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Partial Discharge Diagnostics: Data Cleaning and Feature Extraction

Author

Listed:
  • Donny Soh

    (Infocomm Technology Cluster, Singapore Institute of Technology (SIT), 10 Dover Drive, Singapore 138683, Singapore)

  • Sivaneasan Bala Krishnan

    (Engineering Cluster, Singapore Institute of Technology (SIT), 10 Dover Drive, Singapore 138683, Singapore)

  • Jacob Abraham

    (Infocomm Technology Cluster, Singapore Institute of Technology (SIT), 10 Dover Drive, Singapore 138683, Singapore)

  • Lai Kai Xian

    (SP Group, 2 Kallang Sector, Singapore 349277, Singapore)

  • Tseng King Jet

    (Engineering Cluster, Singapore Institute of Technology (SIT), 10 Dover Drive, Singapore 138683, Singapore)

  • Jimmy Fu Yongyi

    (SP Group, 2 Kallang Sector, Singapore 349277, Singapore)

Abstract

Detection of partial discharge (PD) in switchgears requires extensive data collection and time-consuming analyses. Data from real live operational environments pose great challenges in the development of robust and efficient detection algorithms due to overlapping PDs and the strong presence of random white noise. This paper presents a novel approach using clustering for data cleaning and feature extraction of phase-resolved partial discharge (PRPD) plots derived from live operational data. A total of 452 PRPD 2D plots collected from distribution substations over a six-month period were used to test the proposed technique. The output of the clustering technique is evaluated on different types of machine learning classification techniques and the accuracy is compared using balanced accuracy score. The proposed technique extends the measurement abilities of a portable PD measurement tool for diagnostics of switchgear condition, helping utilities to quickly detect potential PD activities with minimal human manual analysis and higher accuracy.

Suggested Citation

  • Donny Soh & Sivaneasan Bala Krishnan & Jacob Abraham & Lai Kai Xian & Tseng King Jet & Jimmy Fu Yongyi, 2022. "Partial Discharge Diagnostics: Data Cleaning and Feature Extraction," Energies, MDPI, vol. 15(2), pages 1-12, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:508-:d:722477
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    References listed on IDEAS

    as
    1. Yuanlin Luo & Zhaohui Li & Hong Wang, 2017. "A Review of Online Partial Discharge Measurement of Large Generators," Energies, MDPI, vol. 10(11), pages 1-32, October.
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