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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
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    References listed on IDEAS

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    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.
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    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.

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