Autonomous Site Inspection of Power Transmission Line Insulators with Unmanned Aerial Vehicle System

The inspection of overhead power transmission line and assets is an essential aspect to improve the overhead power transmission efficiency and to ensure an uninterrupted power supply. This article has mainly focused on the progression and fabrication of indigenous quadcopter/Unmanned Aerial Vehicle (UAV) for carrying autonomous operations in a coordinated movement along the overhead transmission towers for capturing the images and videos of transmission insulators and assets. A custom based dataset of power line insulators is created by using the quadcopter for overcoming the data scarcity and to perform Deep Learning (DL) assessment for (i) inadequate data for training and (ii) power line insulator detection and faults. The experimental results showcase that, the suggested DL architecture identifies power line insulators and associated faults, such as cracks, broken disk and missing top caps etc. With a detection speed of 56.8 frames/sec and an accuracy of 94.1%, the proposed DL technique has much promise for intelligent examination of power grid insulators. Ecological Footprint assessment of different power line inspection methods are also examined in this study.