IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i4p992-d142077.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/4/992/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/4/992/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.

    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. 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.
    2. Yue Shen & Muhammad Abubakar & Hui Liu & Fida Hussain, 2019. "Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems," Energies, MDPI, vol. 12(7), pages 1-26, April.
    3. Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
    4. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.
    5. Ferhat Ucar & Jose Cordova & Omer F. Alcin & Besir Dandil & Fikret Ata & Reza Arghandeh, 2019. "Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks," Energies, MDPI, vol. 12(8), pages 1-26, April.
    6. Danilo Pinto Moreira de Souza & Eliane Da Silva Christo & Aryfrance Rocha Almeida, 2017. "Location of Faults in Power Transmission Lines Using the ARIMA Method," Energies, MDPI, vol. 10(10), pages 1-12, October.
    7. Supanat Chamchuen & Apirat Siritaratiwat & Pradit Fuangfoo & Puripong Suthisopapan & Pirat Khunkitti, 2021. "High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm," Energies, MDPI, vol. 14(5), pages 1-18, February.
    8. Michal Jasiński & Tomasz Sikorski & Dominika Kaczorowska & Jacek Rezmer & Vishnu Suresh & Zbigniew Leonowicz & Paweł Kostyla & Jarosław Szymańda & Przemysław Janik, 2020. "A Case Study on Power Quality in a Virtual Power Plant: Long Term Assessment and Global Index Application," Energies, MDPI, vol. 13(24), pages 1-20, December.
    9. Zhuo Liu & Tianzhen Wang & Tianhao Tang & Yide Wang, 2017. "A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI," Energies, MDPI, vol. 10(9), pages 1-15, August.

    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:11:y:2018:i:4:p:992-:d:142077. 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.