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Wind Turbine Blade Damage Evaluation under Multiple Operating Conditions and Based on 10-Min SCADA Data

Author

Listed:
  • Antoine Chrétien

    (Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

  • Antoine Tahan

    (Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

  • Francis Pelletier

    (Advanced Analytics Research Power Factors, Montreal, QC H3B 4W5, Canada)

Abstract

The present paper aims to enable the assessment of the fatigue damage of wind turbine blades over a long duration (e.g., several months/years) in conjunction with different operating regimes and based on two information sources: the 10-min SCADA data and an interpolation using response surfaces identified using the FAST aeroelastic numerical tool. To assess blade damage, prior studies highlighted the need for a high-frequency (>1 Hz) sampling rate. Because of data availability and computation resource limitations, such methods limit the duration of the analysis period, making the direct use of such an approach based on a 1 Hz wind speed signal in current wind farms impractical. The present work investigates the possibility of overcoming these issues by estimating the equivalent damage using a 1 Hz wind speed for each 10-min sample stored in the SCADA data. In the literature, the influence of operating regimes is not considered in fatigue damage estimation, and for the first time, the present project takes a pioneering approach by considering these operating regimes.

Suggested Citation

  • Antoine Chrétien & Antoine Tahan & Francis Pelletier, 2024. "Wind Turbine Blade Damage Evaluation under Multiple Operating Conditions and Based on 10-Min SCADA Data," Energies, MDPI, vol. 17(5), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1202-:d:1350193
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    References listed on IDEAS

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    1. Jiang, Zhiyu & Xing, Yihan, 2022. "Load mitigation method for wind turbines during emergency shutdowns," Renewable Energy, Elsevier, vol. 185(C), pages 978-995.
    2. Hu, Weifei & Chen, Weiyi & Wang, Xiaobo & Jiang, Zhiyu & Wang, Yeqing & Verma, Amrit Shankar & Teuwen, Julie J.E., 2021. "A computational framework for coating fatigue analysis of wind turbine blades due to rain erosion," Renewable Energy, Elsevier, vol. 170(C), pages 236-250.
    3. Vera-Tudela, Luis & Kühn, Martin, 2017. "Analysing wind turbine fatigue load prediction: The impact of wind farm flow conditions," Renewable Energy, Elsevier, vol. 107(C), pages 352-360.
    4. Zárate-Miñano, Rafael & Anghel, Marian & Milano, Federico, 2013. "Continuous wind speed models based on stochastic differential equations," Applied Energy, Elsevier, vol. 104(C), pages 42-49.
    5. Jannie S. Nielsen & John D. Sørensen, 2017. "Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades," Energies, MDPI, vol. 10(5), pages 1-13, May.
    Full references (including those not matched with items on IDEAS)

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