IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i18p3465-d265335.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/18/3465/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/18/3465/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3465-:d:265335. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.