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

Authors: Zhenzhuo Wang; Yijie Zhu

Addresses: Department of Automation Engineering, Henan Polytechnic Institute, Nan Yang, 473000, China ' Department of Electronic Information Engineering, Henan Polytechnic Institute, Nan Yang, 473000, China

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.

Keywords: neural network; distribution networks; fault identification; architecture; electrical automation.

DOI: 10.1504/IJETP.2023.134159

International Journal of Energy Technology and Policy, 2023 Vol.18 No.3/4/5, pp.257 - 274

Received: 25 Apr 2023
Accepted: 03 Jul 2023
Published online: 12 Oct 2023 *

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