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Canonical Correlation Between Partial Discharges and Gas Formation in Transformer Oil Paper Insulation

Author

Listed:
  • Weigen Chen

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

  • Xi Chen

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

  • Shangyi Peng

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

  • Jian Li

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

Abstract

Dissolved gas analysis (DGA) has been widely applied to diagnose internal faults in transformer insulation systems. However, the accuracy of DGA technique is limited because of the lack of positive correlation of the fault-identifying gases with faults found in power transformers. This paper presents a laboratory study on the correlation between oil dissolved gas formation and partial discharge (PD) statistical parameters. Canonical correlation analysis (CCA) is employed to explore the underlying correlation and to extract principal feature parameters and gases in the development of different PD defects. This study is aimed to provide more information in assisting the separation, classification and identification of PD defects, which might improve the existing transformer dissolved gas analysis (DGA) schemes. An application of a novel ratio method for discharge diagnosis is proposed. The evaluation of DGA data both in laboratory and actual transformers proves the effectiveness of the method and the correlation investigation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:4:p:1081-1097:d:17275
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    Citations

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    Cited by:

    1. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, January.
    2. 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.
    3. Fabio Henrique Pereira & Francisco Elânio Bezerra & Shigueru Junior & Josemir Santos & Ivan Chabu & Gilberto Francisco Martha de Souza & Fábio Micerino & Silvio Ikuyo Nabeta, 2018. "Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations," Energies, MDPI, vol. 11(7), pages 1-12, June.
    4. Chenmeng Xiang & Quan Zhou & Jian Li & Qingdan Huang & Haoyong Song & Zhaotao Zhang, 2016. "Comparison of Dissolved Gases in Mineral and Vegetable Insulating Oils under Typical Electrical and Thermal Faults," Energies, MDPI, vol. 9(5), pages 1-22, April.
    5. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
    6. Alper Aydogan & Fatih Atalar & Aysel Ersoy Yilmaz & Pawel Rozga, 2020. "Using the Method of Harmonic Distortion Analysis in Partial Discharge Assessment in Mineral Oil in a Non-Uniform Electric Field," Energies, MDPI, vol. 13(18), pages 1-18, September.
    7. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.
    8. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine," Energies, MDPI, vol. 12(5), pages 1-18, March.
    9. Shuaibing Li & Guoqiang Gao & Guangcai Hu & Bo Gao & Haojie Yin & Wenfu Wei & Guangning Wu, 2017. "Influences of Traction Load Shock on Artificial Partial Discharge Faults within Traction Transformer—Experimental Test for Pattern Recognition," Energies, MDPI, vol. 10(10), pages 1-17, October.
    10. Xiaojun Tang & Wenjing Wang & Xuliang Zhang & Erzhen Wang & Xuanjiannan Li, 2018. "On-Line Analysis of Oil-Dissolved Gas in Power Transformers Using Fourier Transform Infrared Spectrometry," Energies, MDPI, vol. 11(11), pages 1-15, November.

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