen C. J. M. Van den Bergh, 2014. "International Diffusion of Renewable Energy Innovations: Lessons from the Lead Markets for Wind Power in China, Germany and USA," Energies, MDPI, vol. 7(12), pages 1-28, December.
    12. Xu, Zhi & Zhang, Ting & Li, Xiaojuan & Li, Yan, 2023. "Effects of ambient temperature and wind speed on icing characteristics and anti-icing energy demand of a blade airfoil for wind turbine," Renewable Energy, Elsevier, vol. 217(C).
    13. Liu, Wenyi, 2016. "Design and kinetic analysis of wind turbine blade-hub-tower coupled system," Renewable Energy, Elsevier, vol. 94(C), pages 547-557.
    14. 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).
    15. 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.
    16. Michael Howlett & Ishani Mukherjee & Jeremy Rayner, 2014. "The Elements of Effective Program Design: A Two-Level Analysis," Politics and Governance, Cogitatio Press, vol. 2(2), pages 1-12.
    17. Francisco Haces-Fernandez, 2021. "Higher Wind: Highlighted Expansion Opportunities to Repower Wind Energy," Energies, MDPI, vol. 14(22), pages 1-19, November.
    18. Son, Chankyu & Kelly, Mark & Kim, Taeseong, 2021. "Boundary-layer transition model for icing simulations of rotating wind turbine blades," Renewable Energy, Elsevier, vol. 167(C), pages 172-183.
    19. 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.
    20. Enevoldsen, Peter, 2016. "Onshore wind energy in Northern European forests: Reviewing the risks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1251-1262.

    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:gam:jeners:v:14:y:2021:i:24:p:8246-:d:697327. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.