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An Approach of Quantifying Gear Fatigue Life for Wind Turbine Gearboxes Using Supervisory Control and Data Acquisition Data

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  • Yingning Qiu

    (School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Lang Chen

    (School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Yanhui Feng

    (School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Yili Xu

    (Zhejiang Windey Ltd. by Share Ltd, 22F, Building A, the West Lake International Plaza S&T, No. 391, Wen’er Road, Hangzhou 310012, China)

Abstract

Quantifying wind turbine (WT) gearbox fatigue life is a critical problem for preventive maintenance when unsolved. This paper proposes a practical approach that uses ten minutes’ average wind speed of Supervisory Control and Data Acquisition (SCADA) data to quantify a WT gearbox’s gear fatigue life. Wind turbulence impacts on gearbox fatigue are studied thoroughly. Short-term fatigue assessment for the gearbox is then performed using linear fatigue theory by considering WT responses under external and internal excitation. The results shows that for a three stage gearbox, the sun gear in the first stage and pinions in the 2nd and 3rd stage are the most vulnerable parts. High mean wind speed, especially above the rated range, leads to a high risk of gearbox fatigue damage. Increase of wind turbulence may not increase fatigue damage as long as a WT has an instant response to external excitation. An approach of using SCADA data recorded every ten minutes to quantify gearbox long-term damages is presented. The calculation results show that the approach effectively presents gears’ performance degradation by quantifying their fatigue damage. This is critical to improve WT reliability and meaningful for WT gearbox fatigue assessment theory. The result provides useful tools for future wind farm prognostic maintenance.

Suggested Citation

  • Yingning Qiu & Lang Chen & Yanhui Feng & Yili Xu, 2017. "An Approach of Quantifying Gear Fatigue Life for Wind Turbine Gearboxes Using Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 10(8), pages 1-21, July.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1084-:d:105773
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    References listed on IDEAS

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    1. Mengnan Cao & Yingning Qiu & Yanhui Feng & Hao Wang & Dan Li, 2016. "Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data," Energies, MDPI, vol. 9(10), pages 1-18, October.
    2. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    3. Beganovic, Nejra & Söffker, Dirk, 2016. "Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained result," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 68-83.
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    Cited by:

    1. W. Dheelibun Remigius & Anand Natarajan, 2022. "A review of wind turbine drivetrain loads and load effects for fixed and floating wind turbines," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(1), January.
    2. Yaru Yang & Hua Li & Jin Yao & Wenxiang Gao & Haiyan Peng, 2019. "Analysis on the Force and Life of Gearbox in Double-Rotor Wind Turbine," Energies, MDPI, vol. 12(21), pages 1-19, November.
    3. Ravi Kumar Pandit & Davide Astolfi & Isidro Durazo Cardenas, 2023. "A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines," Energies, MDPI, vol. 16(4), pages 1-17, February.
    4. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.

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