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

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  • Guo, Peng
  • Infield, David

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.

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  • Guo, Peng & Infield, David, 2021. "Wind turbine blade icing detection with multi-model collaborative monitoring method," Renewable Energy, Elsevier, vol. 179(C), pages 1098-1105.
    • Handle: RePEc:eee:renene:v:179:y:2021:i:c:p:1098-1105
    DOI: 10.1016/j.renene.2021.07.120
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    References listed on IDEAS

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    1. Owusu, Kwadwo Poku & Kuhn, David C.S. & Bibeau, Eric L., 2013. "Capacitive probe for ice detection and accretion rate measurement: Proof of concept," Renewable Energy, Elsevier, vol. 50(C), pages 196-205.
    2. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    3. Madi, Ezieddin & Pope, Kevin & Huang, Weimin & Iqbal, Tariq, 2019. "A review of integrating ice detection and mitigation for wind turbine blades," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 269-281.
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    Cited by:

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    2. Liming Gou & Jian Zhang & Lihao Wen & Yu Fan, 2024. "State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China," Sustainability, MDPI, vol. 16(10), pages 1-19, May.
    3. Ye, Feng & Ezzat, Ahmed Aziz, 2024. "Icing detection and prediction for wind turbines using multivariate sensor data and machine learning," Renewable Energy, Elsevier, vol. 231(C).
    4. Usama Aziz & Sylvie Charbonnier & Christophe Berenguer & Alexis Lebranchu & Frederic Prevost, 2022. "A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods," Energies, MDPI, vol. 15(8), pages 1-21, April.
    5. Tao, Cheng & Tao, Tao & He, Shukai & Bai, Xinjian & Liu, Yongqian, 2024. "Wind turbine blade icing diagnosis using B-SMOTE-Bi-GRU and RFE combined with icing mechanism," Renewable Energy, Elsevier, vol. 221(C).
    6. Shi, Han & Song, Mengjie & Narita, Fumio & Hosseini, Seyyed Hossein & Zhang, Long & Chao, Christopher Yu Hang, 2025. "Experimental study on high-precision detection technology for the freezing front height in brine on a horizontal cold plate surface in cold regions," Renewable Energy, Elsevier, vol. 246(C).
    7. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    8. Adaiton Oliveira-Filho & Ryad Zemouri & Philippe Cambron & Antoine Tahan, 2023. "Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model," Energies, MDPI, vol. 16(12), pages 1-21, June.
    9. Zhang, Lidong & Zhao, Yuze & Guo, Yunfeng & Hu, Tianyu & Xu, Xiandong & Zhang, Duanmei & Song, Changpeng & Guo, Yuanjun & Ma, Yuanchi, 2024. "Research on wind turbine icing prediction data processing and accuracy of machine learning algorithm," Renewable Energy, Elsevier, vol. 237(PB).
    10. Liu, Zhiyuan & Li, Yan & Sun, Yong & Feng, Fang & Tagawa, Kotaro, 2023. "Preparation of biochar-based photothermal superhydrophobic coating based on corn straw biogas residue and blade anti-icing performance by wind tunnel test," Renewable Energy, Elsevier, vol. 210(C), pages 618-626.
    11. Mu, Zhongqiu & Guo, Wenfeng & Li, Yan & Tagawa, Kotaro, 2023. "Wind tunnel test of ice accretion on blade airfoil for wind turbine under offshore atmospheric condition," Renewable Energy, Elsevier, vol. 209(C), pages 42-52.
    12. Hongmei Cui & Zhongyang Li & Bingchuan Sun & Teng Fan & Yonghao Li & Lida Luo & Yong Zhang & Jian Wang, 2022. "A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network," Energies, MDPI, vol. 15(22), pages 1-18, November.

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