Method of Series Arc Fault Detection Based on Phase Space Reconstruction and Convolutional Neural Network

Abstract

Series arc fault detection can improve the safety of low-voltage power systems. The existing arc fault detection is mainly based on various indicators of a frequency domain or time-frequency domain transformation for feature extraction, which is difficult to extract comprehensive arc information, resulting in low detection accuracy. This paper presents a method for extracting comprehensive information, combined with a convolutional neural network to detect arc faults. First, the arc fault experimental platform is developed according to the UL1699 standard, and the current signals of various loads under different operating conditions are collected. Then, the current of a single cycle is embedded by coordinate delay, and the distance matrix is calculated by using 50 vectors reconstructed by a single cycle. Finally, a convolutional neural network classification model is designed, which is used to mine the information in the distance matrix to detect series arc faults. The experimental results show that the average accuracy of the method for arc fault identification of various loads is 99.00% and that the sampling frequency is low. It is suitable for lines with different loads and has certain robustness, so this method has the potential to be implemented on hardware.