Tree Growth Simulation and Safe Distance Calculation Algorithm Design of Transmission Channel Based on Deep Learning

With the continuous growth of power demand, the rapid growth of trees in transmission channels has become an important challenge for power systems. Excessive or irregular tree growth may lead to coverage and damage of power lines, thus endangering the safety of transmission channels. In this study, a tree growth simulation and safe distance calculation algorithm based on deep learning is proposed, aiming at providing a new solution to improve the management efficiency and safety of transmission channels. Firstly, U-Net is selected for training to realize the accurate segmentation of single tree. The training data set contains labeled tree region images as the input of the network and the corresponding binary mask as the label. By minimizing the loss function, the network can learn the semantic information of trees in the image. Secondly, this article constructs a tree growth simulation and safety distance calculation algorithm for transmission channels based on a variant of the Recurrent Neural Network (RNN) called gated recurrent uni (GRU). Compared to traditional RNN, it introduces the concept of cells and solves the problem of gradient explosion in long sequence training. Finally, we validated the effectiveness of the proposed algorithm through actual datasets. The experimental results show that our algorithm has achieved good performance in tree growth simulation and safe distance calculation, which provides a feasible solution for the management of transmission channels.