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Localization of HV Insulation Defects Using a System of Associated Capacitive Sensors

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  • Krzysztof Walczak

    (Institute of Electric Power Engineering, Poznan University of Technology, 60-965 Poznan, Poland)

Abstract

The issue of detecting and locating defects generating partial discharges (PDs) is very important for the proper functioning of power grids. Despite the existence of many localization methods, both very large and relatively small objects are still a challenge due to the problem of obtaining the required measurement accuracy. This article presents the idea of the method of PD localization in small objects of simple structure with the use of a system of four capacitive probes. Based on the relative difference in the amplitudes of the signals recorded by the pair of capacitive sensors and considering their distance characteristics, it is possible to determine the place where the PD pulses are generated. In the example of measurements made on a support insulator, it was shown that the location of a defect using the proposed method allows for an indication accuracy of up to 0.5 cm.

Suggested Citation

  • Krzysztof Walczak, 2023. "Localization of HV Insulation Defects Using a System of Associated Capacitive Sensors," Energies, MDPI, vol. 16(5), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2297-:d:1082285
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    References listed on IDEAS

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    1. Wojciech Sikorski & Krzysztof Walczak & Wieslaw Gil & Cyprian Szymczak, 2020. "On-Line Partial Discharge Monitoring System for Power Transformers Based on the Simultaneous Detection of High Frequency, Ultra-High Frequency, and Acoustic Emission Signals," Energies, MDPI, vol. 13(12), pages 1-37, June.
    2. Hamidreza Karami & Farzane Askari & Farhad Rachidi & Marcos Rubinstein & Wojciech Sikorski, 2022. "An Inverse-Filter-Based Method to Locate Partial Discharge Sources in Power Transformers," Energies, MDPI, vol. 15(6), pages 1-21, March.
    3. 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.
    4. Marek Florkowski, 2020. "Classification of Partial Discharge Images Using Deep Convolutional Neural Networks," Energies, MDPI, vol. 13(20), pages 1-17, October.
    5. Krzysztof Walczak & Wojciech Sikorski, 2021. "Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System," Energies, MDPI, vol. 14(18), pages 1-20, September.
    6. Zbigniew Nadolny, 2022. "Determination of Dielectric Losses in a Power Transformer," Energies, MDPI, vol. 15(3), pages 1-14, January.
    7. Zbigniew Nadolny, 2022. "Electric Field Distribution and Dielectric Losses in XLPE Insulation and Semiconductor Screens of High-Voltage Cables," Energies, MDPI, vol. 15(13), pages 1-14, June.
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    Cited by:

    1. Zbigniew Nadolny, 2023. "Design and Optimization of Power Transformer Diagnostics," Energies, MDPI, vol. 16(18), pages 1-7, September.

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