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Wind turbine blade icing diagnosis using RFECV-TSVM pseudo-sample processing

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  • Bai, Xinjian
  • Tao, Tao
  • Gao, Linyue
  • Tao, Cheng
  • Liu, Yongqian

Abstract

Wind turbine blade icing seriously affects turbine power generation and fatigue life, and an accurate diagnosis of blade icing is beneficial for wind turbines to make in-time adjustments. However, the high dimensional and unbalanced original data recorded by Supervisory Control and Data Acquisition (SCADA) systems pose great challenges to the accurate diagnosis of blade icing. To effectively address the challenges of difficult feature extraction and small number of fault samples, we propose a data processing method based on pseudo-sample processing. Specifically, Recursive Feature Elimination and Cross-Validation (RFECV) is used to analyze the influence of various SCADA features on the diagnostic model and select the most compelling feature set. A Transductive Support Vector Machine (TSVM) is implemented to regenerate unlabelled samples. The labeled pseudo samples and ice data are combined to form the training set. The effectiveness of the proposed method is examined using the three most commonly used classifier algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), and XGBoost, for four utility-scale wind turbines. The results show that this method can effectively obtain the optimal selection, utilize unlabelled samples, and improve the diagnostic accuracy of the model, especially for small sample data, with an average accuracy improvement of 10.06%.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:211:y:2023:i:c:p:412-419
    DOI: 10.1016/j.renene.2023.04.107
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    References listed on IDEAS

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    1. Gao, Linyue & Tao, Tao & Liu, Yongqian & Hu, Hui, 2021. "A field study of ice accretion and its effects on the power production of utility-scale wind turbines," Renewable Energy, Elsevier, vol. 167(C), pages 917-928.
    2. 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.
    3. Fareed, Zeeshan & Pata, Ugur Korkut, 2022. "Renewable, non-renewable energy consumption and income in top ten renewable energy-consuming countries: Advanced Fourier based panel data approaches," Renewable Energy, Elsevier, vol. 194(C), pages 805-821.
    4. Alemzero, David & Acheampong, Theophilus & Huaping, Sun, 2021. "Prospects of wind energy deployment in Africa: Technical and economic analysis," Renewable Energy, Elsevier, vol. 179(C), pages 652-666.
    5. Lei, Xiying & Alharthi, Majed & Ahmad, Ishtiaq & Aziz, Babar & Abdin, Zain ul, 2022. "Importance of international relations for the promotion of renewable energy, preservation of natural resources and environment: Empirics from SEA nations," Renewable Energy, Elsevier, vol. 196(C), pages 1250-1257.
    6. Daniliuk, Vladislav & Xu, Yuanming & Liu, Ruobing & He, Tianpeng & Wang, Xi, 2020. "Ultrasonic de-icing of wind turbine blades: Performance comparison of perspective transducers," Renewable Energy, Elsevier, vol. 145(C), pages 2005-2018.
    7. Tao, Tao & Liu, Yongqian & Qiao, Yanhui & Gao, Linyue & Lu, Jiaoyang & Zhang, Ce & Wang, Yu, 2021. "Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 1004-1013.
    8. 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.
    9. 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.
    10. 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.
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