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A Novel Denoising Method for Partial Discharge Signal Based on Improved Variational Mode Decomposition

Author

Listed:
  • Jingjie Yang

    (School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Ke Yan

    (Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China)

  • Zhuo Wang

    (School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Xiang Zheng

    (School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, China)

Abstract

Partial discharge (PD) online monitoring is a common technique for high-voltage equipment diagnosis. However, due to field interference, the monitored PD signal contains a lot of noise. Therefore, this paper proposes a novel method by integrating the flower pollination algorithm, variational mode decomposition, and Savitzky–Golay filter (FPA-VMD-SG) to effectively suppress white noise and narrowband noise in the PD signal. Firstly, based on the mean envelope entropy (MEE), the decomposition number and quadratic penalty term of the VMD were optimized by the FPA. The PD signal containing noise was broken down into intrinsic mode functions (IMFs) by optimized parameters. Secondly, the IMFs were classified as the signal component, the noise dominant component, and the noise component according to the kurtosis value. Thirdly, the noise dominant component was denoised using the SG filter, and the denoised signal was mixed with the signal component to reconstruct a new signal. Finally, threshold denoising was used to eliminate residual white noise. To verify the performance of the FPA-VMD-SG method, compared with empirical mode decomposition with wavelet transform (EMD-WT) and adaptive singular value decomposition (ASVD), the denoising results of simulated and real PD signals indicated that the FPA-VMD-SG method had excellent performance.

Suggested Citation

  • Jingjie Yang & Ke Yan & Zhuo Wang & Xiang Zheng, 2022. "A Novel Denoising Method for Partial Discharge Signal Based on Improved Variational Mode Decomposition," Energies, MDPI, vol. 15(21), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8167-:d:960706
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    References listed on IDEAS

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    1. Vu Cong Thuc & Han Soo Lee, 2022. "Partial Discharge (PD) Signal Detection and Isolation on High Voltage Equipment Using Improved Complete EEMD Method," Energies, MDPI, vol. 15(16), pages 1-17, August.
    2. Kai Zhou & Mingzhi Li & Yuan Li & Min Xie & Yonglu Huang, 2019. "An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition," Energies, MDPI, vol. 12(18), pages 1-16, September.
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    Cited by:

    1. Songyuan Li & Pengxian Song & Zhanpeng Wei & Xu Li & Qinghua Tang & Zhengzheng Meng & Ji Li & Songtao Liu & Yuhuai Wang & Jin Li, 2022. "Partial Discharge Detection and Defect Location Method in GIS Cable Terminal," Energies, MDPI, vol. 16(1), pages 1-10, December.
    2. Mingsan Ouyang & Peicheng Shen, 2022. "Prediction of Remaining Useful Life of Lithium Batteries Based on WOA-VMD and LSTM," Energies, MDPI, vol. 15(23), pages 1-20, November.
    3. Hairui Wang & Xin Ye & Yuanbo Li & Guifu Zhu, 2023. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series," Sustainability, MDPI, vol. 15(12), pages 1-23, June.

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