IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i16p5819-d885236.html
   My bibliography  Save this article

Partial Discharge (PD) Signal Detection and Isolation on High Voltage Equipment Using Improved Complete EEMD Method

Author

Listed:
  • Vu Cong Thuc

    (Transdisciplinary Science and Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima 739-8529, Japan
    Hanoi Electrical Testing Company, Cau Giay, Ha Noi 100000, Vietnam)

  • Han Soo Lee

    (Transdisciplinary Science and Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima 739-8529, Japan
    Center for the Planetary Health and Innovation Science (PHIS), The IDEC Institute, Hiroshima University)

Abstract

Electricity has a crucial function in contemporary civilization. The power grid must be stable to ensure the efficiency and dependability of electrical equipment. This implies that the high-voltage equipment at the substation must be reliably operated. As a result, the appropriate and dependable use of systems to monitor the operating status of high-voltage electrical equipment has recently gained attention. Partial discharge (PD) analysis is one of the most promising solutions for monitoring and diagnosing potential problems in insulation systems. Noise is a major challenge in diagnosing and detecting defects when using this measurement. This study aims to denoise PD signals using a data decomposition method, improved complete ensemble empirical mode decomposition with adaptive noise algorithm, combined with statistical significance test to increase noise reduction efficiency and to derive and visualize the Hilbert spectrum of the input signal in time-frequency domain after filtering the noise. In the PD signal analysis, both artificial and experimental signals were used as input signals in the decomposition method. For these signals, this study has yielded significant improvement in the denoising and the PD detecting process indicated by statistical measures. Thus, the signal decomposition by using the proposed method is proven to be a useful tool for diagnosing the PD on high voltage equipment.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5819-:d:885236
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/16/5819/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/16/5819/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammed A. Shams & Hussein I. Anis & Mohammed El-Shahat, 2021. "Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform," Energies, MDPI, vol. 14(20), pages 1-22, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wenchen Chen & Yingdong Liu & Yayu Gao & Jingzhu Hu & Zhenghai Liao & Jun Zhao, 2024. "Intelligent Substation Noise Monitoring System: Design, Implementation and Evaluation," Energies, MDPI, vol. 17(13), pages 1-24, June.
    2. 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.
    3. Guo Wang & Yibin Wang & Yongzhi Min & Wu Lei, 2022. "Blind Source Separation of Transformer Acoustic Signal Based on Sparse Component Analysis," Energies, MDPI, vol. 15(16), pages 1-15, August.

    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:15:y:2022:i:16:p:5819-:d:885236. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.