In this paper,a faulty line determination method based on transfer-depthwise separable convolution neural net-works(T-DSCNN)is studied.The method aims to improve the accuracy and efficiency of faulty line determination in power systems.By introducing the concept of migration learning,the T-DSCNN is able to accelerate the training process of the model and improve its generalisation ability by using the pre-trained model parameters as initial weights.Moreover,the use of deep separable convolution technique reduces the number of parameters of the model and lowers the computa-tional complexity,thus making the model more suitable for the scenario of real-time determining faulty line while maintai-ning high accuracy.Through testing on standard datasets,T-DSCNN shows excellent performance on the fault routing task,which significantly improves the recognition speed and accuracy compared to conventional convolutional neural net-works and other methods.
T-DSCNNfaulty line determinationmigration learningfused image