What is the challenge that is being addressed?

In the EU the total length of overhead power lines reaches staggering 5.3 mln. km that need to be routinely inspected and maintained. However, current manual diagnostics practices are highly inefficient and prone to human error. Walking crews focus on reactive maintenance, are inaccurate and slow, while massive amount of data collected by aviation is simply impractical to be analysed by personnel. We estimate that in the EU these inspections demand 3.1 mln. man-hours annually. Despite all this environmental damage, time, and money put into ensuring reliability of the grid, the outages are still prevalent across the EU. We estimate that the reported 10+ thousand forced power outages in the transmission grid alone result in 7-10 bEUR in economic losses per year in the EU. Our objective is reduction of these outage-related economic losses, inspection cost, and environmental impact by providing a reliable AI-based tool for timely automatic defect detection in aerial imagery of transmission power grid. 

What is the AI solution the project implements?

THRUST-AID is based on deep-learning models for computer vision that are finetuned using professionally annotated ultra-high resolution aerial imagery of transmission grid. We use pre-trained detection, classification & segmentation models (transfer learning) within PyTorch framework as a basis for our customized analytics solution. THRUST-AID software is trained on diverse 24-100 MP oblique aerial imagery selected from the entire Lithuania’s transmission network, totalling more than 3 million photographs from 6500+  km of power lines. The software detects defects of pylon elements (insulator strings, wire clamps & spacers, vibration dampers, etc.), as well as extraneous objects within the protective zone (e.g., debris, illegal activity). Each detected defect is assigned a unique ID based on GIS metadata, then classified depending on severity & risk, and reported automatically via a user-friendly interface to enable predictive maintenance upon routine inspections.

Who helped implement this solution?

This THRUST-AID powerline diagnostics solution is part of a THRUST-AID project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016508.