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Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform

Author

Listed:
  • Mohammed A. Shams

    (Electrical Power Department, Faculty of Engineering, Cairo Univrsity, Giza 12613, Egypt)

  • Hussein I. Anis

    (Electrical Power Department, Faculty of Engineering, Cairo Univrsity, Giza 12613, Egypt)

  • Mohammed El-Shahat

    (Electrical Power Department, Faculty of Engineering, Cairo Univrsity, Giza 12613, Egypt)

Abstract

Online detection of partial discharges (PD) is imperative for condition monitoring of high voltage equipment as well as power cables. However, heavily contaminated sites often burden the signals with various types of noise that can be challenging to remove (denoise). This paper proposes an algorithm based on the maximal overlap discrete wavelet transform (MODWT) to denoise PD signals originating from defects in power cables contaminated with various levels of noises. The three most common noise types, namely, Gaussian white noise (GWN), discrete spectral interference (DSI), and stochastic pulse shaped interference (SPI) are considered. The algorithm is applied to an experimentally acquired void-produced partial discharge in a power cable. The MODWT-based algorithm achieved a good improvement in the signal-to-noise ratio (SNR) and in the normalized correlation coefficient (NCC) for the three types of noises. The MODWT-based algorithm performance was also compared to that of the empirical Bayesian wavelet transform (EBWT) algorithm, in which the former showed superior results in denoising SPI and DSI, as well as comparable results in denoising GWN. Finally, the algorithm performance was tested on a PD signal contaminated with the three type of noises simultaneously in which the results were also superior.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6540-:d:654118
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    References listed on IDEAS

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    1. Amir Abbas Soltani & Ayman El-Hag, 2019. "Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network," Energies, MDPI, vol. 12(18), pages 1-14, September.
    2. Abdullahi Abubakar Mas’ud & Ricardo Albarracín & Jorge Alfredo Ardila-Rey & Firdaus Muhammad-Sukki & Hazlee Azil Illias & Nurul Aini Bani & Abu Bakar Munir, 2016. "Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions," Energies, MDPI, vol. 9(8), pages 1-18, July.
    3. Mohanad S. Al-Musaylh & Ravinesh C. Deo & Yan Li, 2020. "Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms," Energies, MDPI, vol. 13(9), pages 1-19, May.
    4. Josué M. Polanco-Martínez & Luis M. Abadie, 2016. "Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach," Energies, MDPI, vol. 9(12), pages 1-19, December.
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    Cited by:

    1. 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.
    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. 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.

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