Autonomous drone technology (AM12)

Business Need


SSE and Scottish Power are seeking to modernise and future-proof their inspection, fault detection, and surveying processes for critical energy infrastructure. Current manual inspection methods are labour-intensive, expensive, and pose significant safety risks - especially in remote, high-voltage, or hazardous environments such as offshore wind farms or rugged transmission line corridors. These challenges can result in extended downtime, reduced asset reliability, and higher operational costs. The rapid advancement of autonomous and Beyond Visual Line of Sight (BVLOS) drone technologies offers a unique opportunity to transform these activities, providing faster access to hard-to-reach sites, richer and more accurate data, and improved safety by reducing human exposure to risk. However, adoption is constrained by evolving regulatory requirements, technology integration challenges, and uncertainty over the most effective platforms and sensor payloads for varied operational contexts. This project will address these gaps, providing the technical, operational, and regulatory insight needed to enable confident, cost-effective, and scalable drone deployment.


Key partners

  • ScottishPower

  • SSE

The solution

The project conducted a targeted technical and legislative evaluation of autonomous and BVLOS drone applications for SSE and Scottish Power with the aim of advancing autonomous drone technology for energy infrastructure innovation and regulatory insights. This included analysis of platform designs, sensor capabilities, and environmental performance, alongside a review of UK CAA rules to identify barriers and enablers for adoption. Ultimately, expert input from the University of Strathclyde and SAMS Enterprise will shape strategic recommendations, guiding investment in future R&D, optimising compliance, and enabling efficient, low-risk, and cost-effective deployment of drones across energy infrastructure operations.

Next steps

  • A measurement campaign capturing functional failures, leveraging the incorporated data cleaning to reduce volume constraints.
  • Mesh with a predictive pipeline that will provide a complete solution from raw capture to inference.
  • Expand feature extraction in collaboration with vibration specialists.

Industry quotes

This project provided clear technical and regulatory insight into autonomous and BVLOS drone applications. The collaborative approach was helpful to help shape what are the utility needs regarding this new technology.

Luis I. de la Barba, SP Energy Networks