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Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data

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  • Li, Yanting
  • Liu, Shujun
  • Shu, Lianjie

Abstract

Effective condition monitoring and fault diagnosis of wind turbines are crucial for avoiding serious damages to wind turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides valuable insights into turbine performance. In order to make full use of such valuable information, this paper investigates fault diagnosis of wind turbines by using Gaussian process classifiers (GPC) to the operational data collected from the SCADA system. Both real-time and predictive fault diagnosis were considered. As an alternative to the support vector machine (SVM) technique, the GPC possesses the capability of providing probabilistic information about the fault types, which is valuable for making maintenance plan in real practice. The comparison results show that the GPC method is able to provide more accurate fault diagnosis results than the SVM technique on average.

Suggested Citation

  • Li, Yanting & Liu, Shujun & Shu, Lianjie, 2019. "Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data," Renewable Energy, Elsevier, vol. 134(C), pages 357-366.
    • Handle: RePEc:eee:renene:v:134:y:2019:i:c:p:357-366
    DOI: 10.1016/j.renene.2018.10.088
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    5. Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
    6. Konstantina Fotiadou & Terpsichori Helen Velivassaki & Artemis Voulkidis & Dimitrios Skias & Corrado De Santis & Theodore Zahariadis, 2020. "Proactive Critical Energy Infrastructure Protection via Deep Feature Learning," Energies, MDPI, vol. 13(10), pages 1-19, May.
    7. Sun, Chenhao & Xu, Hao & Zeng, Xiangjun & Wang, Wen & Jiang, Fei & Yang, Xin, 2023. "A vulnerability spatiotemporal distribution prognosis framework for integrated energy systems within intricate data scenes according to importance-fuzzy high-utility pattern identification," Applied Energy, Elsevier, vol. 344(C).
    8. Li, Yanting & Wu, Zhenyu, 2020. "A condition monitoring approach of multi-turbine based on VAR model at farm level," Renewable Energy, Elsevier, vol. 166(C), pages 66-80.
    9. Li, Yanting & Jiang, Wenbo & Zhang, Guangyao & Shu, Lianjie, 2021. "Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data," Renewable Energy, Elsevier, vol. 171(C), pages 103-115.
    10. Liu, Ding Peng & Ferri, Giulio & Heo, Taemin & Marino, Enzo & Manuel, Lance, 2024. "On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model," Renewable Energy, Elsevier, vol. 225(C).
    11. Wang, Zhenya & Yao, Ligang & Cai, Yongwu & Zhang, Jun, 2020. "Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis," Renewable Energy, Elsevier, vol. 155(C), pages 1312-1327.

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