Fault Diagnosis Algorithm of Transformer and Circuit Breaker in Traction Power Supply System Based on IoT

Transformers and circuit breakers are essential equipment in traction power supply systems. Once a fault occurs, it will affect the train’s regular operation even threaten passengers’ personal safety. Therefore, is to diagnose faults of transformers system. At present, companies have made many achievements diagnosis equipment, but there still problems with real-time accuracy. The Internet Things (IoT) technology that connects different types terminal devices for information exchange communication achieve intelligence. It includes data acquisition transmission, interaction, processing, decision-making from bottom top. uses sensor terminals obtain status on electrical equipment. Moreover, conducts monitoring intelligent processing In this paper, multi-data fusion IoT combines realize breakers. First, we built an system based multi-terminal IoT. Secondly, transformer model established. We adopt BP neural network algorithm particle swarm optimization (PSO) use PSO optimize feature subset improve performance. Finally, vacuum breaker select current change time node as typical quantities PSO–BP breaker.