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Rainflow-counting matrix interpolation over different operating conditions for hydroelectric turbine fatigue assessment

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  • Pham, Quang Hung
  • Gagnon, Martin
  • Antoni, Jérôme
  • Tahan, Antoine
  • Monette, Christine

Abstract

The measurement of stress/strain on blade plays an essential role in the fatigue assessment of the hydroelectric turbine runner. However, a typical measurement campaign does not cover all the possible operating conditions due to the field measurement requirements and limitations, which leads to fatigue evaluation missing for unmeasured conditions. In this paper, kriging interpolation method is used to estimate the missing fatigue information based on the available measured operating conditions of the turbine. The rainflow counting matrix, which contains the fatigue cycles of strain signal, is considered as the input of the interpolation process. For validation, the fatigue cycles are randomly generated, using bivariate Gaussian distributions, from the interpolated matrix for comparison with the experimental values under similar operating conditions. The developed approach will help plan experimental campaign and might contribute to decreasing the measurement requirements for runner fatigue assessment.

Suggested Citation

  • Pham, Quang Hung & Gagnon, Martin & Antoni, Jérôme & Tahan, Antoine & Monette, Christine, 2021. "Rainflow-counting matrix interpolation over different operating conditions for hydroelectric turbine fatigue assessment," Renewable Energy, Elsevier, vol. 172(C), pages 465-476.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:465-476
    DOI: 10.1016/j.renene.2021.03.036
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    References listed on IDEAS

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    1. Liu, Xin & Luo, Yongyao & Wang, Zhengwei, 2016. "A review on fatigue damage mechanism in hydro turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1-14.
    2. Presas, Alexandre & Luo, Yongyao & Wang, Zhengwei & Guo, Bao, 2019. "Fatigue life estimation of Francis turbines based on experimental strain measurements: Review of the actual data and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 96-110.
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    Cited by:

    1. Jie Li & Chongyang Han & Weibin Wu & Ting Tang & Xiao Ran & Zefeng Zheng & Shunli Sun, 2022. "Load Spectrum Compilation Method of Hybrid Electric Vehicle Reducers Based on Multi-Criteria Decision Making," Energies, MDPI, vol. 15(9), pages 1-18, April.
    2. Hojat Behrooz & Carlo Lipizzi & George Korfiatis & Mohammad Ilbeigi & Martin Powell & Mina Nouri, 2023. "Towards Automating the Identification of Sustainable Projects Seeking Financial Support: An AI-Powered Approach," Sustainability, MDPI, vol. 15(12), pages 1-12, June.
    3. Pham, Quang Hung & Gagnon, Martin & Antoni, Jérôme & Tahan, Antoine & Monette, Christine, 2022. "Prediction of hydroelectric turbine runner strain signal via cyclostationary decomposition and kriging interpolation," Renewable Energy, Elsevier, vol. 182(C), pages 998-1011.
    4. Valentín, David & Presas, Alexandre & Egusquiza, Mònica & Drommi, Jean-Louis & Valero, Carme, 2022. "Benefits of battery hybridization in hydraulic turbines. Wear and tear evaluation in a Kaplan prototype," Renewable Energy, Elsevier, vol. 199(C), pages 35-43.

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