The invention discloses a low-
voltage series arc fault detection method,
system and device, and belongs to the technical field of low-
voltage series arc fault detection, and the method comprises the steps: obtaining an original current
signal, and carrying out the preprocessing through sliding window segmentation and instance normalization; performing multi-scale
feature fusion on the preprocessed analysis unit, and generating fusion features through parallel
feature extraction and an attention mechanism; performing context modeling on the fused features through an
encoder, inputting a self-adaptive
bottleneck layer containing an expert
hybrid network, and routing the features to the most appropriate expert network by
context sensing gating according to
global information; the decoder reconstructs the
signal and calculates an error, and generates a dense abnormal fraction sequence;
gaussian position weighted aggregation abnormal scores are adopted, and fault judgment is carried out in combination with a self-adaptive threshold decision
mechanism based on K-Means clustering. The method can be trained without a fault sample, can dynamically adapt to complex current
modes under different loads, gets rid of dependence on the fault sample, and accurately detects the arc fault.