ArcNet: Series AC Arc Fault Detection Based on Raw Current and Convolutional Neural Network

AC series arc is dangerous and can cause serious electric fire hazards and property damage. This paper proposed a Convolutional Neural Network (CNN) based arc detection model named ArcNet. The database of this research is collected from 8 different types of loads according to IEC62606 standard. The two most common types of arcs, including arcs from a loose connection of cables and those caused by the failure of the insulation, are generated in testing and included in the database. Using the database of raw current, experimental results indicate ArcNet can achieve a maximum of 99.47\% arc detection accuracy at 10 kHz sampling rate. Even with a reduced sampling frequency of 1 kHz, our ArcNet achieved reasonably good performance. The model is also implemented in Raspberry Pi 3B for classification accuracy. A trade-off study between the arc detection accuracy and model runtime has been conducted. The proposed ArcNet obtained an average runtime of 31ms/sample of 1 cycle at 10 kHz sampling rate, which proves the feasibility of practical hardware deployment for real-time processing.