Efficient-ArcNet: Series AC Arc Fault Detection using Lightweight Convolutional Neural Network

Series ac arc fault is dangerous and is difficult to detect because of load characteristics and external noise. Artificial Intelligence (AI)-based arc fault detection models even though can achieve a high arc fault detection accuracy, most of the reported researches are still not suitable for practical real-time and commercial application because of the higher computation burden. This research proposed a lightweight arc fault detection algorithm, Efficient-ArcNet, based on EffNet building blocks which can achieve an arc fault detection accuracy of 99.36%. Considering the practical application, the Efficient-ArcNet model is converted into the TF-Lite model to further optimize it for edge computing devices such as Raspberry PI 4 or Jetson Nano. The proposed model achieves an average runtime of 1.17 ms per sample using Raspberry PI 4B, which indicates its suitability for practical deployment in commercial microcontroller units (MCUs).