| Summary: | Diagnosing the fault type accurately from a variety of faults is very essential to ensure a stable electricity supply when a short-circuit fault occurs. In this paper, a hybrid classification model combining the one-dimensional convolutional neural network (1D-CNN) and the bidirectional long short-term memory network (BiLSTM) is proposed for the classification of cable short-circuit faults to improve the accuracy of fault diagnosis. Sample sets of the current signal for single-phase grounding short circuit, two-phase grounding short circuit, two-phase to phase short circuit, and three-phase grounding short-circuit are obtained by the simulink model, and the signal is input to this network model. The local features of the cable fault signals are extracted using 1D-CNN and the fault signal timing information is captured using BiLSTM, which enables the diagnosis of cable faults based on the automatically extracted features. The experimental results of the simulation show that the model can obtain a good recognition performance and can achieve an overall accuracy of 99.45% in classifying the four short-circuit faults with 500 iterations. In addition, the analysis of loss function curves and accuracy curves shows that the method performs better than networks with only temporal feature extraction, such as 1D-CNN and LSTM.
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