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A New Discharge Pattern for the Characterization and Identification of Insulation Defects in GIS

Author

Listed:
  • Rui Yao

    (School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China)

  • Meng Hui

    (School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China)

  • Jun Li

    (State Grid Shaanxi Electric Power Research Institute, Xi’an 710049, China)

  • Lin Bai

    (School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China)

  • Qisheng Wu

    (School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China)

Abstract

Identification of insulation defects in gas insulated metal-enclosed switchgear (GIS) is important for partial discharge (PD) evaluation. This article proposes a polar coordinate pattern approach to characterize the different kinds of defect types. These defect types include floating electrodes, a fixed protrusion on the enclosure, surface contamination on the spacer, metallic prominence on the high voltage electrode, a void in the insulator, and free metal particles on the enclosure. First, the physical models for the insulation defects in the established GIS model are designed. Second, the phase resolved pulse sequence (PRPS) data sets are obtained using ultra-high frequency (UHF) measurement. Then, the polar coordinate patterns are proposed to characterize the defects. Nine discharge parameters combined with the parameters based on quadrant statistical theory constitute the input feature vector to identify the PD types. The experimental results show that these new parameters could produce a clear, quantitative description of the characteristics of the defect types and could be used to distinguish between the different kinds of defect types.

Suggested Citation

  • Rui Yao & Meng Hui & Jun Li & Lin Bai & Qisheng Wu, 2018. "A New Discharge Pattern for the Characterization and Identification of Insulation Defects in GIS," Energies, MDPI, vol. 11(4), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:971-:d:141761
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    Citations

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    Cited by:

    1. Yanxin Wang & Jing Yan & Zhou Yang & Tingliang Liu & Yiming Zhao & Junyi Li, 2019. "Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network," Energies, MDPI, vol. 12(24), pages 1-19, December.
    2. Junping Zhao & Zhengjie An & Bin Lv & Zhicheng Wu & Qiaogen Zhang, 2020. "Characteristics of the Partial Discharge in the Development of Conductive Particle-Initiated Flashover of a GIS Insulator," Energies, MDPI, vol. 13(10), pages 1-11, May.

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