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Partial Discharge Pattern Recognition Based on 3D Graphs of Phase Resolved Pulse Sequence

Author

Listed:
  • Simeng Song

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yong Qian

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Hui Wang

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yiming Zang

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Gehao Sheng

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Xiuchen Jiang

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Partial discharge (PD) is an important phenomenon that reflects the insulation condition of electrical equipment. In order to protect the safety of power grids, it is of significance to diagnose the type of insulation defects inside the equipment accurately and early through PD pattern recognition. In this article, phase resolved pulse sequence (PRPS) graphs in 3D were constructed by the PD pulse data of the gas-insulated switchgear (GIS) acquired, then the histogram of oriented gradient (HOG) features were extracted directly from the 3D PRPS graphs, and finally the attribute selective Naïve Bayes classifier was used to recognize the discharge pattern. In addition, this method was compared with two traditional methods, i.e., the statistical method and the grayscale gradient co-occurrence matrix method, from three aspects. The result shows that 3D PRPS graphs have different morphology characteristics in vision under different defects, and the similarity among different voltages applied is higher than among different defects, so it is reasonable to use them as the basis for PD pattern recognition. The contrast indicates that the HOG method not only has the highest accuracy with the least requirement for pretreatment and training, but it also has robustness when the voltage applied changes. Consequently, this method has the universality for PD pattern recognition that is based on 3D PRPS graphs.

Suggested Citation

  • Simeng Song & Yong Qian & Hui Wang & Yiming Zang & Gehao Sheng & Xiuchen Jiang, 2020. "Partial Discharge Pattern Recognition Based on 3D Graphs of Phase Resolved Pulse Sequence," Energies, MDPI, vol. 13(16), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4103-:d:396127
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    Citations

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    Cited by:

    1. Jianfeng Zheng & Zhichao Chen & Qun Wang & Hao Qiang & Weiyue Xu, 2022. "GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network," Energies, MDPI, vol. 15(19), pages 1-14, October.
    2. Marek Florkowski, 2020. "Classification of Partial Discharge Images Using Deep Convolutional Neural Networks," Energies, MDPI, vol. 13(20), pages 1-17, October.
    3. Marek Florkowski, 2021. "Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns," Energies, MDPI, vol. 14(13), pages 1-18, June.

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