IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v179y2021icp1098-1105.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148121011253
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2021.07.120?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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. 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).
    7. 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.
    8. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bai, Xinjian & Tao, Tao & Gao, Linyue & Tao, Cheng & Liu, Yongqian, 2023. "Wind turbine blade icing diagnosis using RFECV-TSVM pseudo-sample processing," Renewable Energy, Elsevier, vol. 211(C), pages 412-419.
    2. Cheng Tao & Tao Tao & Xinjian Bai & Yongqian Liu, 2023. "Wind Turbine Blade Icing Prediction Using Focal Loss Function and CNN-Attention-GRU Algorithm," Energies, MDPI, vol. 16(15), pages 1-15, July.
    3. Chen, Wanqiu & Qiu, Yingning & Feng, Yanhui & Li, Ye & Kusiak, Andrew, 2021. "Diagnosis of wind turbine faults with transfer learning algorithms," Renewable Energy, Elsevier, vol. 163(C), pages 2053-2067.
    4. García Márquez, Fausto Pedro & Peco Chacón, Ana María, 2020. "A review of non-destructive testing on wind turbines blades," Renewable Energy, Elsevier, vol. 161(C), pages 998-1010.
    5. 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).
    6. Dong, Xinghui & Gao, Di & Li, Jia & Jincao, Zhang & Zheng, Kai, 2020. "Blades icing identification model of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 162(C), pages 575-586.
    7. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    8. Thanh-Cao Le & Tran-Huu-Tin Luu & Huu-Phuong Nguyen & Trung-Hau Nguyen & Duc-Duy Ho & Thanh-Canh Huynh, 2022. "Piezoelectric Impedance-Based Structural Health Monitoring of Wind Turbine Structures: Current Status and Future Perspectives," Energies, MDPI, vol. 15(15), pages 1-31, July.
    9. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    10. Olabi, A.G. & Abdelkareem, Mohammad Ali, 2022. "Renewable energy and climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    11. Yang, Xiyun & Zhang, Yanfeng & Lv, Wei & Wang, Dong, 2021. "Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier," Renewable Energy, Elsevier, vol. 163(C), pages 386-397.
    12. Wei Li & Shinai Xu & Baiyun Qian & Xiaoxia Gao & Xiaoxun Zhu & Zeqi Shi & Wei Liu & Qiaoliang Hu, 2022. "Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review," Sustainability, MDPI, vol. 14(24), pages 1-29, December.
    13. Arcos Jiménez, Alfredo & Zhang, Long & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2020. "Maintenance management based on Machine Learning and nonlinear features in wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 316-328.
    14. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    15. Wenjie Wang & Yu Xue & Chengkuan He & Yongnian Zhao, 2022. "Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades," Energies, MDPI, vol. 15(15), pages 1-31, August.
    16. Cheng, Xu & Shi, Fan & Liu, Yongping & Liu, Xiufeng & Huang, Lizhen, 2022. "Wind turbine blade icing detection: a federated learning approach," Energy, Elsevier, vol. 254(PC).
    17. Khazaee, Meghdad & Derian, Pierre & Mouraud, Anthony, 2022. "A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods," Renewable Energy, Elsevier, vol. 199(C), pages 1568-1579.
    18. Stoyanov, D.B. & Nixon, J.D. & Sarlak, H., 2021. "Analysis of derating and anti-icing strategies for wind turbines in cold climates," Applied Energy, Elsevier, vol. 288(C).
    19. Wang, Zixuan & Qin, Bo & Sun, Haiyue & Zhang, Jian & Butala, Mark D. & Demartino, Cristoforo & Peng, Peng & Wang, Hongwei, 2023. "An imbalanced semi-supervised wind turbine blade icing detection method based on contrastive learning," Renewable Energy, Elsevier, vol. 212(C), pages 251-262.
    20. Ivan Kabardin & Sergey Dvoynishnikov & Maxim Gordienko & Sergey Kakaulin & Vadim Ledovsky & Grigoriy Gusev & Vladislav Zuev & Valery Okulov, 2021. "Optical Methods for Measuring Icing of Wind Turbine Blades," Energies, MDPI, vol. 14(20), pages 1-14, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:179:y:2021:i:c:p:1098-1105. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.