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Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS

Author

Listed:
  • Ruixu Zhou

    (State Key Laboratory of Power System and Generation Equipment, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Wensheng Gao

    (State Key Laboratory of Power System and Generation Equipment, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Weidong Liu

    (State Key Laboratory of Power System and Generation Equipment, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Dengwei Ding

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China)

  • Bowen Zhang

    (China Electric Power Research Institute, Beijing 100192, China)

Abstract

Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy of fault diagnosis in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers. This newly proposed method not only solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC condition but also immensely facilitates the fault diagnosis of HVDC GIS.

Suggested Citation

  • Ruixu Zhou & Wensheng Gao & Weidong Liu & Dengwei Ding & Bowen Zhang, 2021. "Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS," Energies, MDPI, vol. 14(22), pages 1-27, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7674-:d:680609
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    References listed on IDEAS

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