## 5 Experiment and Result Analysis
### The Experimental Results  
Various existing models and comparative experimental designs are shown in Table 1. All kinds of existing models directly use the original data for three-phase fault detection. Each phase fault detection dataset and three-phase fault mixture dataset were used as the input of different network models for fault diagnosis and fault phase selection. Since there is no global attention in GRU, LSTM and original TCN, IMF4, IMF5 and IMF6 after VMD decomposition are superimposed and reconstructed as the input of the three networks. The Mish activation function was used in both networks. The detection results of each phase fault and fault phase selection of GTCN are compared with the detection results of traditional recurrent neural network GRU, LSTM and original TCN. The experimental results are selected after the network iteration process is stable. Experimental results show that the detection accuracy of GTCN is higher than that of existing deep learning fault detection models. In recurrent neural networks, the detection accuracy of the model using VMD for initial feature extraction is significantly higher than that of the model without VMD. Only models using VMD are analyzed below. The phase selection accuracy of GRU and LSTM reached 93.87% and 96.13%, respectively. Compared with LSTM, the accuracy of fault arc detection in each phase of GRU is reduced by about 1%, and the accuracy of fault phase selection is reduced by about 2.3%. This indicates that although GRU has fewer parameters and a relatively simple structure, compared with the special gate structure of LSTM, GRU has insufficient ability to capture depth features. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **A** & **B** & **C** & **Phase Selection** \\ \hline CNN & 76.50\% & 77.75\% & 75.26\% & 75.13\% \\ ALEXNET & 72.01\% & 73.76\% & 72.24\% & 69.87\% \\ CNN-LSTM & 94.76\% & 94.01\% & 95.76\% & 94.50\% \\ GRU & 90.50\% & 89.76\% & 92.26\% & 89.26\% \\ LSTM & 93.50\% & 94.25\% & 94.76\% & 94.13\% \\ TCN & 95.26\% & 94.00\% & 95.51\% & 95.76\% \\ VMD-GRU & 95.25\% & 94.75\% & 95.70\% & 93.87\% \\ VMD-LSTM & 96.13\% & 96.31\% & 96.12\% & 96.13\% \\ VMD-TCN & 96.38\% & 94.98\% & 96.98\% & 95.37\% \\ VMD-GTCN & 99.39\% & 98.63\% & 98.93\% & 98.62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fault identification and phase selection accuracy of each model. The phase selection accuracy of the original TCN network is 95.37%, and the fault detection accuracy of phase A, phase B and phase C is 96.38%, 94.98% and 96.98%. Compared with the recurrent neural network, although TCN has a larger receptive field, the causal convolutional neural network loses some fault and dissimilarity features during the convolutional operation, resulting in its accuracy being similar to that of LSTM and unstable. The fault arc phase selection accuracy of GTCN is 98.62%, and the fault detection accuracy of each phase is about 99%. Compared with the original TCN network, the classification and detection accuracy is improved by about 3%. The global attention mechanism improves the ability of the network to mine deep information by extracting faults and different features. Compared with LSTM, the fault phase selection accuracy of GTCN is increased by about 3%, indicating that GTCN has a large receptive field to sense the phase differentiation characteristics of strong timing. To visually demonstrate the detection effect of the GTCN model, the detection results are visualized and the confusion matrix is generated, as shown in Figure 11. The larger the value on the diagonal of the confusion matrix, the better the detection effect of the model. Here, 0, 1, 2 and 3 represent the normal working state, A-phase fault state, B-phase fault state and C-phase fault state, respectively. In order to verify the impact of the difference of activation functions on the overall architecture, GTCN neural network models under different activation functions were trained, and the evaluation criteria were accuracy, precision, recall and F1 value.