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Prognostic techniques applied to maintenance of wind turbines: a concise and specific review

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  • Leite, Gustavo de Novaes Pires
  • Araújo, Alex Maurício
  • Rosas, Pedro André Carvalho

Abstract

Wind turbine installation is growing consistently and fast. Wind turbines are getting bigger in size and power, what incurs that a simple unit breakdown causes large energy losses. They operate under varying, complex and dynamic loads due to the environmental conditions, such as wind shear, turbulence, gusts, etc. Condition-based and prognostic and health maintenance is key to assure reliable and efficient performance of wind farms, especially offshore. Fault diagnosis is important, but given the operational complexity of the wind turbines, previous knowledge about the condition of a wind turbine component and fault prognostics are the state of the art of wind farm operation. Although some advance has been made in the diagnostics of faults of wind turbines, very little research has been carried out in the field of fault prognostics of wind turbines. Then, there is an urgent need to develop prognostic techniques for such complex systems operating in real conditions. This article aims to present a comprehensive, specific and concise review of the up to date efforts and advances in the research of prognostic techniques and remaining useful life estimation methods applied to the critical components of wind turbines and analyse its advantages, capabilities and limitations.

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  • Leite, Gustavo de Novaes Pires & Araújo, Alex Maurício & Rosas, Pedro André Carvalho, 2018. "Prognostic techniques applied to maintenance of wind turbines: a concise and specific review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1917-1925.
    • Handle: RePEc:eee:rensus:v:81:y:2018:i:p2:p:1917-1925
    DOI: 10.1016/j.rser.2017.06.002
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    7. de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.
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    Cited by:

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    4. Luis M. Abadie & Nestor Goicoechea, 2021. "Old Wind Farm Life Extension vs. Full Repowering: A Review of Economic Issues and a Stochastic Application for Spain," Energies, MDPI, vol. 14(12), pages 1-24, June.
    5. 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.
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    7. Rybak, Aurelia & Rybak, Aleksandra & Kolev, Spas D., 2024. "Development of wind energy and access to REE. The case of Poland," Resources Policy, Elsevier, vol. 90(C).
    8. Bakdi, Azzeddine & Kouadri, Abdelmalek & Mekhilef, Saad, 2019. "A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 546-555.

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