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Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD

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  • Jiang, Yonghua
  • Tang, Baoping
  • Qin, Yi
  • Liu, Wenyi

Abstract

Analyzing the vibration signals of wind turbine usually requires feature extraction. However, in many cases, to extract feature components becomes challenging and the applicability of information drops down due to the large amount of noise. In this paper, a new denoising method based on adaptive Morlet wavelet and singular value decomposition (SVD) is applied to feature extraction for wind turbine vibration signals. Modified Shannon wavelet entropy is utilized to optimize central frequency and bandwidth parameter of the Morlet wavelet so as to achieve optimal match with the impulsive components. The time-frequency resolution can be adapted to different signals of interest. Then, an improved matrix construction method is used to construct matrix of the wavelet coefficient, and the scale periodical exponential (SPE) spectrum is obtained by SVD for selecting the appropriate transform scale. Experimental analysis and application into signal denoising indicate that the proposed method has better denoising performance than other wavelet transforms. The results of the experimental analysis in rolling bearing and the application in planetary gearbox show that the proposed method is an effective approach to detecting the impulsive feature components hidden in vibration signals and performs well for wind turbine fault diagnosis.

Suggested Citation

  • Jiang, Yonghua & Tang, Baoping & Qin, Yi & Liu, Wenyi, 2011. "Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD," Renewable Energy, Elsevier, vol. 36(8), pages 2146-2153.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:8:p:2146-2153
    DOI: 10.1016/j.renene.2011.01.009
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    1. Liu, Wenyi & Tang, Baoping & Jiang, Yonghua, 2010. "Status and problems of wind turbine structural health monitoring techniques in China," Renewable Energy, Elsevier, vol. 35(7), pages 1414-1418.
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    2. Salcedo-Sanz, S. & Cornejo-Bueno, L. & Prieto, L. & Paredes, D. & García-Herrera, R., 2018. "Feature selection in machine learning prediction systems for renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 728-741.
    3. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    4. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    5. Haiping Li & Jianmin Zhao & Xianglong Ni & Xinghui Zhang, 2018. "Fault diagnosis for machinery based on feature extraction and general regression neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 1034-1046, October.
    6. 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.
    7. Chang, Tian-Pau & Liu, Feng-Jiao & Ko, Hong-Hsi & Huang, Ming-Chao, 2017. "Oscillation characteristic study of wind speed, global solar radiation and air temperature using wavelet analysis," Applied Energy, Elsevier, vol. 190(C), pages 650-657.
    8. 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.
    9. Lixiao Cao & Zheng Qian & Hamid Zareipour & David Wood & Ehsan Mollasalehi & Shuangshu Tian & Yan Pei, 2018. "Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions," Energies, MDPI, vol. 11(12), pages 1-20, November.
    10. He, Guolin & Ding, Kang & Li, Weihua & Jiao, Xintao, 2016. "A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique," Renewable Energy, Elsevier, vol. 87(P1), pages 364-375.
    11. Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    12. Feng, Zhipeng & Liang, Ming & Zhang, Yi & Hou, Shumin, 2012. "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation," Renewable Energy, Elsevier, vol. 47(C), pages 112-126.
    13. Khazaee, Meghdad & Derian, Pierre & Mouraud, Anthony, 2022. "A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods," Renewable Energy, Elsevier, vol. 199(C), pages 1568-1579.

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