Thermographic Diagnostics of Overhead Power Lines Using Uav-Based Modeling and Simulation Methods

Introduction. The reliability of overhead power lines (OHL) is a key factor affecting the stability and safety of electric power systems. Traditional inspection methods such as ground-based visual surveys and helicopter patrols are limited by weather conditions, high labor costs, and restricted accessibility. The use of unmanned aerial vehicles (UAVs) provides new opportunities for fast, accurate, and safe diagnostics of high-voltage line components. Problem. Despite the growing use of UAVs in power engineering, existing approaches are mainly focused on visual inspection and do not ensure reproducible quantitative diagnostics. In particular, the integration of flight modeling with thermographic defect detection algorithms remains insufficiently studied, which limits the reliability of temperature anomaly identification on conductors and insulators. Aim. The purpose of this work is to develop and model an integrated UAV-based system for thermographic diagnostics of overhead power lines that ensures stable flight performance and high accuracy in identifying thermal defects under varying environmental conditions. Methodology. The study applies dynamic modeling of UAV motion in MATLAB/Simulink, taking into account aerodynamic and wind disturbances, and develops a thermographic image analysis algorithm based on temperature gradient comparison and adaptive thresholding. Experimental verification was carried out on test OHL sections with voltage levels up to 110 kV. The proposed model includes interrelated flight, sensor, and analytical subsystems. Results. Simulation results demonstrated stable UAV flight at wind speeds up to 5 m/s with trajectory deviations not exceeding 0.25 m. The thermographic algorithm achieved a 95% correct defect detection rate with an average temperature measurement error within ± 1.5°C. Inspection time of a 1 km line segment was reduced to about 9–10 minutes, which is three to four times faster than conventional methods. Scientific novelty. For the first time, UAV flight dynamics modeling and thermographic image processing are combined into a unified diagnostic framework, enabling quantitative assessment of the thermal condition of OHL components in real time. Practical value. The developed approach enhances the efficiency and safety of power line maintenance, providing a foundation for integrating UAV-based monitoring into predictive maintenance and digital asset management systems of power networks. References 20, table 5, Figs. 6.