Wind turbine blade icing detection with multi-model collaborative monitoring method

Peng Guo, David Infield

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
21 Downloads (Pure)


Blade ice accretion endangers the safety of wind turbines located at high altitudes with a humid climate, particularly during winter. Timely detection of ice accretion facilitates appropriate regulation of the wind turbine, including shut down, to ensure safety. This paper provides a detailed analysis of the impact of ice accretion on wind turbine performance and relevant operational parameters. Rotor speed, output power and ambient temperature are selected as variables that can facilitate the detection of blade ice accretion. The XGBoost method is used to accurately construct normal behavior models for output power and rotor speed respectively, and the model errors (Mean Absolute Percentage Error, MAPE) can be as low as 0.53%. A Sequential Probability Ratio Test (SPRT) is introduced to analyze the model prediction residuals and thus identify any abnormal changes to output power and rotor speed. If significant changes are detected when the ambient temperature is below zero, an ice accretion alarm is triggered. Using real blade ice accretion data, a case study demonstrates that the proposed blade ice detection method can give blace icing alarm 5 h in advance and offers sufficient time to gurantte the safety of wind turbine.

Original languageEnglish
Pages (from-to)1098-1105
Number of pages8
JournalRenewable Energy
Early online date29 Jul 2021
Publication statusE-pub ahead of print - 29 Jul 2021


  • wind turbine
  • condition monitoring
  • XGBoost
  • blade icing detection


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