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Optimal Design of a Band Pass Filter and an Algorithm for Series Arc Detection

Author

Listed:
  • Hong-Keun Ji

    (Physical Engineering Division, National Forensic Service, Daegu 39872, Korea)

  • Guoming Wang

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

  • Woo-Hyun Kim

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

  • Gyung-Suk Kil

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

Abstract

Detection and analysis of series arcs is significantly meaningful for preventing arc-caused electrical fires in advance. However, the improvement of arc detection sensitivity and the discrimination of arc conditions are still challenges when developing an arc fault detector. In this paper, arc signals in various loads with three major incomplete connection states were detected and further analyzed using the discrete wavelet transform. It was verified that the db13 was the optimal mother wavelet to analyze the arc pulses and the decomposed signals in the detail components of D5, D6, D7, and D8 were related with arc phenomena. Therefore, a band pass filter with a frequency from 2.4 to 39 kHz was designed, which can extract arc signals while eliminating the AC mains current and noise generated in loads. By investigating the arc signal energy as well as the arc pulse counts that were important parameters of arc occurrence, an arc diagnosis algorithm was developed based on LabVIEW program for electrical fire prevention.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:992-:d:142077
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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. Qiwei Lu & Zeyu Ye & Yilei Zhang & Tao Wang & Zhixuan Gao, 2019. "Analysis of the Effects of Arc Volt–Ampere Characteristics on Different Loads and Detection Methods of Series Arc Faults," Energies, MDPI, vol. 12(2), pages 1-16, January.
    2. Kiyoto Takenaka & Yusuke Ishikawa & Yukio Mizuno & Wenyi Lin, 2020. "Arc Discharge–Induced Ignition of Combustibles Placed on a Damaged AC Power Supply Cord," Energies, MDPI, vol. 13(3), pages 1-15, February.
    3. Hong-Keun Ji & Sung-Wook Kim & Gyung-Suk Kil, 2020. "Phase Analysis of Series Arc Signals for Low-Voltage Electrical Devices," Energies, MDPI, vol. 13(20), pages 1-14, October.
    4. Marit Sigfrid Bakka & Erling Kristian Handal & Torgrim Log, 2020. "Analysis of a High-Voltage Room Quasi-Smoke Gas Explosion," Energies, MDPI, vol. 13(3), pages 1-14, January.
    5. 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.
    6. Qiongfang Yu & Yaqian Hu & Yi Yang, 2019. "Identification Method for Series Arc Faults Based on Wavelet Transform and Deep Neural Network," Energies, MDPI, vol. 13(1), pages 1-12, December.

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