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
相关论文
共 34 条
  • [1] WALTER M, FRANCK C．, Improved method for direct black-box arc parameter determination and model validation[J], IEEE Transactions on Power Delivery, 29, 2, pp. 580-588, (2014)
  • [2] LIU Yanli, GUO Fengyi, LI Lei, A kind of series fault arc mathematical model[J], Transactions of China Electrotechnical Society, 34, 14, pp. 2901-2912, (2019)
  • [3] Xiangzheng XU, Wenguang HU, Modeling and simulation of arc grounding fault of middle and low voltage distribution network based on ATP-EMTP[J], Journal of Computational Methods in Sciences and Engineering, 20, 4, pp. 1279-1288, (2020)
  • [4] WU Jiazhou, Jiawen SHI, Yanfeng GAO, Penetration recognition in GTAW welding based on time and spectrum images of arc sound using deep learning method[J], Metals, 12, 9, (2022)
  • [5] Chao CHEN, Runquan XIAO, Huabin CHEN, Arc sound model for pulsed GTAW and recognition of different penetration states[J], The International Journal of Advanced Manufacturing Technology, 108, 9-10, pp. 3175-3191, (2020)
  • [6] Qing XIONG, Shengchang JI, ZHU Lingyu, A novel DC arc fault detection method based on electromagnetic radiation signal[J], IEEE Transactions on Plasma Science, 45, 3, pp. 472-478, (2017)
  • [7] SONG Dongdong, CHENG Lin, LIN Zhifa, Review of arc thermal plasma modeling，simulation and application[J], High Voltage Engineering, 44, 3, pp. 932-943, (2018)
  • [8] CUI Ruihua, ZHANG Zhen, TONG Deshuan, Aviation AC series arc fault detection based on improve empirical wavelet transform multi-feature fusion[J], Transactions of China Electrotechnical Society, 37, 12, pp. 3148-3161, (2022)
  • [9] XIONG Lan, ZENG Zeyu, YANG Jun, Mathematical model and characteristics of low current DC fault arc[J], Transactions of China Electrotechnical Society, 34, 13, pp. 2820-2829, (2019)
  • [10] Silei CHEN, Xingwen LI, Zhimin XIE, Time–frequency distribution characteristic and model simulation of photovoltaic series arc fault with power electronic equipment[J], IEEE Journal of Photovoltaics, 9, 4, pp. 1128-1137, (2019)
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