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Probabilistic failure rate model of a tidal turbine pitch system

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  • Ewing, Fraser J.
  • Thies, Philipp R.
  • Shek, Jonathan
  • Ferreira, Claudio Bittencourt

Abstract

Accurate reliability prediction for tidal turbines is challenging due to scarce reliability data. To achieve commercialization, it is widely acknowledged that reductions in maintenance costs are vital and robust component reliability assessments can help drive this. For established technologies, reliability prediction either involves a statistical assessment of historical failure data, or a physics of failure approach based on dedicated accelerated testing. However, for low/mid Technology Readiness Level tidal developers these common approaches are difficult. Thus, developers require a method of making reliability predictions for components in the absence of tidal turbine specific failure data and physical testing results. This paper presents a failure rate model for a tidal turbine pitch system using empirical Physics of Failure equations, with associated uncertainties. Critical component design parameters are determined and their effects on the failure rate investigated via a sensitivity analysis. The modelled failure rate is then compared with wind turbine failure data from a series of turbines. The tidal turbine failure rate is approximately 50% lower, however high reliability requirements mean this is unlikely to be acceptable. The developed model can assist turbine developers in estimating failure rates and determining reliability critical design parameters for the failure critical pitch system.

Suggested Citation

  • Ewing, Fraser J. & Thies, Philipp R. & Shek, Jonathan & Ferreira, Claudio Bittencourt, 2020. "Probabilistic failure rate model of a tidal turbine pitch system," Renewable Energy, Elsevier, vol. 160(C), pages 987-997.
  • Handle: RePEc:eee:renene:v:160:y:2020:i:c:p:987-997
    DOI: 10.1016/j.renene.2020.06.142
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    References listed on IDEAS

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    1. Lin, Yonggang & Tu, Le & Liu, Hongwei & Li, Wei, 2016. "Fault analysis of wind turbines in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 482-490.
    2. Thies, Philipp R. & Smith, George H. & Johanning, Lars, 2012. "Addressing failure rate uncertainties of marine energy converters," Renewable Energy, Elsevier, vol. 44(C), pages 359-367.
    3. Brian G. Sellar & Gareth Wakelam & Duncan R. J. Sutherland & David M. Ingram & Vengatesan Venugopal, 2018. "Characterisation of Tidal Flows at the European Marine Energy Centre in the Absence of Ocean Waves," Energies, MDPI, vol. 11(1), pages 1-23, January.
    4. Sebastian Pfaffel & Stefan Faulstich & Kurt Rohrig, 2017. "Performance and Reliability of Wind Turbines: A Review," Energies, MDPI, vol. 10(11), pages 1-27, November.
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    Cited by:

    1. Dimitri V. Val & Leon Chernin & Daniil Yurchenko, 2021. "Updatable Probabilistic Evaluation of Failure Rates of Mechanical Components in Power Take-Off Systems of Tidal Stream Turbines," Energies, MDPI, vol. 14(20), pages 1-19, October.
    2. Famoso, Fabio & Brusca, Sebastian & D'Urso, Diego & Galvagno, Antonio & Chiacchio, Ferdinando, 2020. "A novel hybrid model for the estimation of energy conversion in a wind farm combining wake effects and stochastic dependability," Applied Energy, Elsevier, vol. 280(C).
    3. Tenis Ranjan Munaweera Thanthirige & Jamie Goggins & Michael Flanagan & William Finnegan, 2023. "A State-of-the-Art Review of Structural Testing of Tidal Turbine Blades," Energies, MDPI, vol. 16(10), pages 1-20, May.
    4. D'Urso, Diego & Chiacchio, Ferdinando & Cavalieri, Salvatore & Gambadoro, Salvatore & Khodayee, Soheyl Moheb, 2024. "Predictive maintenance of standalone steel industrial components powered by a dynamic reliability digital twin model with artificial intelligence," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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