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| Reference Number | NIA2_NGET0048 | |
| Title | Visual Inspection and Condition Assessment Platform for OHL Steelwork 2 (VICAP 2) | |
| Status | Started | |
| 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) 50%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%; |
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| 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 November 2023 | |
| End Date | 30 April 2025 | |
| Duration | ENA months | |
| Total Grant Value | £321,110 | |
| 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_NGET0048 |
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| Objectives | n the previous innovation project (NIA_NGET0009), the ability of an AI model to detect corrosion and apportion the corrosion to their respective position on a tower was demonstrated. The AI models will be extended as part of this project to be able to not only identify corrosion but to grade it according to NGET standards. Historic steelwork imagery will be assessed to determine corrosion rates over the last decade for towers across the network. This will be combined with atmospheric and weather data to create a system which is able to forecast a tower"s future state, presenting plans and scenarios for the network. The first phase of the project will entail the collection of data comprising of images, historic atmospheric condition around steel works, manual assessment information of steel works and automated assessments by the AI. The combination of these will create an updated AI model and subsequently generate corrosion grading of the different parts of the steel tower using all the data sources highlighted.In the second phase of the project, dynamic models of the future state of the steel tower will be created in the AI. The model will take as input the network constraints, cost and corrosion grading and with this, generate future scenario for levels of corrosion and spend.The project will enable transmission operators to maintain the steelwork of towers to the same or better standard with reduced assessment costs and reduced number of surveys through more targeted surveys.Key deliverables for the project are:Data Aggregation & Collection;Normalised manual assessments;Deep Steel Augmentation;Forecasting Model;Scenario Engine;Publication and Dissemination. The objectives of the project are:Reduce the cost, improve the assessment consistency and increase the speed of steel tower assessments using machine learning and AI;Combine the more consistent AI powered assessments with atmospheric / climate data to help develop a forecasting model for steel tower corrosion;Develop a painting strategy modelling algorithm that builds on the forecasting model to generate accurate future network condition scenarios (painting and replacement). | |
| Abstract | VICAP 2 builds on the success of VICAP. In VICAP, drones recorded success in automatically capturing asset condition for use in condition-based asset maintenance. In VICAP 2, a refined artificial intelligence (AI) model will be adopted to automatically process the asset data and grade the steelwork across the tower. As manual processing of the data will be removed from the system, there will be savings on time and cost. As a result, the AI will provide efficient, reliable and consistent output / recommendations. In addition, the AI model will predict future asset condition and make reports on recommendations for painting / replacement of steel work. | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 02/10/24 | |
