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Projects: Projects for Investigator
Reference Number NIA2_NGET0009
Title Visual Inspection and Condition Assessment Platform for OHL Steelwork (VICAP)
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) 10%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 90%;
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 April 2022
End Date 30 September 2023
Duration ENA months
Total Grant Value £430,000
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_NGET0009
Objectives Through this project, NGET aim to mesh technologies such as autonomous drone flights, Light Detection and Ranging (LiDAR) and Artificial Intelligence (AI) to create a new automated end-to-end process for condition assessment of steel lattice towers. The end-to-end process will include capturing, processing, and presenting condition information, particularly corrosion of OHL tower steelwork. To achieve this, following technical gaps will be investigated and solved to arrive at an end-to-end process:Collecting 3d lidar and 2d image data in a way that allows the 2d images to be mapped to specific points in the 3d worldFast and automated data collection, without any requirement for beacons / reference markersAligning 3D and 2D data so regions of interest can be localised in 3D spaceManaging 2D & 3D data and automating handoff from collection to processing and presentation platformsDevelopment of algorithms to split foreground steelwork from backgroundDevelopment of algorithms to assess corrosion on steel bars using supplied dataDisplaying results via a web based application Data Quality Statement (DQS): The project will be delivered under the NIA framework in line with OFGEM, ENA and NGGT / 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. Relevant project documentation and reports will also be made available on the ENA Smarter Networks Portal and dissemination material will be shared with the relevant stakeholders.   Measurement Quality Statement (MQS): ​ The methodology used in this project will be subject to our 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 Low.TRL Steps = 1 (2 TRL steps)Cost = 1 (~ £430k)Suppliers = 1 (2 suppliers)Data Assumption = 2 (Assumptions known but will be defined within project) Following are the scope elements in this project:1. Design and Planning: This involves developing the understanding around expected operating conditions for drones, data gathering requirements, operational requirements, selection of towers for reference as well as demonstration flights, etc. Following elements will be covered:a. Requirements and Use Case Definition b. Options Selection and Evaluationc. Reference Towers Selectiond. Design of an Integrated Approache. Candidate Algorithm Selection2. Drone System Development: This involves customising and updating drone, drone platform and Ground Control Station (GCS) to meet requirements for flight around OHL towers. Following scope elements will be covered:a. Systems Developmentb. Operations Process Development for BVLoS permissions c. Demonstration Flights3. Iterative Data Collection: This involves carrying out iterative flights to collect data, feed into the processing platform and optimise the end-to-end processto be developed in the project. At the end of each flight, feedback and lessons will be incorporated into systems, algorithm, and presentation development items (scope items #2, 4 and 5 in this list respectively). Following flights will be carried out: a. Flight 0b. Flight 1c. Flight 2d. Flight 34. Algorithm Refinement and Development: This involves developing algorithms on the data processing and presentation platform. This segment will continue throughout the project, taking feedback from iterative drone flights planned in scope element #3. Following elements will be covered:a. Localization: Assignment of 3D locations to 2D images capturedb. Segmentation: Separating background from steelwork elements captured in the imageryc. Corrosion Detection and Evaluation: Quantification of level of corrosion using NGETs grading mechanism5. Presentation: This involves development of the steps required in the end-to-end process for automated OHL steelwork condition assessment including managing data transfer from collection to processing platform, processing steps, and visualisation. Following elements will be covered: a. Data Ingestion Pipelineb. Processing Pipelinec. Presentation Layer6. Readiness for Deployment at Scale: This involves development and documentation required to transfer deployment-ready project outcomes into BAU. It is anticipated that updated algorithms will be deployment-ready at the end of the project and can be deployed into the existing data processing platform. However, drone and drone control system would be in prototype stage and can be brought to deployment ready stage in 12-24 months. Following elements will be covered:a. BVLoS Permissions b. Costed Business Casec. Implementation Pland. Final Reports and Presentation  This project aims to test the feasibility of and build an end-to-end process for collecting, uploading, and processing visual data for an OHL steel tower to assess the extent of corrosion present on the tower steelwork. The process should be able to:Capture high definition images of OHL steelwork through drone flightsLocalise the captured imagery by automatically mapping 2D images to the 3D structureAnalyse the captured imagery, and assign correct grading based on the corrosion presentCollate all assessments to generate necessary reporting (as per regulatory requirements) along with recommendations for painting/replacement
Abstract The innovation project in RIIO-T1: NIA_NGET0215 proved the feasibility of automating Overhead Lines (OHL) Steelwork corrosion assessment using multi-spectral and RGB (Red-Green-Blue) imaging combined with clustering algorithms to grade the extent of corrosion. The feasibility of the method was proven using physical steelwork samples and towers in pre-decided locations such as substations, and test facilities. To move towards an end-to-end solution that is suitable for BAU use, the automation needs to include the capability to classify collected imagery and assign the images to the right section of the tower. This project aims to test the feasibility of and build an end-to-end process for collecting, uploading, and processing visual data for an OHL tower steelwork by combining autonomous drone flights with automated data processing platform.
Publications (none)
Final Report (none)
Added to Database 14/10/22