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A Novel Methodology for Series Arc Fault Detection by Temporal Domain Visualization and Convolutional Neural Network.

Sensors (Basel, Switzerland)·2020
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Series AC Arc Fault Detection Method Based on High-Frequency Coupling Sensor and Convolution Neural Network.

Ruobo Chu1, Patrick Schweitzer2, Rencheng Zhang1

  • 1Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Xiamen 361021, China.

Sensors (Basel, Switzerland)
|September 4, 2020
PubMed
Summary

This study introduces a new high-frequency sensor and a convolutional neural network to detect series arc faults in residential low-voltage lines. The system accurately identifies arc faults and appliance types, enhancing fire safety.

Keywords:
convolution neural networkfault diagnosishigh-frequency coupling sensorseries arc

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Area of Science:

  • Electrical Engineering
  • Fire Safety Engineering
  • Artificial Intelligence

Background:

  • Series arc faults in low-voltage residential distribution networks are a significant cause of fires.
  • Existing circuit protection devices fail to detect series arc faults due to limited fault current.

Purpose of the Study:

  • To propose a novel high-frequency coupling sensor for detecting low-voltage series arc faults.
  • To develop and validate a convolutional neural network-based detection algorithm for series arcs and load identification.

Main Methods:

  • A high-frequency coupling sensor was designed to capture arc fault signals from various loads.
  • Temporal-domain signals were converted into two-dimensional feature gray images.
  • A three-layer convolutional neural network was trained and tested on image datasets of normal and arc states.

Main Results:

  • The sensor demonstrated a simple structure and wide frequency response.
  • The detection algorithm achieved 98.36% accuracy in classifying seven domestic appliances and 99% accuracy with adjustable loads.
  • The feasibility of hardware implementation using FPGA was evaluated.

Conclusions:

  • The proposed method effectively detects series arc faults and identifies load types with high accuracy.
  • The system offers a promising solution for enhancing fire safety in residential electrical systems.
  • The approach is stable, universal, and suitable for hardware implementation.