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An Intelligent Sensor for the Ultra-High-Frequency Partial Discharge Online Monitoring of Power Transformers

Author

Listed:
  • Jian Li

    (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Xudong Li

    (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Lin Du

    (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Min Cao

    (Yunnan Electric Power Research Institute, Kunming 650217, China)

  • Guochao Qian

    (Yunnan Electric Power Research Institute, Kunming 650217, China)

Abstract

Ultra-high-frequency (UHF) partial discharge (PD) online monitoring is an effective way to inspect potential faults and insulation defects in power transformers. The construction of UHF PD online monitoring system is a challenge because of the high-frequency and wide-frequency band of the UHF PD signal. This paper presents a novel, intelligent sensor for UHF PD online monitoring based on a new method, namely a level scanning method. The intelligent sensor can directly acquire the statistical characteristic quantities and is characterized by low cost, few data to output and transmit, Ethernet functionality, and small size for easy installation. The prototype of an intelligent sensor was made. Actual UHF PD experiments with three typical artificial defect models of power transformers were carried out in a laboratory, and the waveform recording method and intelligent sensor proposed were simultaneously used for UHF PD measurement for comparison. The results show that the proposed intelligent sensor is qualified for the UHF PD online monitoring of power transformers. Additionally, three methods to improve the performance of intelligent sensors were proposed according to the principle of the level scanning method.

Suggested Citation

  • Jian Li & Xudong Li & Lin Du & Min Cao & Guochao Qian, 2016. "An Intelligent Sensor for the Ultra-High-Frequency Partial Discharge Online Monitoring of Power Transformers," Energies, MDPI, vol. 9(5), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:5:p:383-:d:70381
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    References listed on IDEAS

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    1. Ju Tang & Jiabin Zhou & Xiaoxing Zhang & Fan Liu, 2012. "A Transformer Partial Discharge Measurement System Based on Fluorescent Fiber," Energies, MDPI, vol. 5(5), pages 1-13, May.
    2. Weigen Chen & Xi Chen & Shangyi Peng & Jian Li, 2012. "Canonical Correlation Between Partial Discharges and Gas Formation in Transformer Oil Paper Insulation," Energies, MDPI, vol. 5(4), pages 1-17, April.
    3. Wenxia Sima & Chilong Jiang & Paul Lewin & Qing Yang & Tao Yuan, 2013. "Modeling of the Partial Discharge Process in a Liquid Dielectric: Effect of Applied Voltage, Gap Distance, and Electrode Type," Energies, MDPI, vol. 6(2), pages 1-19, February.
    4. Tianyan Jiang & Jian Li & Yuanbing Zheng & Caixin Sun, 2011. "Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges," Energies, MDPI, vol. 4(7), pages 1-15, July.
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    Cited by:

    1. Xizheng Guo & Jiaqi Yuan & Yiguo Tang & Xiaojie You, 2018. "Hardware in the Loop Real-time Simulation for the Associated Discrete Circuit Modeling Optimization Method of Power Converters," Energies, MDPI, vol. 11(11), pages 1-14, November.
    2. Martin Siegel & Sebastian Coenen & Michael Beltle & Stefan Tenbohlen & Marc Weber & Pascal Fehlmann & Stefan M. Hoek & Ulrich Kempf & Robert Schwarz & Thomas Linn & Jitka Fuhr, 2019. "Calibration Proposal for UHF Partial Discharge Measurements at Power Transformers," Energies, MDPI, vol. 12(16), pages 1-17, August.
    3. 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.
    4. Wojciech Sikorski, 2018. "Active Dielectric Window: A New Concept of Combined Acoustic Emission and Electromagnetic Partial Discharge Detector for Power Transformers," Energies, MDPI, vol. 12(1), pages 1-27, December.
    5. Daria Wotzka & Wojciech Sikorski & Cyprian Szymczak, 2022. "Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning," Energies, MDPI, vol. 15(9), pages 1-20, April.
    6. Issouf Fofana & Yazid Hadjadj, 2018. "Power Transformer Diagnostics, Monitoring and Design Features," Energies, MDPI, vol. 11(12), pages 1-5, November.
    7. Sinda Kaziz & Mohamed Hadj Said & Antonino Imburgia & Bilel Maamer & Denis Flandre & Pietro Romano & Fares Tounsi, 2023. "Radiometric Partial Discharge Detection: A Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    8. Janvier Sylvestre N’cho & Issouf Fofana, 2020. "Review of Fiber Optic Diagnostic Techniques for Power Transformers," Energies, MDPI, vol. 13(7), pages 1-24, April.

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