Abstract
Fault arcs exhibit randomness, with current waveforms closely mirroring those of standard nonlinear load operations, posing challenges for traditional series fault arc detection methods. This study presents an improved detection approach using a lightweight convolutional neural network model, ShuffleNet V2. Current data from household loads were collected and preprocessed to establish a comprehensive database, leveraging one-dimensional convolution and channel attention mechanisms for precise analysis. Experimental results demonstrate a high fault arc detection accuracy of 97.8%, supporting real-time detection on the Jetson Nano embedded platform, with an efficient detection cycle time of 15.65 ms per sample. The proposed approach outperforms existing methods in both accuracy and speed, providing a robust foundation for developing advanced fault arc circuit breakers.
Published Version
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