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Data-driven fault detection and isolation scheme for a wind turbine benchmark

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  • de Bessa, Iury Valente
  • Palhares, Reinaldo Martinez
  • D'Angelo, Marcos Flávio Silveira Vasconcelos
  • Chaves Filho, João Edgar

Abstract

This paper investigates a new scheme for fault detection and isolation based on time series and data analysis. This scheme is applied in wind turbines which are used to tap the potential of renewable energy. The proposed scheme is performed in two steps and it is based on process data without using any kind of physical modeling. The first step, the fault detection, is based on an alternative method based on the Gibbs sampling algorithm in which the occurrence of a sensor fault is modeled as a change point detection in a time series. The second step, the fault isolation, is handled via a Fuzzy/Bayesian network scheme classifying the kind of fault. The proposed fault detection and isolation (FDI) strategy offers as main contribution the independence from any kind of dynamical modeling and the unprecedented usage of the Gibbs sampling. Furthermore, this work offers a novel data driven FDI approach based on Fuzzy-Bayesian inference and suitable for the wind turbines systems. This approach presents a good performance for detection and diagnostics of sensor faults in a standard wind turbine benchmark.

Suggested Citation

  • de Bessa, Iury Valente & Palhares, Reinaldo Martinez & D'Angelo, Marcos Flávio Silveira Vasconcelos & Chaves Filho, João Edgar, 2016. "Data-driven fault detection and isolation scheme for a wind turbine benchmark," Renewable Energy, Elsevier, vol. 87(P1), pages 634-645.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:634-645
    DOI: 10.1016/j.renene.2015.10.061
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    References listed on IDEAS

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    Cited by:

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    2. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
    3. Yun-Tao Shi & Yuan Zhang & Xiang Xiang & Li Wang & Zhen-Wu Lei & De-Hui Sun, 2018. "Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs," Energies, MDPI, vol. 11(9), pages 1-22, August.
    4. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    5. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
    6. Menon, Muraleekrishnan & Ponta, Fernando L., 2017. "Dynamic aeroelastic behavior of wind turbine rotors in rapid pitch-control actions," Renewable Energy, Elsevier, vol. 107(C), pages 327-339.
    7. Liu, Xianzeng & Yang, Yuhu & Zhang, Jun, 2018. "Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear," Renewable Energy, Elsevier, vol. 122(C), pages 65-79.
    8. 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.
    9. Bakdi, Azzeddine & Kouadri, Abdelmalek & Mekhilef, Saad, 2019. "A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 546-555.

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