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.
Highlights
Electrical fires could be induced by multiple reasons, e.g., arc fault, over current, leakage or overheating of electrical appliances
A data acquisition system (DAQ), which consists of a NI-PXIe-1071 chassis, a NI-PXIe-5122 module, and a current transformer (CT, ZCT20-H, with cut-off frequency 250 kHz) is used to collect the loop current from the experimental circuit
When a series arc fault occurs in the circuit, a large number of high-frequency signals would be generated randomly
Summary
Electrical fires could be induced by multiple reasons, e.g., arc fault, over current, leakage or overheating of electrical appliances. Arc faults often occur in residential power wires due to cable aging, loose electrical connections or virtual contacts, which are able to produce high temperatures exceeding. If effective arc fault identification measures are not taken in time to implement interruption, it may lead to a risk of electrical fire or even explosion [2]. 40% of electrical fires are caused by arc faults. More attention should be paid worldwide to protect against electrical fires caused by arc fault [4,5]. According to the Under Laboratories (UL) Standard UL1699 [6], arcing is defined as a continuous luminous discharge of electricity across an insulating medium, usually accompanied by the partial volatilization of the electrodes, and is a very complicated electromagnetic reaction process [2]. Arc faults are categorized into three types as shown in Figure 1: series arc fault, parallel arc fault, and ground arc fault, among which the series arc occurs the most frequently [7,8,9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
