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A hybrid prognostic methodology for tidal turbine gearboxes

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Listed:
  • Elasha, Faris
  • Mba, David
  • Togneri, Michael
  • Masters, Ian
  • Teixeira, Joao Amaral

Abstract

Tidal energy is one of promising solutions for reducing greenhouse gas emissions and it is estimated that 100 TWh of electricity could be produced every year from suitable sites around the world. Although premature gearbox failures have plagued the wind turbine industry, and considerable research efforts continue to address this challenge, tidal turbine gearboxes are expected to experience higher mechanical failure rates given they will experience higher torque and thrust forces. In order to minimize the maintenance cost and prevent unexpected failures there exists a fundamental need for prognostic tools that can reliably estimate the current health and predict the future condition of the gearbox.

Suggested Citation

  • Elasha, Faris & Mba, David & Togneri, Michael & Masters, Ian & Teixeira, Joao Amaral, 2017. "A hybrid prognostic methodology for tidal turbine gearboxes," Renewable Energy, Elsevier, vol. 114(PB), pages 1051-1061.
    • Handle: RePEc:eee:renene:v:114:y:2017:i:pb:p:1051-1061
    DOI: 10.1016/j.renene.2017.07.093
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    References listed on IDEAS

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    1. Tedds, S.C. & Owen, I. & Poole, R.J., 2014. "Near-wake characteristics of a model horizontal axis tidal stream turbine," Renewable Energy, Elsevier, vol. 63(C), pages 222-235.
    2. Culley, D.M. & Funke, S.W. & Kramer, S.C. & Piggott, M.D., 2016. "Integration of cost modelling within the micro-siting design optimisation of tidal turbine arrays," Renewable Energy, Elsevier, vol. 85(C), pages 215-227.
    3. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
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    Cited by:

    1. Qian, Peng & Feng, Bo & Liu, Hao & Tian, Xiange & Si, Yulin & Zhang, Dahai, 2019. "Review on configuration and control methods of tidal current turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 125-139.
    2. Xiaochuan Li & Faris Elasha & Suliman Shanbr & David Mba, 2019. "Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning," Energies, MDPI, vol. 12(14), pages 1-17, July.
    3. 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.
    4. Pan, Yubin & Hong, Rongjing & Chen, Jie & Wu, Weiwei, 2020. "A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox," Renewable Energy, Elsevier, vol. 152(C), pages 138-154.
    5. Teng, Yiming & Hu, Dewen & Wu, Feng & Zhang, Ridong & Gao, Furong, 2020. "Fast economic model predictive control for marine current turbine generator system," Renewable Energy, Elsevier, vol. 166(C), pages 108-116.
    6. Perez, Larissa & Cossu, Remo & Grinham, Alistair & Penesis, Irene, 2022. "An investigation of tidal turbine performance and loads under various turbulence conditions using Blade Element Momentum theory and high-frequency field data acquired in two prospective tidal energy s," Renewable Energy, Elsevier, vol. 201(P1), pages 928-937.

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