Practical Real-Time AC Arc Fault Detection using Lightweight Features on Embedded Hardware

Abstract

AC series arc faults in household appliances pose significant safety risks but remain challenging to detect due to load-dependent signatures and complex interactions in mixed-branch operations. This paper presents a practical, real-time arc fault detection system that integrates a comprehensive two-branch household dataset, lightweight handcrafted features, and classical machine learning for deployment on an STM32F407 microcontroller (MCU). Current signals were sampled at 10 kHz across 32 scenarios, encompassing 13 singleload and 19 two-branch mixed-load cases. Each signal window is represented by a 133-dimensional feature vector, comprising five statistical descriptors and 128 FFT magnitude coefficients, optimized for resource-constrained environments. Among various evaluated classifiers, Random Forest was selected for its superior trade-off between recognition performance and deployment efficiency. The proposed model achieved 89.18% accuracy, 91.34% recall, and 86.96% F1-score. Furthermore, a leave-one-scenario-out (LOSO) evaluation was employed to systematically analyze the masking effect in multi-branch environments, identifying challenges in which high-power loads obscure weak arc signatures. The complete firmware requires 896 KB of Flash and 102 KB of RAM, with a total processing time of only 7.02 ms per cycle, significantly within the update interval and confirming its feasibility for low-cost, real-time protection devices.