Optimizing Powerline Infrastructure Inspection, Monitoring And Asset Management Using UAVs And Artificial Intelligence Techniques

Abstract

Ensuring the reliability of powerline infrastructure requires efficient inspection and maintenance. Traditional methods are labor-intensive, costly, and expose workers to risks. This project integrates Unmanned Aerial Vehicles (UAVs) with Artificial Intelligence (AI) to enhance powerline inspection and predictive maintenance. The system employs a Tello drone to capture high-resolution images, which are processed using OpenCV and analyzed by a Convolutional Neural Network (CNN) model to detect faults such as insulator cracks, conductor sagging, and corrosion. Findings demonstrated an 62% fault detection accuracy, with the AI model correctly identifying and classifying defects in powerline components. The system’s real-time monitoring capability significantly improved fault identification speed compared to manual inspections. Additionally, the asset management framework enabled proactive maintenance scheduling, reducing downtime by 40%. These findings highlight the potential of AI-UAV integration to improve infrastructure reliability, reduce costs, and enhance worker safety, making powerline management more efficient and sustainable.