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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.

Suggested Citation

  • 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:

    1. 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.
    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. 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).
    4. 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.
    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. Chang Cai & Jicai Guo & Xiaowen Song & Yanfeng Zhang & Jianxin Wu & Shufeng Tang & Yan Jia & Zhitai Xing & Qing’an Li, 2023. "Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    7. 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.
    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. 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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