IDEAS home Printed from https://ideas.repec.org/a/ids/ijetpo/v18y2023i3-4-5p257-274.html
   My bibliography  Save this article

Fault identification method of electrical automation distribution equipment in distribution networks based on neural network

Author

Listed:
  • Zhenzhuo Wang
  • Yijie Zhu

Abstract

Fault identification of power distribution equipment is of great significance in ensuring the reliability of power supply, saving operating costs, and improving work efficiency. Therefore, a fault identification method of electrical automation distribution equipment in distribution networks based on neural network is proposed. AT89C51 microcontroller is used to establish the architecture of equipment running status signal acquisition, and carry out noise reduction processing. The BP neural network is used to build a fault identification model for power distribution equipment, with the filtered signal used as the model input parameter, and the fault identification result used as the model output parameter, to obtain the fault identification result. The experimental results show that the signal-to-noise ratio of the equipment operation signal of this method has an average value of 54.61 dB, the recognition accuracy remains above 95%, and the average completion time of the identification task is 69.1 ms.

Suggested Citation

  • Zhenzhuo Wang & Yijie Zhu, 2023. "Fault identification method of electrical automation distribution equipment in distribution networks based on neural network," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 18(3/4/5), pages 257-274.
    • Handle: RePEc:ids:ijetpo:v:18:y:2023:i:3/4/5:p:257-274
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=134159
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijetpo:v:18:y:2023:i:3/4/5:p:257-274. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=12 .

    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.