Analysis of Arc Characteristics and Fault Detection in Low Voltage DC System

Xiao, Xiaolong; 苏伟,; Guo, Jiahao; Zhang, Chen; Xiong, Qing; Di, Zhenguo; Ji, Shengchang

doi:10.1109/spies52282.2021.9633862

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…As a time-frequency analysis method, the multi-resolution characteristic of wavelet analysis is efficient in many fields. The fusion of fault features in time-frequency domain ensures the realtime detection (Xiong and Chen, 2020) (Liu and Du, 2017).…”

Section: Research Gapmentioning

confidence: 99%

Research on arc fault detection using ResNet and gamma transform regularization

Zhang

Qu²,

Zheng³

et al. 2023

Front. Energy Res.

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Series arc fault is the main cause of electrical fire in low-voltage distribution system. A fast and accurate detection system can reduce the risk of fire effectively. In this paper, series arc experiment is carried out for different kinds of electrical load. The time-domain current is analyzed by Morlet wavelet. Then, the multiscale wavelet coefficients are expressed as the coefficient matrix. In order to meet the data dimension requirements of neural networks, a color domain transformation method is used to transform the feature matrix into an image. A regularization method based on gamma transform is proposed for small sample data sets. The results showed that the proposed regularization method improved the validation set accuracy of ResNet50 from 66.67% to 96.53%. The overfitting problem of neural network was solved. In addition, this method fused fault features of 64 different scales, and provided a valuable manually labeled arc fault dataset. Compared with the threshold detection method, this method was more objective. The use of image features increased intuitiveness and generality. Compared with other typical lightweight networks, this method had the best detection performance.

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Section: Research Gapmentioning

confidence: 99%