IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v146y2020icp2170-2187.html
   My bibliography  Save this article

An efficient rotational sampling method of wind fields for wind turbine blade fatigue analysis

Author

Listed:
  • Chen, Jianbing
  • Song, Yupeng
  • Peng, Yongbo
  • Nielsen, Søren R.K.
  • Zhang, Zili

Abstract

Both the operational and ultimate load conditions should be considered in the structural design and reliability assessment of wind turbine systems. In the operational condition, the fatigue load experienced by wind turbine blades is of great concern in design which highly relies upon the rotor’s rotation. Three kinds of methods have been developed to explore the rotational sampling effect of wind speeds on wind turbine blades, which, however, are somewhat inconvenient in practical applications. In view of the recent developments in wind field simulation, a novel rotational sampling method allowing for the analytical expression of fluctuating wind speeds on rotating blades is proposed in the present paper. In contrast to the existing methods, the proposed method circumvents the decomposition of cross power spectrum density (PSD) matrix and the interpolation in spatial and temporal dimensions. In particular, a closed-form expression of the rotational sampling spectrum is provided, thereby the mechanism of transfer of turbulent kinetic energy in frequency domain is quantitatively revealed. For illustrative purposes, fatigue analysis of the blades of a 5-MW offshore wind turbine is carried out, demonstrating the non-negligible influence of the rotational sampling on the fatigue load of blades and the competitive efficiency of the proposed method.

Suggested Citation

  • Chen, Jianbing & Song, Yupeng & Peng, Yongbo & Nielsen, Søren R.K. & Zhang, Zili, 2020. "An efficient rotational sampling method of wind fields for wind turbine blade fatigue analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2170-2187.
  • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:2170-2187
    DOI: 10.1016/j.renene.2019.08.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119312029
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2019.08.015?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.

    Citations

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


    Cited by:

    1. Song, Yupeng & Sun, Tao & Zhang, Zili, 2023. "Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions," Renewable Energy, Elsevier, vol. 212(C), pages 570-588.
    2. Song, Yupeng & Basu, Biswajit & Zhang, Zili & Sørensen, John Dalsgaard & Li, Jie & Chen, Jianbing, 2021. "Dynamic reliability analysis of a floating offshore wind turbine under wind-wave joint excitations via probability density evolution method," Renewable Energy, Elsevier, vol. 168(C), pages 991-1014.
    3. Ren, Xiaojun & Wu, Yongtang & Hao, Dongmin & Liu, Guoxu & Zafetti, Nicholas, 2021. "Analysis of the performance of the multi-objective hybrid hydropower-photovoltaic-wind system to reduce variance and maximum power generation by developed owl search algorithm," Energy, Elsevier, vol. 231(C).
    4. Kaewniam, Panida & Cao, Maosen & Alkayem, Nizar Faisal & Li, Dayang & Manoach, Emil, 2022. "Recent advances in damage detection of wind turbine blades: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

    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:renene:v:146:y:2020:i:c:p:2170-2187. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.journals.elsevier.com/renewable-energy .

    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.