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

Peng Guo and David Infield

Renewable Energy, 2021, vol. 179, issue C, 1098-1105

Abstract: 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.

Keywords: Wind turbine; Blade icing detection; XGBoost; Condition monitoring (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:179:y:2021:i:c:p:1098-1105

DOI: 10.1016/j.renene.2021.07.120

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