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Fault diagnosis of CNN-based AC transmission lines

As the scale of the transmission system continues to expand, the structure and fault characteristics of the grid become more and more complex, and the existing fault diagnosis methods have difficulty in extracting the fault characteristics accurately in the face of the complex grid. A convolutional neural network (CNN) based fault diagnosis method is proposed. Firstly, the network structure is tested step by step through layer-by-layer filtering and layer-by-layer incremental stacking, with the aim of building a network structure fully adapted to grid fault diagnosis; then the training parameters are adjusted by optimization strategies; finally, an AC transmission system model is built on the MATLAB/SIMULINK platform, and the diagnostic effect is demonstrated by combining the network with the fault diagnosis. It is proved that this method has a high diagnostic accuracy and also reduces the number of fault samples, which is more in line with the practical needs.