IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v259y2026ics0960148125027454.html

A method using SCADA data for diagnosing wind turbine’s anemometer systematic errors

Author

Listed:
  • Chen, Danyang
  • Li, Qian
  • Lin, Hangbing
  • Cheng, Shilin
  • Yang, Xiaolei

Abstract

The failure of anemometers to accurately measure wind speed significantly impacts wind turbine conversion efficiency as well as the prediction of annual energy production and generation efficiency. Current methods, however, fail to adequately account for local environmental factors and wind turbine operational conditions. This paper presents a novel diagnostic method for identifying anemometer systematic errors in wind turbines solely based on the SCADA data, incorporating both environmental factors (particularly, the inflow turbulence intensity) and turbine operational conditions. The proposed diagnostic framework is tested using three wind turbines in a wind farm located in complex terrain, demonstrating high effectiveness in detecting systematic errors. The results show good agreement with wind tunnel experiments, with mean square errors (MSEs, m2s−2) of 0.0012 and 0.0062, and correlation coefficients (C.C.) of 0.974 and 0.717, respectively. Moreover, the inclusion of turbulence intensity in the model significantly improves diagnostic performance across different wind speeds. Additionally, analyzing error variation with wind speed shows promising potential for identifying the causes of anemometer errors.

Suggested Citation

  • Chen, Danyang & Li, Qian & Lin, Hangbing & Cheng, Shilin & Yang, Xiaolei, 2026. "A method using SCADA data for diagnosing wind turbine’s anemometer systematic errors," Renewable Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:renene:v:259:y:2026:i:c:s0960148125027454
    DOI: 10.1016/j.renene.2025.125081
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125027454
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.125081?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:259:y:2026:i:c:s0960148125027454. 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.