IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2019i1p142-d302544.html
   My bibliography  Save this article

Identification Method for Series Arc Faults Based on Wavelet Transform and Deep Neural Network

Author

Listed:
  • Qiongfang Yu

    (School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
    Postdoctoral Programme of Beijing Research Institute, Dalian University of Technology, Beijing 100000, China)

  • Yaqian Hu

    (School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China)

  • Yi Yang

    (School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China)

Abstract

The power supply quality and power supply safety of a low-voltage residential power distribution system is seriously affected by the occurrence of series arc faults. It is difficult to detect and extinguish them due to the characteristics of small current, high stochasticity, and strong concealment. In order to improve the overall safety of residential distribution systems, a novel method based on discrete wavelet transform (DWT) and deep neural network (DNN) is proposed to detect series arc faults in this paper. An experimental bed is built to obtain current signals under two states, normal and arcing. The collected signals are discomposed in different scales applying the DWT. The wavelet coefficient sequences are used for forming training set and test set. The deep neural network trained by training set under 4 different loads adaptively learn the feature of arc faults. The accuracy of arc faults recognition is sent through feeding test set into the model, about 97.75%. The experimental result shows that this method has good accuracy and generality under different types of loading.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:142-:d:302544
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/1/142/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/1/142/
    Download Restriction: no
    ---><---

    References listed on IDEAS

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

    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 & 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.
    2. 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.
    3. 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.
    4. Karol Nowak & Jerzy Janiszewski & Grzegorz Dombek, 2021. "The Possibilities to Reduce Arc Flash Exposure with Arc Fault Eliminators," Energies, MDPI, vol. 14(7), pages 1-25, March.
    5. 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.
    6. 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.
    7. Giovanni Bucci & Fabrizio Ciancetta & Andrea Fioravanti & Edoardo Fiorucci & Simone Mari & Alberto Prudenzi, 2020. "Testing System for the On-Site Checking of Magneto-Thermal Switches with Arc Fault Detection," Energies, MDPI, vol. 13(18), pages 1-18, September.
    8. Karol Nowak & Jerzy Janiszewski & Grzegorz Dombek, 2019. "Thyristor Arc Eliminator for Protection of Low Voltage Electrical Equipment," Energies, MDPI, vol. 12(14), pages 1-15, 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:13:y:2019:i:1:p:142-:d:302544. 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.