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

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  • 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
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    References listed on IDEAS

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