Design of intelligent optimization system for transmission lines based on BP neural network

In the electric power system, transmission lines perform a vital role. This article provides a comprehensive analysis of the current situation and existing problems in the maintenance of transmission and transformation lines through the practical summary of maintenance work, in order to provide a reference for improving the safe operation level of transmission and transformation lines. This article first provides a detailed introduction to the Back Propagation neural network (BPNN) and intelligent optimization of transmission lines. Then, it explores the design of an intelligent optimization system for transmission lines based on BPNN. At the same time, it discusses the design of an optimization system for transmission lines based on the algorithm proposed in this article. Finally, experiments were conducted to verify that the intelligent optimization system for transmission lines based on BPNNs has better accuracy and accuracy in detecting line faults than systems based on random forest models and convolutional block attention models. The fault detection rate of the algorithm in this article is 16% and 21% higher than that of the random forest model and convolutional block attention model systems. The fault location accuracy of the algorithm in this article is 10.88% and 16.11% higher than that of the systems using random forest models and convolutional block attention models. The accuracy of fault type recognition in this algorithm is higher than that of the random forest model and convolutional block attention model systems by 7% and 17.12%, respectively.