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A novel methodology for series arc fault detection by temporal domain visualization and convolutional neural network
Yang K(杨凯)1; Chu, Ruobo1,2; Zhang RC(张认成)1; Xiao JC(肖金超)2; Tu R(涂然)1
Department广州中国科学院沈阳自动化研究所分所
Source PublicationSensors (Switzerland)
ISSN1424-8220
2020
Volume20Issue:1Pages:1-13
Indexed BySCI ; EI
EI Accession number20200207989434
WOS IDWOS:000510493100162
Contribution Rank2
Funding OrganizationFujian Natural Science Foundation of China (No. 2018J05082) ; Quanzhou City Science&Technology Program (No. 2018C117R) ; Pearl River S&T Nova Program of Guangzhou (No. 201710010023) ; Scientific Research Funds of Huaqiao University (No. 19BS103) ; National Natural Science Foundation of China (No. 51506059) ; Open Project Program of State Key Laboratory of Fire Science and the Subsidized Project for Postgraduates’ Innovative Fund in Scientific Research of Huaqiao University
Keywordseries arc fault convolutional neural network temporal domain visualization gray image
Abstract

AC arc faults are one of the most important causes of residential electrical wiring fires, which may produce extremely high temperatures and easily ignite surrounding combustible materials. The global interest in machine learning-based methods for arc fault diagnosis applications is increasing due to continuous challenges in efficiency and accuracy. In this paper, a temporal domain visualization convolutional neural network (TDV-CNN) methodology is proposed. The current transformer and high-speed data acquisition system are used to collect the current of a series of arc faults, then the signal is filtered by a digital filter and converted into a gray image in time sequence before being fed into TDV-CNN. Five different electric loads were selected for experimental validation with various signal characteristics, including vacuum cleaner, fluorescent lamp, dimmer, heater, and desktop computer. The experimental results confirm that the classification accuracy of the five loads’ work states in the ten categories could reach 98.7% or even higher by adjusting parameters perfectly. The methodology is believed to be reliable for series arc detection with relatively high accuracy and also has important potential applications in other fault diagnosis fields.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26183
Collection广州中国科学院沈阳自动化研究所分所
Corresponding AuthorZhang RC(张认成)
Affiliation1.Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Xiamen 361021, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Guangzhou 511458, China
Recommended Citation
GB/T 7714
Yang K,Chu, Ruobo,Zhang RC,et al. A novel methodology for series arc fault detection by temporal domain visualization and convolutional neural network[J]. Sensors (Switzerland),2020,20(1):1-13.
APA Yang K,Chu, Ruobo,Zhang RC,Xiao JC,&Tu R.(2020).A novel methodology for series arc fault detection by temporal domain visualization and convolutional neural network.Sensors (Switzerland),20(1),1-13.
MLA Yang K,et al."A novel methodology for series arc fault detection by temporal domain visualization and convolutional neural network".Sensors (Switzerland) 20.1(2020):1-13.
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