This project aims to develop and test a scalable approach for assessing the external condition of pole‑mounted transformers using high‑resolution aerial imagery and automated visual analysis. By combining routine helicopter and drone image capture with computer‑vision techniques, the project will identify and classify visible condition indicators such as corrosion, oil leakage, structural condition, and bushing health, and convert these into standardised, auditable asset condition data aligned with CNAIM. The project will also compare the cost, efficiency, and suitability of helicopter versus drone‑based capture for this use case. The outcome will be evidence on how pole‑mounted transformer condition can be monitored more consistently and efficiently, supporting earlier intervention, improved network resilience, and readiness for ED3 regulatory reporting.
Benefits
PMT Scan will improve how NGED captures and uses condition information for pole-mounted transformers. It moves away from manual inspection methods that are time-consuming, subjective, and difficult to apply consistently at scale, toward a more structured and repeatable way of identifying deterioration. This will give the business a more reliable picture of corrosion, oil leakage, and external component damage across the fleet, with condition information properly linked to the transformer asset rather than recorded inconsistently or against the wrong asset record.
Improved condition data will give planners, engineers, and asset managers better evidence to work from. At present, condition information can vary depending on how it is observed and recorded, making it harder to compare assets, track deterioration over time, or identify which transformers need attention most urgently. A more consistent approach will support stronger asset histories, more reliable trend analysis, and a clearer basis for targeting maintenance and replacement activity where it is most needed. Rather than waiting for deterioration to become severe enough to cause failure, NGED will be better placed to prioritise intervention on assets showing the earliest signs of significant corrosion, leakage, or structural concern, particularly in coastal and higher-exposure areas where deterioration can progress quickly and where the case for earlier planned replacement is strongest.
The project will also strengthen regulatory readiness ahead of ED3. As PMTs become more important within CNAIM and NARM-related processes, NGED will need condition inputs that are robust, traceable, and auditable. PMT Scan is expected to improve the quality of those inputs, reduce the risk of incorrect condition assumptions being applied to assets, and support more defensible Health Index calculations and risk reporting.
On the operational side, the project will help reduce the manual effort involved in assessing PMT condition at scale. A more structured image-based approach will lower the time needed to turn inspection activity into usable condition data, improve consistency, and free up engineering resource for higher-value work. If parts of the process can be automated or semi-automated, that benefit grows further, reducing the cost and effort of large-scale condition assessment and making it more practical to keep condition data current across the full fleet.
Over time, that shift toward more targeted and evidence-based intervention is expected to reduce reactive works, lower emergency response costs, and improve the overall efficiency of how the PMT fleet is managed.
There are also wider reliability, safety, and environmental benefits. Earlier detection of corrosion, leakage, and structural deterioration will help reduce the likelihood of failures that could lead to customer interruptions, safety hazards, or oil-related environmental incidents. This is especially relevant in coastal and rural areas where environmental exposure may accelerate deterioration and where the consequences of failure can be harder to manage quickly.
Finally, the project will generate practical learning on how PMT condition can be captured, assessed, and used more effectively. That learning will support future rollout within NGED and could provide useful insight for other network licensees facing similar challenges, helping to establish a more consistent and scalable approach to PMT condition assessment across GB networks.
