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A new wind turbine fault diagnosis method based on the local mean decomposition

Author

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  • Liu, W.Y.
  • Zhang, W.H.
  • Han, J.G.
  • Wang, G.F.

Abstract

This paper proposed a novel wind turbine fault diagnosis method based on the local mean decomposition (LMD) technology. Wind energy is a renewable power source that produces no atmospheric pollution. The condition monitoring and fault diagnosis in wind turbine system are important in avoiding serious damage. Vibration analysis is a normal and useful technology in wind turbine condition monitoring and fault diagnosis. However, the relatively slow speed of the wind turbine components set a limitation in early fault diagnosis using vibration monitoring method. The traditional time-frequency analysis techniques have some drawbacks which make them not suitable for the nonlinear, non-Gaussian signal analysis. LMD is a new iterative approach to demodulate amplitude and frequency modulated signals, which is suitable for obtaining instantaneous frequencies in wind turbine condition monitoring and fault diagnosis. The experiment analysis of the wind turbine vibration signal proves the validity and availability of the new method.

Suggested Citation

  • Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
    • Handle: RePEc:eee:renene:v:48:y:2012:i:c:p:411-415
    DOI: 10.1016/j.renene.2012.05.018
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    1. Amirat, Y. & Benbouzid, M.E.H. & Al-Ahmar, E. & Bensaker, B. & Turri, S., 2009. "A brief status on condition monitoring and fault diagnosis in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2629-2636, December.
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    Cited by:

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    2. Ukashatu Abubakar & Saad Mekhilef & Hazlie Mokhlis & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski & Hussain Bassi & Muhyaddin Jamal Hosin Rawa, 2018. "Transient Faults in Wind Energy Conversion Systems: Analysis, Modelling Methodologies and Remedies," Energies, MDPI, vol. 11(9), pages 1-33, August.
    3. Xueli An & Dongxiang Jiang, 2014. "Bearing fault diagnosis of wind turbine based on intrinsic time-scale decomposition frequency spectrum," Journal of Risk and Reliability, , vol. 228(6), pages 558-566, December.
    4. Liu, Dongdong & Cui, Lingli & Cheng, Weidong, 2023. "Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation," Renewable Energy, Elsevier, vol. 206(C), pages 645-657.
    5. 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.
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    7. Guowei Cai & Lixin Wang & Deyou Yang & Zhenglong Sun & Bo Wang, 2019. "Harmonic Detection for Power Grids Using Adaptive Variational Mode Decomposition," Energies, MDPI, vol. 12(2), pages 1-16, January.
    8. Hu, Aijun & Yan, Xiaoan & Xiang, Ling, 2015. "A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension," Renewable Energy, Elsevier, vol. 83(C), pages 767-778.
    9. Liu, Wenyi, 2016. "Design and kinetic analysis of wind turbine blade-hub-tower coupled system," Renewable Energy, Elsevier, vol. 94(C), pages 547-557.
    10. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    11. Guoyuan Ma & Xiaofeng Yue & Juan Zhu & Zeyuan Liu & Shibo Lu, 2023. "Deep Learning Network Based on Improved Sparrow Search Algorithm Optimization for Rolling Bearing Fault Diagnosis," Mathematics, MDPI, vol. 11(22), pages 1-20, November.
    12. Melo Junior, Francisco Erivan de Abreu & de Moura, Elineudo Pinho & Costa Rocha, Paulo Alexandre & de Andrade, Carla Freitas, 2019. "Unbalance evaluation of a scaled wind turbine under different rotational regimes via detrended fluctuation analysis of vibration signals combined with pattern recognition techniques," Energy, Elsevier, vol. 171(C), pages 556-565.
    13. Liu, W.Y., 2017. "A review on wind turbine noise mechanism and de-noising techniques," Renewable Energy, Elsevier, vol. 108(C), pages 311-320.
    14. Zhang, Yu & Lu, Wenxiu & Chu, Fulei, 2017. "Planet gear fault localization for wind turbine gearbox using acoustic emission signals," Renewable Energy, Elsevier, vol. 109(C), pages 449-460.
    15. Colak, Ilhami & Fulli, Gianluca & Bayhan, Sertac & Chondrogiannis, Stamatios & Demirbas, Sevki, 2015. "Critical aspects of wind energy systems in smart grid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 155-171.
    16. Fan Zhang & Juchuan Dai & Deshun Liu & Linxing Li & Xin Long, 2019. "Investigation of the Pitch Load of Large-Scale Wind Turbines Using Field SCADA Data," Energies, MDPI, vol. 12(3), pages 1-20, February.
    17. Sun, Kang & Xu, Zifei & Li, Shujun & Jin, Jiangtao & Wang, Peilin & Yue, Minnan & Li, Chun, 2023. "Dynamic response analysis of floating wind turbine platform in local fatigue of mooring," Renewable Energy, Elsevier, vol. 204(C), pages 733-749.
    18. Lida Liao & Bin Huang & Qi Tan & Kan Huang & Mei Ma & Kang Zhang, 2020. "Development of an Improved LMD Method for the Low-Frequency Elements Extraction from Turbine Noise Background," Energies, MDPI, vol. 13(4), pages 1-17, February.

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