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Fault diagnosis of wind turbine with SCADA alarms based multidimensional information processing method

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  • Qiu, Yingning
  • Feng, Yanhui
  • Infield, David

Abstract

This paper presents a first attempt to use Dempster-Shafer (D-S) evidence theory for the fault diagnosis of wind turbine (WT) on SCADA alarm data. As two important elements in D-S evidence theory, identification framework (IF) and Basic Probability Assignment (BPA) are derived from WT maintenance records and SCADA alarm data. A procedure of multi-dimensional information fusion for WT fault diagnosis is presented. The diagnosis accuracy using BPAs obtained from a sample WT and from the wind farm are compared and evaluated. The result shows that D-S evidence theory as a multidimensional information processing method is useful for WT fault diagnosis. Compared to previous SCADA alarms processing methods, the approach proposed predominates at aspects of simple calculation, superior capability on dealing with large volume of alarms through quantifying fault probabilities. It has the advantages of being easy to perform, low cost and explainable, which make it ideal for online application. A self-BPA-generating procedure for future online application with this approach is also provided in this paper. It is concluded that D-S evidence theory applied to SCADA alarm analysis is a valuable approach to intelligent wind farm management.

Suggested Citation

  • Qiu, Yingning & Feng, Yanhui & Infield, David, 2020. "Fault diagnosis of wind turbine with SCADA alarms based multidimensional information processing method," Renewable Energy, Elsevier, vol. 145(C), pages 1923-1931.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:1923-1931
    DOI: 10.1016/j.renene.2019.07.110
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    Citations

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    Cited by:

    1. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
    2. Zhan, Jun & Wu, Chengkun & Yang, Canqun & Miao, Qiucheng & Wang, Shilin & Ma, Xiandong, 2022. "Condition monitoring of wind turbines based on spatial-temporal feature aggregation networks," Renewable Energy, Elsevier, vol. 200(C), pages 751-766.
    3. Huifan Zeng & Juchuan Dai & Chengming Zuo & Huanguo Chen & Mimi Li & Fan Zhang, 2022. "Correlation Investigation of Wind Turbine Multiple Operating Parameters Based on SCADA Data," Energies, MDPI, vol. 15(14), pages 1-24, July.
    4. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    5. 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.
    6. Ruiming, Fang & Minling, Wu & xinhua, Guo & Rongyan, Shang & Pengfei, Shao, 2020. "Identifying early defects of wind turbine based on SCADA data and dynamical network marker," Renewable Energy, Elsevier, vol. 154(C), pages 625-635.
    7. Wang, Ziqi & Liu, Changliang & Yan, Feng, 2022. "Condition monitoring of wind turbine based on incremental learning and multivariate state estimation technique," Renewable Energy, Elsevier, vol. 184(C), pages 343-360.
    8. Kenneth E. Okedu & S. M. Muyeen, 2022. "Comparative Performance of DFIG and PMSG Wind Turbines during Transient State in Weak and Strong Grid Conditions Considering Series Dynamic Braking Resistor," Energies, MDPI, vol. 15(23), pages 1-22, December.
    9. Sun, Chuan & Chen, Yueyi & Cheng, Cheng, 2021. "Imputation of missing data from offshore wind farms using spatio-temporal correlation and feature correlation," Energy, Elsevier, vol. 229(C).
    10. Wu, Yueqi & Ma, Xiandong, 2022. "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines," Renewable Energy, Elsevier, vol. 181(C), pages 554-566.
    11. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    12. Aphrodis Nduwamungu & Etienne Ntagwirumugara & Francis Mulolani & Waqar Bashir, 2020. "Fault Ride through Capability Analysis (FRT) in Wind Power Plants with Doubly Fed Induction Generators for Smart Grid Technologies," Energies, MDPI, vol. 13(16), pages 1-26, August.

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