go to top scroll for more


Projects: Projects for Investigator
Reference Number NIA2_NGET0018
Title Autonomous Aerial, Thermal Inspections of Substations
Status Completed
Energy Categories Other Power and Storage Technologies(Electricity transmission and distribution) 100%;
Research Types Applied Research and Development 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 25%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 75%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
National Grid Electricity Transmission
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 May 2022
End Date 30 April 2023
Duration ENA months
Total Grant Value £572,000
Industrial Sectors Power
Region London
Programme Network Innovation Allowance
Investigators Principal Investigator Project Contact , National Grid Electricity Transmission (100.000%)
  Industrial Collaborator Project Contact , National Grid Electricity Transmission (0.000%)
Web Site https://smarter.energynetworks.org/projects/NIA2_NGET0018
Objectives NGET recognises that the challenges highlighted above could be more effectively and efficiently addressed by employing a repeatable and automated procedure, which would be capable of performing inspections with the same or even higher quality than a human, and at the same time, capable of delivering autonomous analysis to diagnose potential damages, defects, etc., so that asset health condition could be assessed and failures may be predicted.An automated drone inspection system, with incorporated image analysis methodology for generating inspection results in near real-time appears to be a viable option for such an automated procedure. With a drone-in-a-box solution, which is a purposed built system that is capable of automatic deployment, recovery and recharging of drones without the need for manual intervention, NGET would be able to inspect assets with much higher frequency and thus obtain a lot more data to enable condition-based asset management. This solution would remove the need for sending operatives to site to perform the same inspections and thus the time and costs related with such inspections could be reduced. The images and data collected by the drones would be uploaded to a cloud system for storage and analysis. AI (artificial intelligence) interfaced with the cloud system would use purpose-built image recognition and analysis methodology with machine learning to process all the images collected by the drones and deliver asset assessments in near real-time. This would remove the need for time consuming manual analysis and provide insights to NGET in a much more timely fashion without the risk of data overload.Data Quality Statement (DQS):​The project will be delivered under the NIA framework in line with OFGEM, ENA and NGET internal policy. Data produced as part of this project will be subject to quality assurance to ensure that the information produced with each deliverable is accurate to the best of our knowledge and sources of information are appropriately documented. All deliverables and project outputs will be stored on our internal sharepoint platform ensuring access control, backup and version management. Deliverables will be shared with other network licensees through following channels:Closedown reports on the Smarter Networks Portal.Measurement Quality Statement (MQS): ​The methodology used in this project will be subject to suppliers own quality assurance regime. Quality assurance processes and the source of data, measurement processes and equipment as well as data processing will be clearly documented and verifiable. The measurements, designs and economic assessments will also be clearly documented in the relevant deliverables and final project report and will be made available for review.In line with the ENAs ENIP document, the risk rating is scored 6 = Low.TRL Steps = 1 (2 TRL steps)Cost = 2 (£500,000 - £1m)Suppliers = 1 (2 supplier)Data Assumption = 2 (Assumptions known but will be defined within project) The project is scoped into 4 phases. Phase 1: Core system engineeringRequirement captureTimeline planningWorkshop for stakeholdersPhase 2: System development and TrialVisual line-of-sight (VLOS) trial: A 6-month VLOS trial would be held in the Deeside Centre for Innovation (DCI). During this trial, the system would be tested in DCI under direct visual line-of-sight of a remote pilot using a pre-planned drone route, configured to acquire images of the assets from the same position and angle, ensuring that post-processing will be able to accurately determine the asset being inspected.Intelligent obstacle avoidance: This part of the project is to develop and demonstrate the capability of the drone to avoid obstacles in a substation environment intelligentlyDevelop BVLOS operating safety case and trial plan: This part of the project would involve working closely with the Civil Aviation Authority (CAA) to obtain an authorised Beyond visual line-of-sight (BVLOS) Operating Safety Case (OSC) and related licence.AI prototyping and producing preliminary results with trial data: Development of image processing and the AI model would take place simultaneously with the trial.Phase 3: BVLOS capability demonstrationTo obtain CAA approval for BVLOS operationTo demonstrate the full functioning system at DCIPhase 4: Capability roadmap and summary reportTo summarise all the learnings and evidence in the previous phases and produce a scalable model for larger BaU (business as usual) deployment The objective of this project is to assess and demonstrate an autonomous drone systems capability of performing condition monitoring surveys (CMS) in a transmission substation environment. The key aspects are:Validation of the drones ability of navigation in a substation environment, especially in avoiding planned and unplanned obstaclesValidation of the drones capability of taking normalised thermal and RGB images of substation assetsDevelopment and validation of an AI that is capable of processing the images collected and produce CMS result with high quality in near real-timeObtain BVLOS approval from the CAA to enable a full BVLOS trial of the whole system at DCI
Abstract This project aims to investigate and validate a drone-based, autonomous systems capability of carrying out thermal condition monitoring surveys for transmission substation assets automatically. A drone-in-a-box system will be installed at the Deeside Centre for Innovation (DCI) to demonstrate that such a system can fly Beyond-Visual-Line-of-Sight (BVLOS) missions and replace the current manual practices. This project will investigate and recommend the best practice of drone operation in a transmission substation environment. This project will also work closely with the Civil Aviation Authority to obtain the BVLOS licence and to fulfil other regulatory requirements for drone operation at DCI. This project will also produce a cloud-based AI model that will process the data and images collected by the drone and produce near real-time asset condition reports.
Publications (none)
Final Report (none)
Added to Database 14/10/22