An Intelligent Identification Strategy for Fault Types of Transmission Lines Through Unmanned Aerial Vehicle Inspection Based on Machine Learning Classification

Abstract

Power transmission lines, as the core of the power system, are prone to failures due to long-term exposure to outdoor environments. Traditional manual inspection and data analysis methods are unable to meet the requirements of intelligent operation and maintenance. This paper uses 1829 sets of transmission line fault data collected by drones, covering multiple features such as electrical and environmental aspects, and classifies them into five operational states and conducts variable correlation analysis. To address the problem of insufficient feature extraction and poor generalization of a single algorithm in fault identification, a TCN-Transformer algorithm that integrates local temporal feature extraction and global attention mechanism is proposed, and an end-to-end classification prediction framework is constructed. The data set is divided in a 7:3 ratio and core parameters are set. The results show that the model has an accuracy rate of 0.823 and an AUC of 0.972. It outperforms traditional machine learning and ensemble learning models such as decision trees, random forests, and XGBoost in multiple key indicators, effectively improving the accuracy and stability of transmission line fault identification in complex scenarios, and providing a feasible technical solution for intelligent fault discrimination in drone inspections.