부산대학교 도서관

With the development of electronic technology and artificial intelligence technology, the power consumption of consumer electronics is increasing, such as sweeping robot, dining robot, electric vehicle and so on. And most of these consumer electronics using DC power from lithium batteries, which are organized together in series and parallel for a high power supply. The DC arc occured between connectors and wires is a potential threat to human safety. Lots of researchers and companies study and develop DC arc sensors to detect DC arc faults. Due to the limitations of the DC sensors, the detection range is concentrated in the low-frequency spectrum band(20 kHz–500 kHz). To address this constraint, we propose a neural network-based arc fault detection sensor for DC arc detection. Firstly, we design an arc signal acquisition module based on electromagnetic induction, which automatically matches the sampling frequency and achieves signal amplification. The sampling frequency can reach 4 MHz. Secondly, we propose a Leftover Gated Recurrent Neural Network that extracts sufficient features from the context of current information and performs classification. The test results demonstrate that the model has outstanding accuracy performance, with an improvement of 1.5% over existing models.