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Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

Author

Listed:
  • Kang Sun

    (School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China)

  • Jing Zhang

    (School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China)

  • Wenwen Shi

    (China Aluminum Logistics Group Zhongzhou Co., Ltd., Xinxiang 453000, China)

  • Jingdie Guo

    (State Grid Jiyuan power supply company, Jiyuan 459000, China)

Abstract

While both periodic narrowband noise and white noise are significant sources of interference in the detection and localization of partial discharge (PD) signals in power cables, existing research has focused nearly exclusively on white noise suppression. This paper addresses this issue by proposing a new signal extraction method for effectively detecting random PD signals in power cables subject to complex noise environments involving both white noise and periodic narrowband noise. Firstly, the power cable signal was decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the periodic narrowband noise and frequency aliasing in the obtained signal components were suppressed using singular value decomposition. Then, signal components contributing significantly to the PD signal were determined according to the cross-correlation coefficient between each component and the original PD signal, and the PD signal was reconstructed solely from the obtained significant components. Finally, the wavelet packet threshold method was used to filter out residual white noise in the reconstructed PD signal. The performance of the proposed algorithm was demonstrated by its application to synthesized PD signals with complex noise environments composed of both Gaussian white noise and periodic narrowband noise. In addition, the time-varying kurtosis method was demonstrated to accurately determine the PD signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB. When the SNR was reduced to −23 dB, the arrival time error of the PD signal was only one sampling point.

Suggested Citation

  • Kang Sun & Jing Zhang & Wenwen Shi & Jingdie Guo, 2019. "Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet," Energies, MDPI, vol. 12(17), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3242-:d:260071
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    References listed on IDEAS

    as
    1. Jun Jiang & Mingxin Zhao & Chaohai Zhang & Min Chen & Haojun Liu & Ricardo Albarracín, 2018. "Partial Discharge Analysis in High-Frequency Transformer Based on High-Frequency Current Transducer," Energies, MDPI, vol. 11(8), pages 1-13, August.
    2. Marek Florkowski & Barbara Florkowska & Pawel Zydron, 2019. "Partial Discharges in Insulating Systems of Low Voltage Electric Motors Fed by Power Electronics—Twisted-Pair Samples Evaluation," Energies, MDPI, vol. 12(5), pages 1-19, February.
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    Cited by:

    1. Hui Hwang Goh & Ling Liao & Dongdong Zhang & Wei Dai & Chee Shen Lim & Tonni Agustiono Kurniawan & Kai Chen Goh & Chin Leei Cham, 2022. "Denoising Transient Power Quality Disturbances Using an Improved Adaptive Wavelet Threshold Method Based on Energy Optimization," Energies, MDPI, vol. 15(9), pages 1-21, April.

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