Design of Characteristic Marking System for Danger Sources Invasion by Tree Barriers in Transmission Channels Based on Random Forest Algorithm

The purpose of this study is to propose and design a characteristic marking system for the risk sources of tree barrier intrusion in power transmission channels based on random forest (RF) algorithm, so as to strengthen the safety and reliability of power transmission channels. By combining the domain knowledge of machine learning and power system security management, the system can accurately mark the potential risk position of tree barrier intrusion in the transmission channel, and find and prevent possible security risks in advance. The system design includes key steps such as data collection, model construction, feature importance evaluation and risk location marking. By collecting a large number of multidimensional data under different geographical and meteorological conditions, we have established a comprehensive training data set. Using RF algorithm, the system can learn multiple decision trees in the training stage, which improves the robustness and generalization ability of the model. The experimental results show that the system designed in this paper has obvious advantages over the traditional methods in identifying the dangerous sources of tree barrier intrusion in transmission channels. The design system can mark the potential risk position more accurately, and improve the real-time monitoring ability of transmission channel security state. The system is expected to become an important tool for power system safety management, which provides a new guarantee for the reliable operation of power system and the safety of personnel and property.