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Condition monitoring of a wind turbine drive train based on its power dependant vibrations

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  • Romero, Antonio
  • Soua, Slim
  • Gan, Tat-Hean
  • Wang, Bin

Abstract

Increasing the reliability and the downtime of wind turbines is critical to minimise the cost of energy (COE) in the wind sector, especially for offshore wind turbines. Due to the high impact that gearboxes and generator downtimes create on wind turbines, reliable and cost-effective condition monitoring systems (CMS) for the drive train are a great concern to the wind industry. This manuscript presents an approach for condition health monitoring and fault diagnosis in wind turbine gearboxes and generators by means of analysing the power dependant vibrations gathered. This methodology is based on the establishment of the normal operation boundaries for carrying out the identification of deviations related to a defect. The validity of the baseline is studied using q-factor and probability of detection (POD) concepts. Given the nonlinear and nonstationary nature of the faulty vibration signals, envelope analysis is proposed as a demodulation technique to be applied to the signals, prior to the frequency response being extracted. The methodology is validated by field trials in a WINDMASTER300 wind turbine. Baselines for the generator and gearbox were produced as a tool to detect future faults developed within the turbine. Envelope analysis makes the identification of the vibrational frequencies representative of failure very likely.

Suggested Citation

  • Romero, Antonio & Soua, Slim & Gan, Tat-Hean & Wang, Bin, 2018. "Condition monitoring of a wind turbine drive train based on its power dependant vibrations," Renewable Energy, Elsevier, vol. 123(C), pages 817-827.
    • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:817-827
    DOI: 10.1016/j.renene.2017.07.086
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    6. Liu, Hongwei & Zhang, Pengpeng & Gu, Yajing & Shu, Yongdong & Song, Jiajun & Lin, Yonggang & Li, Wei, 2022. "Dynamics analysis of the power train of 650 kW horizontal-axis tidal current turbine," Renewable Energy, Elsevier, vol. 194(C), pages 51-67.
    7. Koukoura, Sofia & Scheu, Matti Niclas & Kolios, Athanasios, 2021. "Influence of extended potential-to-functional failure intervals through condition monitoring systems on offshore wind turbine availability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    8. García Márquez, Fausto Pedro & Peco Chacón, Ana María, 2020. "A review of non-destructive testing on wind turbines blades," Renewable Energy, Elsevier, vol. 161(C), pages 998-1010.
    9. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
    10. Xin, Ge & Hamzaoui, Nacer & Antoni, Jérôme, 2020. "Extraction of second-order cyclostationary sources by matching instantaneous power spectrum with stochastic model – application to wind turbine gearbox," Renewable Energy, Elsevier, vol. 147(P1), pages 1739-1758.
    11. Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.

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