Abstract:
:Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit. With capacitance coupling and a logarithmic detector, the high-frequency radiation characteristics of arc faults can be extracted. A rapid method for computing the current waveform slope characteristics of an arc fault provides another characteristic. Current waveform periodic integral characteristics can be extracted according to asymmetries of the arc faults. These three characteristics are used to develop a detection algorithm of arc faults based on multiinformation fusion and support vector machine learning models. The tests indicated that for series arc faults with single and combination loads and for parallel arc faults between metallic contacts and along carbonization paths, the recognition algorithm could effectively avoid the problems of crosstalk and signal loss during arc fault detection.
journal_name
R Soc Open Scijournal_title
Royal Society open scienceauthors
Yang JH,Fang HY,Zhang RC,Yang Kdoi
10.1098/rsos.180160subject
Has Abstractpub_date
2018-09-19 00:00:00pages
180160issue
9issn
2054-5703pii
rsos180160journal_volume
5pub_type
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