IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v97y2016icp481-491.html
   My bibliography  Save this article

Load sharing behavior analysis method of wind turbine gearbox in consideration of multiple-errors

Author

Listed:
  • Mo, Shuai
  • Zhang, Yidu
  • Wu, Qiong
  • Matsumura, Shigeki
  • Houjoh, Haruo

Abstract

Conducting a further analysis on loading sharing among compound planetary gear system in wind turbine gearbox, and making a meshing error analysis on the eccentricity error, gear thickness error, base pitch error, assembly error, and bearing error of wind turbine gearbox respectively. In view of the floating meshing error resulting from meshing clearance variation caused by the simultaneous floating of all gears, this paper establishes a refined mathematical model of two-stage power split loading sharing coefficient calculation in consideration of multiple errors. Also obtains the regular curves of the load sharing coefficient and floating orbits of center gears, and conducts a load sharing coefficient test experiment of compound planetary gear system to verify the research results, which can provide scientific theory evidence for proper tolerance distribution and control in design and process.

Suggested Citation

  • Mo, Shuai & Zhang, Yidu & Wu, Qiong & Matsumura, Shigeki & Houjoh, Haruo, 2016. "Load sharing behavior analysis method of wind turbine gearbox in consideration of multiple-errors," Renewable Energy, Elsevier, vol. 97(C), pages 481-491.
    • Handle: RePEc:eee:renene:v:97:y:2016:i:c:p:481-491
    DOI: 10.1016/j.renene.2016.05.058
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116304682
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2016.05.058?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. Yunxuan Dong & Jianzhou Wang & Chen Wang & Zhenhai Guo, 2017. "Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting," Energies, MDPI, vol. 10(4), pages 1-27, April.
    2. He, Guolin & Ding, Kang & Wu, Xiaomeng & Yang, Xiaoqing, 2019. "Dynamics modeling and vibration modulation signal analysis of wind turbine planetary gearbox with a floating sun gear," Renewable Energy, Elsevier, vol. 139(C), pages 718-729.

    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:97:y:2016:i:c:p:481-491. 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.