Research on the Construction of Transformer Fault Diagnosis Simulation System Based on Digital Twin Technology

Transformers are important equipment in the power system. In order to improve the stability and safety of power system operation, a transformer fault diagnosis model based on digital twin technology was studied and constructed. This model uses one-dimensional convolutional neural networks to diagnose and analyze transformer faults. The results show that the model can achieve 100 % diagnostic accuracy when the transformer is in a low-temperature overheating fault state, and the diagnostic accuracy drops to 97 % when the transformer experiences a medium temperature overheating fault. The power equipment fault diagnosis model constructed through research has effectively improved the accuracy of secondary equipment transformer fault diagnosis, achieved real-time detection of power system transformer fault diagnosis, and ensured the stable and safe operation of the power system. Through the diagnosis of transformer faults in secondary equipment, simulation analysis can help power system maintenance personnel better understand the mechanism of transformer faults and corresponding measures.