A Deep Learning-based Study of Terrain Classification and Distance Optimization for Transmission Lines

The current terrain classification of transmission lines is generally target classification or fixed-point classification, which lacks comparability and leads to the reduction of the optimization frequency of a single transmission line, so we propose to study the terrain classification and distance optimization of transmission lines based on deep learning. According to the current measurement, data acquisition and terrain feature extraction are carried out first to improve the contrast of optimization in a multistage way, and the multistage classification of terrain and preliminary planning of paths are carried out. Based on this, a deep learning transmission line distance optimization model is designed, and dynamic correction scheduling is adopted to realize the optimization process. The test results show that the deep learning transmission line terrain classification and distance optimization method is higher compared with the renewable energy transmission line protection method under the prediction of traveling wave arrival time, and the efficient hybrid strategy of FFT and fuzzy logic technology in the transmission line fault diagnosis method, which indicates that the optimization effect of the present study has a stronger coverage, better processing speed, and a more accurate completion of the preset tasks.