Transmission Line Fault Diagnosis Based on CNN-GRU Neural Network

The environment in which transmission lines are located is complex and harsh, and various types of faults are prone to occur. In order to prevent the further expansion of the scope of accidents caused by transmission line faults, this paper proposes a transmission line fault diagnosis method combining Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). Firstly, a fault diagnosis model of transmission line based on CNN-GRU is established to train and test the collected data; then, a simulation test system of transmission line is established, the parameters of the system and line are designed, and the transmission line is proposed. Finally, accurate fault diagnosis is carried out by simulating the test system and the diagnostic model. The experimental results show that the accuracy of fault diagnosis using this method is very high, and it can guide the daily operation and maintenance of actual transmission lines.