Integration of drone and machine learning technology for predicting power infrastructure faults efficiently

Abstract

• Proposes a fully autonomous UAV-based system for power infrastructure inspection and fault prediction, achieving a precision of 89.76 %. • Introduces a mathematical model that enhances system robustness and reduces reliance on specific datasets. • Utilizes YOLO V8 deep learning for insulator fault detection, achieving a box loss of 0.525 and a classification loss of 0.3887 as a proof of concept. • Demonstrates proactive fault prediction, enhancing power network reliability and reducing maintenance costs. • Visualizes risk levels using a color-coded system, providing actionable insights for proactive maintenance. Power transmission and distribution networks frequently face issues, especially in harsh environments, leading to high maintenance costs and the need for uninterrupted electricity. Current field inspections by skilled personnel are labor-intensive, costly, and slow, often lacking efficiency and posing safety risks. While automated helicopters, flying robots, and climbing robots have been explored for visual inspections, the widespread adoption of automatic vision-based inspection remains limited due to high accuracy demands and unique challenges. This highlights the need for a fully autonomous vision-based system to inspect electrical power infrastructure and predict potential future faults. This research introduces Unmanned Aerial Vehicles (UAVs) as a promising solution for infrastructure inspection, deep learning for data analysis and prediction, and a mathematical model to ensure system accuracy doesn't rely solely on the dataset. As a proof of concept, YOLO V8 was employed to predict electrical faults in insulators, achieving a box loss of 0.525, a classification loss of 0.3887, and a precision of 0.8976, demonstrating high accuracy and low loss.