Abstract: The single-phase fault routing problem of small-current grounding systems is an important problem in the fault of distribution network power systems. Due to the temporal continuity and excessive data length of power fault data and the fact that existing research work cannot effectively distinguish the characteristics of single-phase ground fault currents with timing. To overcome these problems, this paper proposed a hybrid neural network model based on a self-attentive TCN+Transformer (called TTHNN-SA model). Since the power fault data has a single feature, the use of wavelet transform decomposition and principal component analysis (PCA) methods can increase the number of features in the sample data. Therefore, the TTHNN-SA model used a temporal convolution network (TCN) to extract features by convolution operations on the original fault data and on the fault data decomposed by wavelet transform separately respectively, applied Transformer to extract the features of the fault data processed by PCA. Then the extracted feature matrices of the three models were fused and input to the self-attentive layer, and this layer assigned higher weights to the important features through matrix calculation, which can solve the long-time dependence problem of the model. Finally, the output of the self-attentive layer was pooled by global averaging and then classified using the softmax function. the TTHNN-SA model can learn the current data relationship between different waveform faults more comprehensively, and it had a good effect on the detection of single-phase faults in distribution networks.