Projects
Projects: Projects for Investigator |
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| Reference Number | NIA_NGTO034 | |
| Title | Environmental Exposure of Overhead Lines: Data Delivery for Physical Testing | |
| Status | Completed | |
| Energy Categories | Other Cross-Cutting Technologies or Research(Energy Models) 25%; Other Power and Storage Technologies(Electricity transmission and distribution) 50%; Other Cross-Cutting Technologies or Research(Environmental, social and economic impacts) 25%; |
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| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 25%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 10%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%; ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences) 15%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 50%; Systems Analysis related to energy R&D (Energy modelling) 50%; |
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| Principal Investigator |
Project Contact No email address given National Grid Electricity Transmission |
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| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 September 2019 | |
| End Date | 31 December 2020 | |
| Duration | ENA months | |
| Total Grant Value | £886,000 | |
| 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%) |
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| Web Site | https://smarter.energynetworks.org/projects/NIA_NGTO034 |
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| Objectives | This project will make use of several numerical models to generate environmental and asset health data for OHL assets, in a form that will subsequently allow verification by physical testing. These models include:Numerical weather prediction modelTurbulence intensity modelConductor icing modelAeolian vibration modelConductor galloping modelPollutant deposition modelCorrosion modelAssuming sufficient accuracy can be demonstrated, using desktop numerical models is a cost-effective approach for generating OHL environmental exposure information, when compared to destructive testing or extensive inspections. The results from these models may be compared to:Accelerated ageing experimentsCorrosion field measurements Past observed galloping eventsDue to the fact that many thousands of OHL assets are exposed to the environment for decades, time series data for each asset results in large volumes of data. Long-term averages are not detailed enough however, when comparing outputs to physical tests. A practical approach to delivering asset health data, whilst balancing these two considerations, will be determined in the final phase of this project. The project will deliver asset health data at span level, using localised simulated weather data. The asset health data will include:Aeolian vibration risk levelsConductor galloping risk levelsCorrosion risk levelsAsset life modifiersThe project will also investigate the key variables (beyond the outputs listed above) that may be required by other asset health modelling approaches, or when making comparisons to physical tests. Any variables idenfitied in this phase will be delivered in a suitable format, with practicality in mind, considering the potential overheads required to handle large volumes of data. To produce OHL asset health data that can be verified through physical testing. | |
| Abstract | One of the key factors affecting deterioration of overhead line (OHL) routes is the environmental conditions to which they are exposed. The two primary environmental factors which lead to OHL degradation are:• Conductor wear caused by motion of conductors due to wind input• Corrosion resulting from deposition of sulphur dioxide and chlorides (and local weather conditions)Asset health models exist for capturing the risk to assets due to the environmental factors above. Determining correlation between the model and existing weather and condition data sets has allowed approaches to asset management to be refined, however, in order to maximise the benefit from these models, there must be confidence that predictions from the model correlate with the conditions observed on the network. | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 09/11/22 | |
