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Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region

Author

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  • Minh-Quang Tran

    (Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan
    Industry 4.0 Implementation Center, Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan)

  • Yi-Chen Li

    (Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, Hsinchu 31040, Taiwan)

  • Chen-Yang Lan

    (Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan)

  • Meng-Kun Liu

    (Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan
    Industry 4.0 Implementation Center, Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan)

Abstract

A novel concept of wind farm fault detection by monitoring the wind speed in the wake region is proposed in this study. A wind energy dissipation model was coupled with a computational fluid dynamics solver to simulate the fluid field of a wind turbine array, and the wind velocity and direction in the simulation were exported for identifying wind turbine faults. The 3D steady Navier–Stokes equations were solved by using the cell center finite volume method with a second order upwind scheme and a k − ε turbulence model. In addition, the wind energy dissipation model, derived from energy balance and Betz’s law, was added to the Navier–Stokes equations’ source term. The simulation results indicate that the wind speed distribution in the wake region contains significant information regarding multiple wind turbine faults. A feature selection algorithm specifically designed for the analysis of wind flow was proposed to reduce the number of features. This algorithm proved to have better performance than fuzzy entropy measures and recursive feature elimination methods under a limited number of features. As a result, faults in the wind turbine array could be detected and identified by machine learning algorithms.

Suggested Citation

  • Minh-Quang Tran & Yi-Chen Li & Chen-Yang Lan & Meng-Kun Liu, 2020. "Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region," Energies, MDPI, vol. 13(24), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6559-:d:460902
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Mona A. S. Ali & Fathimathul Rajeena P. P. & Diaa Salama Abd Elminaam, 2022. "An Efficient Heap Based Optimizer Algorithm for Feature Selection," Mathematics, MDPI, vol. 10(14), pages 1-33, July.

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