Research on a cloud-edge collaborative adaptive detection system for AC series arc faults

Abstract AC series arc faults (SAFs) are one of the leading causes of electrical fires in buildings, and the development of arc fault detection devices (AFDDs) can effectively reduce the fire risk caused by arc faults. To address the issue of unsatisfactory detection performance for SAFs and frequent false positives in existing AFDDs when dealing with unknown load combinations, this paper proposes an adaptive SAF detection system. The system is based on the remote interaction between AFDD and cloud server, which enables the AFDD to update its SAF detection model for unknown load combinations, thereby improving its generalization performance. First, a lightweight neural network model for SAF detection based on depth-wise separable convolution and inverted residual block was designed and ported to the K210 chip, combined with peripheral circuits to create the AFDD. The AFDD collects high-frequency coupling signals from the circuit at a sampling rate of 100 kHz, achieving real-time SAF detection with a detection cycle of 80 ms. The cloud server receives and filters false positive and SAF data uploaded by the AFDD during operation, and updates the detection model on the AFDD through data augmentation and transfer learning to improve its generalization capability. Experimental results show that the normal state recognition rate of the updated AFDD for unknown load combinations increased from 98.87% to 99.92%, and the SAF recognition rate improved from 96.26% to 98.16%. The results demonstrate that the adaptive SAF detection system significantly improves the AFDD’s performance in reducing false positives and missed detections for unknown load combinations.