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Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Author

Listed:
  • Yuri Merizalde

    (Faculty of Chemical Engineering, University of Guayaquil, Clemente Ballen 2709 and Ismael Perez Pazmiño, Guayaquil 593, Ecuador)

  • Luis Hernández-Callejo

    (Department of Agricultural Engineering and Forestry, University of Valladolid (UVA), Campus Universitario Duques de Soria, 42004 Soria, Spain)

  • Oscar Duque-Perez

    (Department of Electrical Engineering, University of Valladolid (UVA), Escuela de Ingenierías Industriales, Paseo del Cauce 59, 47011 Valladolid, Spain)

  • Víctor Alonso-Gómez

    (Department of Phisical, University of Valladolid (UVA), Campus Universitario Duques de Soria, 42004 Soria, Spain)

Abstract

Wind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models.

Suggested Citation

  • Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez & Víctor Alonso-Gómez, 2019. "Maintenance Models Applied to Wind Turbines. A Comprehensive Overview," Energies, MDPI, vol. 12(2), pages 1-41, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:225-:d:196983
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    References listed on IDEAS

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    2. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    3. Tianna Bloise Thomaz & David Crooks & Encarni Medina-Lopez & Leonore van Velzen & Henry Jeffrey & Joseba Lopez Mendia & Raul Rodriguez Arias & Pablo Ruiz Minguela, 2019. "O&M Models for Ocean Energy Converters: Calibrating through Real Sea Data," Energies, MDPI, vol. 12(13), pages 1-20, June.
    4. Luca Pinciroli & Piero Baraldi & Guido Ballabio & Michele Compare & Enrico Zio, 2021. "Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews," Energies, MDPI, vol. 14(20), pages 1-17, October.
    5. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    6. Juan Izquierdo & Adolfo Crespo Márquez & Jone Uribetxebarria & Asier Erguido, 2019. "Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance," Energies, MDPI, vol. 12(11), pages 1-17, May.

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