Research on Overvoltage Identification Method of EMUs High Voltage Electrical System Based on Deep Learning

To solve the problem that it is difficult to identify the overvoltage in high-voltage electrical system of EMUs, a method for identifying overvoltage types of high-voltage electrical system of multiple units based on ShuffleNet lightweight convolutional neural network (CNN) is proposed. Six overvoltage types are mapped into gray images by B2G algorithm, which is input into ShuffleNet network to identify all kinds of gray images, and different overvoltages are classified into their families by training. This method analyzes the accuracy of the model under different parameters, and obtains the optimal parameter combination of the model through the training of the model under different parameters. Six shallow machine learning models are built and compared. Experimental results show that this method has higher accuracy in small sample data sets. Compared with traditional machine learning, it avoids the complexity of manual feature extraction, improves the generalization ability of the model, and verifies that this method has better recognition and classification performance.