Electrical fires caused by arc faults necessitate advanced detection methods for improved safety and reliability in AC and DC power systems. This research introduces innovative artificial intelligence (AI)-based methods for the efficient detection of arc faults, enhancing both safety and reliability in electrical systems. For AC systems, we developed a convolutional neural network (CNN)-based arc fault detection algorithm that autonomously extracts arc fault features without manual thresholding. Using raw current as input, the algorithm achieves an arc fault detection accuracy of 99.47%. Additionally, this research determined an optimal sampling rate of 10 kHz for the input current. The model's efficacy was verified using the Raspberry Pi 3B platform.
While traditional CNN algorithms have high accuracy, they require optimization for real-time arc fault detection on resource-limited hardware. To address this, we proposed a lightweight CNN architecture combined with a model compression technique using a knowledge distillation-based teacher-student algorithm. This model maintains a high detection accuracy of 99.31% and operates with an impressively minimal runtime of 0.20 milliseconds per sample when implemented on the Raspberry Pi 3B platform. This performance demonstrates its suitability for commercial embedded microcontrollers (MCUs) with limited computational capability.
Extending our research to DC systems, we introduced a cost-effective, AI-driven Arc Fault Circuit Interrupter (AFCI) for DC applications. Utilizing an STM32 MCU and a silicon carbide (SiC) MOSFET-based solid-state circuit breaker (SSCB), the proposed method achieves a detection accuracy of 98.15% with a remarkable arc fault interruption time of 25 milliseconds. This AFCI solution stands out for its rapid response and high reliability, promising significant improvements in safety for DC systems.
Together, these contributions signify a leap forward in electrical safety, presenting viable solutions for the timely detection and interruption of arc faults in AC and DC systems. The outcomes of this research are expected to influence future standards and practices in electrical safety management across residential, commercial, and industrial sectors.