Research on Transmission Line Tree Fault Risk Prediction Based on GA-BP Neural Network and Digital Twin

Abstract

With the increasing complexity and management requirements of power inspection, the use of advanced technologies such as artificial intelligence and digital twins and their application in the field of transmission line inspection has gradually become a cutting-edge direction for identifying typical problems such as tree faults and hidden dangers in transmission lines. The article presents a novel approach for the construction of a digital twin of transmission lines, integrating laser point cloud data, multispectral data, and relevant environmental data generated by tree barriers. This is achieved through the utilization of an optimized GA-BP algorithm, facilitating high-precision prediction and analysis of the growth height and risk level of tree barriers. This offers a promising digital solution for the refined inspection business of transmission lines. The experimental results show that the BP neural network optimized by GA has improved the prediction accuracy by 30% compared to conventional methods, and has higher prediction accuracy. By integrating multiple sources of data such as laser point clouds, a twin transmission line is constructed, combined with advanced artificial intelligence technology, which can not only achieve observability and measurability of transmission lines, but also accurately deduce and predict the growth of tree obstacles in the future, improving the safe operation and management level of the power grid. This is the technological development direction for empowering production business with transmission lines and even digital power grids in the future.