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Wind Turbine Generator Controller Signals Supervised Machine Learning for Shaft Misalignment Fault Detection: A Doubly Fed Induction Generator Practical Case Study

Author

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  • Ahmed Al-Ajmi

    (Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 9PL, UK)

  • Yingzhao Wang

    (Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 9PL, UK)

  • Siniša Djurović

    (Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 9PL, UK)

Abstract

With a continued strong increase in wind generator applications, the condition monitoring of wind turbine systems has become ever more important in ensuring the availability and reduced cost of produced power. One of the key turbine conditions requiring constant monitoring is the generator shaft alignment, which if compromised and untreated can lead to catastrophic system failures. This study explores the possibility of employing supervised machine learning methods on the readily available generator controller loop signals to achieve detection of shaft misalignment condition. This could provide a highly noninvasive and low-cost solution for misalignment monitoring in comparison with the current misalignment monitoring field practice that relies on invasive and costly drivetrain vibration analysis. The study utilises signal datasets measured on a dedicated doubly fed induction generator test rig to demonstrate that high consistency and accuracy recognition of shaft angular misalignment can be achieved through the application of supervised machine learning on controller loop signals. The average recognition accuracy rate of up to 98.8% is shown to be attainable through analysis of a key feature subset of the stator flux-oriented controller signals in a range of operating speeds and loads.

Suggested Citation

  • Ahmed Al-Ajmi & Yingzhao Wang & Siniša Djurović, 2021. "Wind Turbine Generator Controller Signals Supervised Machine Learning for Shaft Misalignment Fault Detection: A Doubly Fed Induction Generator Practical Case Study," Energies, MDPI, vol. 14(6), pages 1-15, March.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1601-:d:516463
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    References listed on IDEAS

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    1. Amirat, Y. & Benbouzid, M.E.H. & Al-Ahmar, E. & Bensaker, B. & Turri, S., 2009. "A brief status on condition monitoring and fault diagnosis in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2629-2636, December.
    2. Martin, Rebecca & Lazakis, Iraklis & Barbouchi, Sami & Johanning, Lars, 2016. "Sensitivity analysis of offshore wind farm operation and maintenance cost and availability," Renewable Energy, Elsevier, vol. 85(C), pages 1226-1236.
    3. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    4. Elsie Swana & Wesley Doorsamy, 2021. "An Unsupervised Learning Approach to Condition Assessment on a Wound-Rotor Induction Generator," Energies, MDPI, vol. 14(3), pages 1-18, January.
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    Cited by:

    1. Adolfo Dannier & Emanuele Fedele & Ivan Spina & Gianluca Brando, 2022. "Doubly-Fed Induction Generator (DFIG) in Connected or Weak Grids for Turbine-Based Wind Energy Conversion System," Energies, MDPI, vol. 15(17), pages 1-5, September.

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