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Optimal Detection and Identification of DC Series Arc in Power Distribution System on Shipboards

Author

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  • Hong-Keun Ji

    (Physics and Engineering Division, National Forensic Service, Busan 50612, Korea)

  • Guoming Wang

    (Hangzhou Guozhou Power Technology Co., Ltd., Hangzhou 310015, China)

  • Gyung-Suk Kil

    (Department of Electrical and Electronics Engineering, Korea Maritime and Ocean University, Busan 49112, Korea)

Abstract

In this paper, a series arc was simulated under resistive load and motor load, which are mainly used in small ships, and the arc signal was analyzed using discrete wavelet transform. After calculating the correlation coefficient between the single arc pulse and the wavelet, Biorthogonal (bior) 3.1 was selected as the optimal mother wavelet, and the signal was analyzed using multiresolution analysis. From the results, arc signals were distributed in the detail components D2, D3, D4 and D5, corresponding to a frequency range of 19.5–312.5 kHz, with the optimal arc signal extracted based on these values. In addition, in order to distinguish between arc and normal conditions, signal energy was analyzed. By applying the magnitude and signal energy analysis method, the DC series arc generated in the power distribution system of a shipboard was identified.

Suggested Citation

  • Hong-Keun Ji & Guoming Wang & Gyung-Suk Kil, 2020. "Optimal Detection and Identification of DC Series Arc in Power Distribution System on Shipboards," Energies, MDPI, vol. 13(22), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5973-:d:445760
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    References listed on IDEAS

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    1. Hong-Keun Ji & Guoming Wang & Woo-Hyun Kim & Gyung-Suk Kil, 2018. "Optimal Design of a Band Pass Filter and an Algorithm for Series Arc Detection," Energies, MDPI, vol. 11(4), pages 1-13, April.
    2. Guoming Wang & Gyung-Suk Kil & Hong-Keun Ji & Jong-Hyuk Lee, 2017. "Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears," Energies, MDPI, vol. 10(11), pages 1-12, November.
    3. Hsueh-Hsien Chang & Nguyen Viet Linh, 2017. "Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems," Energies, MDPI, vol. 10(5), pages 1-20, April.
    4. Ferhat Ucar & Omer F. Alcin & Besir Dandil & Fikret Ata, 2018. "Power Quality Event Detection Using a Fast Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-14, January.
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    Cited by:

    1. Chaochun Yu & Liang Qi & Jie Sun & Chunhui Jiang & Jun Su & Wentao Shu, 2022. "Fault Diagnosis Technology for Ship Electrical Power System," Energies, MDPI, vol. 15(4), pages 1-16, February.
    2. Chunwang Xiaogeng LiRen & Xiaojun Ma & Fuxiang Chen & Zhicheng Yang & Sandeep Panchal, 2022. "Simulation and inspection of fault arc in building energy-saving distribution system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 331-339, March.

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