Analysis of Small Wind Turbine Inspection via Autonomous Drone

This paper presents an autonomous drone-based inspection system designed to improve the accuracy, efficiency, and safety of small wind turbine inspections. Our approach integrates real-time computer vision algorithms, object-based distance estimation, and optimized flight path strategies to facilitate precise turbine inspection. We used a distance estimation method based on similar triangles and computer vision-based bounding box analysis, enables the drone to maintain an optimal inspection distance without requiring expensive laser rangefinders. Furthermore, we introduce an adaptive arc-flight strategy that enables the drone to capture media from multiple angles, ensuring a comprehensive inspection of the wind turbine. We propose a real-time inspection method for static wind turbine blades, validated through initial testing on the Ryse micro-turbine and experimental evaluation on the Ryse E5 wind turbine. The experiments confirm the reliability of the system in real-world conditions. The results confirm the robustness and reliability of the system for autonomous turbine inspections, with an average distance estimation error of only 2%, demonstrating the effectiveness of the computer vision-based approach.