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An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

Author

Listed:
  • Kai Zhou

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Mingzhi Li

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Yuan Li

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Min Xie

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Yonglu Huang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

Abstract

To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a denoising method for PD signals, named adaptive short-time singular value decomposition (ASTSVD). First, a sliding window was moved along the time axis of a PD signal to cut a whole signal into segments with overlaps. The singular value decomposition (SVD) method was then applied to each segment to obtain its singular value sequence. The minimum description length (MDL) criterion was used to determine the number of effective singular values automatically. Then, the selected singular values of each signal segment were used to reconstruct the noise-free signal segment, from which the denoised PD signal was obtained. To evaluate ASTSVD, we applied ASTSVD and two other methods on simulated, laboratory-measured, and field-detected noisy PD signals, respectively. Compared to the other two methods, the denoised PD signals of ASTSVD contain less residual noise and exhibit smaller waveform distortion.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3465-:d:265335
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    Citations

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    Cited by:

    1. Xianjie Rao & Kai Zhou & Yuan Li & Guangya Zhu & Pengfei Meng, 2020. "A New Cross-Correlation Algorithm Based on Distance for Improving Localization Accuracy of Partial Discharge in Cables Lines," Energies, MDPI, vol. 13(17), pages 1-13, September.
    2. 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.
    3. Linao Li & Xinlao Wei, 2022. "Power Interference Suppression Method for Measuring Partial Discharges under Pulse Square Voltage Conditions," Energies, MDPI, vol. 15(9), pages 1-15, May.
    4. Xing Zhang & Chongchong Zhang & Zhuoqun Wei, 2019. "Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors," Energies, MDPI, vol. 12(22), pages 1-23, November.

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