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Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

Author

Listed:
  • Mohamed Benbouzid

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027), University of Brest, 29238 Brest, France
    Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

  • Tarek Berghout

    (Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, Algeria)

  • Nur Sarma

    (Electrical and Electronic Engineering Department, Duzce University, Düzce 81620, Turkey)

  • Siniša Djurović

    (Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 3BB, UK)

  • Yueqi Wu

    (Engineering Department, Lancaster University, Lancaster LA1 4YW, UK)

  • Xiandong Ma

    (Engineering Department, Lancaster University, Lancaster LA1 4YW, UK)

Abstract

Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

Suggested Citation

  • Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5967-:d:639498
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    References listed on IDEAS

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    Cited by:

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    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. Usama Aziz & Sylvie Charbonnier & Christophe Berenguer & Alexis Lebranchu & Frederic Prevost, 2022. "A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods," Energies, MDPI, vol. 15(8), pages 1-21, April.
    4. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
    5. Gang Li & Weidong Zhu, 2022. "A Review on Up-to-Date Gearbox Technologies and Maintenance of Tidal Current Energy Converters," Energies, MDPI, vol. 15(23), pages 1-24, December.
    6. Małgorzata Jastrzębska, 2022. "Installation’s Conception in the Field of Renewable Energy Sources for the Needs of the Silesian Botanical Garden," Energies, MDPI, vol. 15(18), pages 1-28, September.

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