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Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges

Author

Listed:
  • Tianyan Jiang

    (State Key Laboratory of Power Transmission Equipment& System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China)

  • Jian Li

    (State Key Laboratory of Power Transmission Equipment& System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China)

  • Yuanbing Zheng

    (State Key Laboratory of Power Transmission Equipment& System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China)

  • Caixin Sun

    (State Key Laboratory of Power Transmission Equipment& System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China)

Abstract

This paper presents an Improved Bagging Algorithm (IBA) to recognize ultra-high-frequency (UHF) signals of partial discharges (PDs). This approach establishes the sample information entropy for each sample and the re-sampling process of the traditional Bagging algorithm is optimized. Four typical discharge models were designed in the laboratory to simulate the internal insulation faults of power transformers. The optimized third order Peano fractal antenna was applied to capture the PD UHF signals. Multi-scale fractal dimensions as well as energy parameters extracted from the decomposed signals by wavelet packet transform were used as the characteristic parameters for pattern recognition. In order to verify the effectiveness of the proposed algorithm, the back propagation neural network (BPNN) and the support vector machine (SVM) based on the IBA were adopted in this paper to carry out the pattern recognition for PD UHF signals. Experimental results show that the proposed approach of IBA can effectively enhance the generalization capability and also improve the accuracy of the recognition for PD UHF signals.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:7:p:1087-1101:d:13262
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    Citations

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    Cited by:

    1. 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.
    2. Jian Li & Zhiman He & Youyuan Wang & Jinzhuang Lv & Linjie Zhao, 2012. "A Two-Dimensional Cloud Model for Condition Assessment of HVDC Converter Transformers," Energies, MDPI, vol. 5(1), pages 1-11, January.
    3. Al-geelani, Nasir A. & M. Piah, M. Afendi & Bashir, Nouruddeen, 2015. "A review on hybrid wavelet regrouping particle swarm optimization neural networks for characterization of partial discharge acoustic signals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 20-35.
    4. Gaoyang Li & Xiaohua Wang & Aijun Yang & Mingzhe Rong & Kang Yang, 2017. "Failure Prognosis of High Voltage Circuit Breakers with Temporal Latent Dirichlet Allocation," Energies, MDPI, vol. 10(11), pages 1-20, November.
    5. Stefan Tenbohlen & Chandra Prakash Beura & Wojciech Sikorski & Ricardo Albarracín Sánchez & Bruno Albuquerque de Castro & Michael Beltle & Pascal Fehlmann & Martin Judd & Falk Werner & Martin Siegel, 2023. "Frequency Range of UHF PD Measurements in Power Transformers," Energies, MDPI, vol. 16(3), pages 1-21, January.
    6. Tianhui Li & Xianhai Pang & Boyan Jia & Yanwei Xia & Siming Zeng & Hongliang Liu & Hao Tian & Fen Lin & Dan Wang, 2020. "Detection and Diagnosis of Defect in GIS Based on X-ray Digital Imaging Technology," Energies, MDPI, vol. 13(3), pages 1-18, February.
    7. Tianhui Li & Mingzhe Rong & Xiaohua Wang & Jin Pan, 2017. "Experimental Investigation on Propagation Characteristics of PD Radiated UHF Signal in Actual 252 kV GIS," Energies, MDPI, vol. 10(7), pages 1-12, July.

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