Real-Time Series Arc Fault Detection and Appliances Classification in AC Networks Based on Competing Convolutional Kernels

This article presents a method to detect and classify series arc faults affecting domestic AC electrical circuits by the analysis of electric current time series data, based on the HYDRA (HYbrid Dictionary-Rocket Architecture) algorithm, a fast dictionary method for time series classification employing competing convolutional kernels. The key novel contributions are twofold: Competing convolutional kernels are suitable to effectively extract features representing an effective set of arc fault detection indicators, and the classification performed in this way is feasible to be executed in real time. The proposed method is validated using a public database, where data from 13 different types of loads is collected according to the IEC 62606 standard. To reduce inference time and optimize the algorithm for embedded control units, a feature reduction strategy is employed. The effectiveness of the proposed method is demonstrated through experimental tests conducted under both arcing and non-arcing conditions and across different load types. Moreover, its accuracy is also tested in case of transients caused by operational changes in common electrical appliances. Achieved results show a detection accuracy of approximately 99%, with appliance classification performance around 98%, with inference times ranging from 2.8 to 172.0 ms while executing the algorithm on an ARM Cortex-based board.