Fault Diagnosis of Communication Networks in Smart Substations Based on Convolutional Neural Networks

Rapid fault diagnosis and localization in smart substation communication networks can significantly enhance maintenance efficiency and operational reliability. Focusing on the secondary-system communication network of smart substations, this study leverages the advantages of convolutional neural networks (CNNs) in complex data processing and information fusion to propose a CNN-based fault diagnosis model. The model's performance is evaluated by comparing the similarity between its predicted fault types and the actual ones. The results demonstrate that the model achieves high accuracy.