Research on Time–frequency Analysis and Identification of Series Arc Fault Based on Generalized S-transform

被引：0
|
作者：
Zhang P. [1 ]
Qin Y. [1 ]
Song R. [1 ]
Chen G. [1 ]
机构：
[1] China Electric Power Research Institute, Haidian District, Beijing
来源：
Dianwang Jishu/Power System Technology | 2024年 / 48卷 / 07期
关键词：
arc fault; bi-Gaussian window; CNN; generalized S-transform; time-frequency analysis;
D O I：
10.13335/j.1000-3673.pst.2023.1129
中图分类号：
学科分类号：
摘要：
It is difficult to identify the arc fault effectively when the loads on the user side have become increasingly complex, which blocks the development of fault monitoring and pre-warning inspection. In this paper, the time–frequency analysis and identification of series arc fault was studied based on the generalized S-transform. Firstly, the differences in time-frequency features of arc faults among 3 signal processing methods, STFT (Short-time Fourier Transform), wavelet transform and generalized S-transform were compared, highlighting the advantages of generalized S-transform in processing high-frequency features of nonlinear loads. After that, the bi-Gaussian generalized S-transform was used to receive time-frequency features of the nonlinear loads and construct image feature samples. Finally, the samples are trained and classified by 2D-CNN (two-dimensional Convolutional Neural Network), and the recognition effectiveness was verified by the accuracy and clustering analysis. The overall accuracy is 96.81%, of which involves various domestic loads, providing a reference for the follow-up arc fault monitoring and inspection research. © 2024 Power System Technology Press. All rights reserved.
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页码：2995 / 3003
页数：8
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