IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1556-d114486.html
   My bibliography  Save this article

Influences of Traction Load Shock on Artificial Partial Discharge Faults within Traction Transformer—Experimental Test for Pattern Recognition

Author

Listed:
  • Shuaibing Li

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Guoqiang Gao

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Guangcai Hu

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Bo Gao

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Haojie Yin

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Wenfu Wei

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Guangning Wu

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

Abstract

Partial discharge (PD) measurement and its pattern recognition are vital to fault diagnosis of transformers, especially to those traction substation transformers undergoing repetitive traction load shocks. This paper presents the primary factors induced by traction load shocks including high total harmonics distortion (THD), transient voltage impulse and high-temperature rise, and their effects on the feature parameters of PD. Experimental tests are conducted on six artificial PD models with these factors introduced one by one. Results reveal that the maximum PD quantity and the PD repetitive rate are favorable to be enlarged when the oil temperature exceeds 80 °C or the THD is higher than 16% with certain orders of harmonic. The decline in PD inception voltage can mainly be attributed to the transient voltage impulse. The variation in central frequency of the fast Fourier transformation (FFT) spectra transformed from ultra-high frequency signals can mainly be attributed to high THD, especially when it exceeds 20%. The temperature rise has no significant influence on the FFT spectra; the transient voltage impulse, however, can result in a central frequency shift of the floating particle discharge. With the rapid development of high-speed railways, the study presented in this paper will be helpful for field PD detection and recognition of traction substation transformers in the future.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1556-:d:114486
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1556/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/10/1556/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marek Florkowski, 2018. "Observations of Partial Discharge Echo in Dielectric Void by Applying a Voltage Chopped Sequence," Energies, MDPI, vol. 11(10), pages 1-15, September.
    2. Dante Ruiz-Robles & Vicente Venegas-Rebollar & Adolfo Anaya-Ruiz & Edgar L. Moreno-Goytia & Juan R. Rodríguez-Rodríguez, 2018. "Design and Prototyping Medium-Frequency Transformers Featuring a Nanocrystalline Core for DC–DC Converters," Energies, MDPI, vol. 11(8), pages 1-17, August.
    3. Jun Jiang & Mingxin Zhao & Chaohai Zhang & Min Chen & Haojun Liu & Ricardo Albarracín, 2018. "Partial Discharge Analysis in High-Frequency Transformer Based on High-Frequency Current Transducer," Energies, MDPI, vol. 11(8), pages 1-13, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    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. 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.
    4. Ming Ren & Ming Dong & Jialin Liu, 2016. "Statistical Analysis of Partial Discharges in SF 6 Gas via Optical Detection in Various Spectral Ranges," Energies, MDPI, vol. 9(3), pages 1-15, March.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1556-:d:114486. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.