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Partial Discharge Analysis in High-Frequency Transformer Based on High-Frequency Current Transducer

Author

Listed:
  • Jun Jiang

    (Jiangsu Key Laboratory of New Energy Generation and Power Conversion, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Mingxin Zhao

    (Jiangsu Key Laboratory of New Energy Generation and Power Conversion, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Chaohai Zhang

    (Jiangsu Key Laboratory of New Energy Generation and Power Conversion, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Min Chen

    (State Grid Zhejiang Electric Power Co. Ltd., Research Institute, Hangzhou 310014, China)

  • Haojun Liu

    (State Grid Zhejiang Electric Power Co. Ltd., Research Institute, Hangzhou 310014, China)

  • Ricardo Albarracín

    (Departamento de Ingeniería Eléctrica, Electrónica, Automática y Física Aplicada, Escuela Técnica Superior de Ingeniería y Diseño Industrial, Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain)

Abstract

High-frequency transformers are the core components of power electronic transformers (PET), whose insulation is deeply threatened by high voltage (HV) and high frequency (HF). The partial discharge (PD) test is an effective method to assess an electrical insulation system. A PD measurement platform applying different frequencies was set up in this manuscript. PD signals were acquired with a high-frequency current transducer (HFCT). For improving the signal-to-noise ( SNR ) ratio of PD pulses, empirical mode decomposition (EMD) was used to increase the SNR by 4 dB. PD characteristic parameters such as partial discharge inception voltage (PDIV) and PD phase, number, and magnitude were all analyzed as frequency dependent. High frequency led to high PDIV and a smaller discharge phase region. PD number and magnitude were first up and then down as the frequency increased. As a result, a suitable frequency for evaluating the insulation of high-frequency transformers is proposed at 8 kHz according to this work.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1997-:d:161268
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    References listed on IDEAS

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    1. Qing Yang & Peiyu Su & Yong Chen, 2017. "Comparison of Impulse Wave and Sweep Frequency Response Analysis Methods for Diagnosis of Transformer Winding Faults," Energies, MDPI, vol. 10(4), pages 1-16, March.
    2. 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.
    3. Xiaoxing Zhang & Guozhi Zhang & Yalong Li & Jian Zhang & Rui Huang, 2017. "On the Feasibility of Gap Detection of Power Transformer Partial Discharge UHF Signals: Gap Propagation Characteristics of Electromagnetic Waves," Energies, MDPI, vol. 10(10), pages 1-16, October.
    4. Radu Godina & Eduardo M. G. Rodrigues & João C. O. Matias & João P. S. Catalão, 2015. "Effect of Loads and Other Key Factors on Oil-Transformer Ageing: Sustainability Benefits and Challenges," Energies, MDPI, vol. 8(10), pages 1-40, October.
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    Cited by:

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    2. 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.
    3. Kang Sun & Jing Zhang & Wenwen Shi & Jingdie Guo, 2019. "Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet," Energies, MDPI, vol. 12(17), pages 1-17, August.
    4. Benhui Lai & Shichang Yang & Heng Zhang & Yiyi Zhang & Xianhao Fan & Jiefeng Liu, 2020. "Performance Assessment of Oil-Immersed Cellulose Insulator Materials Using Time–Domain Spectroscopy under Varying Temperature and Humidity Conditions," Energies, MDPI, vol. 13(17), pages 1-14, August.

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