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Reliability assessment of generic geared wind turbines by GTST-MLD model and Monte Carlo simulation

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  • Li, Y.F.
  • Valla, S.
  • Zio, E.

Abstract

In the last decade, the installed capacity of wind turbines has increased far more than other renewable energy sources such as solar, biomass or geothermal. As for any energy equipment, reliability is the fundamental attribute that needs to be guaranteed. A number of studies have been carried out for wind turbine reliability assessment. Most of them model the wind turbine system as a whole, without investigating its interior structure and failure logic. In this paper, a modeling and simulation framework is proposed for the reliability assessment of generic geared wind turbine systems. It is based on a Goal Tree, Success Tree and Master Logic Diagram for modeling the relationships among components and functions in a wind turbine system, and the impact of factors and mechanisms influencing the failure of the components. The modeling framework is customized to represent the strength of the relationships and the uncertainty of the impact of failures of these components on other components and functions. The model is eventually integrated in a Monte Carlo simulation framework for the computation of the wind turbine system reliability. Finally, model validation is performed by comparing the simulation results with those obtained by a Bayesian network model developed for this purpose.

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  • Li, Y.F. & Valla, S. & Zio, E., 2015. "Reliability assessment of generic geared wind turbines by GTST-MLD model and Monte Carlo simulation," Renewable Energy, Elsevier, vol. 83(C), pages 222-233.
    • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:222-233
    DOI: 10.1016/j.renene.2015.04.035
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    References listed on IDEAS

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    Cited by:

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    8. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    9. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. 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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