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| Reference Number | NIA2_NGET0055 | |
| Title | Knowledge Elicitation of Risks to Assets Under LightNing Impulse Conditions (KERAUnIC) | |
| 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 | ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%; ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences) 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 March 2024 | |
| End Date | 30 September 2025 | |
| Duration | ENA months | |
| Total Grant Value | £558,656 | |
| 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/NIA2_NGET0055 |
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| Objectives | The initial project aims will be achieved by connecting data from multiple interdisciplinary domains and by applying novel machine learning and data analytics approaches. Lightning flashes could be used as a proxy measure for severe weather, which is thought to become more important as part of global warming. Lightning was recently elevated to become an essential climate variable by the World Meteorological Organisation (WMO). This means that lightning data will be collected and archived, including thunder hour data provided by ground-based lightning detection networks and earth-observing satellite missions. New thunder hour data from Earth Networks with approximately 5.5 km spatial resolution will be used. There shall be use of the data describing extreme events collected in project WELLNESS (NGET/Whole Energy System Resilience Vulnerability Assessment/SIFIESRR/Rd2_Alpha,ENA Innovation Portal (energynetworks.org)), and data from flood sensors installed under ERA (Environmental Risk and Assurance (ERA),ENA Innovation Portal (energynetworks.org)). The project understands the importance of sharing data and knowledge, thus it will ensure that the data, results, models, and other key outputs are compatible to best practise standards to match other networks projects platforms/standards like in CReDo+ Climate Resilience Demonstrator (extension to new climate risks),ENA Innovation Portal (energynetworks.org). Historic lightning data will be used to predict climate trends whilst real-time satellite observations will be used to by using innovative machine learning methods. Particular emphasis will be placed on quantifying and discriminating the main atmospheric processes driving climate change, such as the North Atlantic Oscillation, Madden Julian Oscillation, and the El Nino Southern Oscillation. The results of these novel analyses of actual lightning observations will finally be compared to the numerical simulation of climate trends of lightning reported in recent studies e.g., Kendon et al., UKCP 2023, Suoto et al., 2023a & 2023b, Kahraman et al., 2022.National Grid"s Transmission Entry Capacity (TEC) register provides a list of projects that hold contracts for existing and future connection projects. Further, National Grids" Infrastructure projects provide information regarding the project to be delivered. Complimentary datasets will be used to build a network that can be the best representative for the future so as to detect hotspots and most vulnerable locations from lightning strikes.Historical lightning strike events and failure data provided by NGET will be used to build the lightning-infrastructure relationship and a scheme developed to differentiate non-lightning failures. There are some existing models available in the literature that describe how lightning strikes affect various electricity assets. Parameters of existing lightning strike models will be tuned to reflect the specific features of assets of various (operational) types, locations and complement trained models that look at historical events with limited asset data.Quantum Geographic Information System (QGIS), a free and open-source cross-platform application that supports viewing, editing, printing, and analysis of geospatial data will be used to build/demonstrate the proposed capabilities. Work package 1: Forecasting of regionally climatology thunderstorms and lightning hazard.Develop a regionally specific climatology of short-term and long-term thunderstorm and lightning hazards, including all types of lightning, such as cloud-to-ground and in-cloud lightning. The results will be provided in Excel spreadsheets and QGIS shapefiles. Milestone 1: short-term lightning hazard forecastingMilestone 2: long-term lightning hazard mappingWork package 2: The mapping of UK electricity infrastructure.Create a map of the UK electricity infrastructure. This map will include existing assets, those to be built, and those potentially to be built. The results will be provided in Excel spreadsheets and QGIS shapefiles. Milestone 3: existing NGET infrastructure mappingMilestone 4: future NGET infrastructure mappingWork package 3: Understanding and modelling the impact of lightning strikes.This work package we will build models by combining historical events and existing models, to assess how lightning would affect different network assets. Data analyses will be designed for recorded data to identify the relationship between lightning strikes and their impact on assets. Milestone 5: Data fusion for model buildingMilestone 6: Data analytics for impact identification Work package 4: Assessment of the impact of lightning strikes on infrastructure and system.This work package will present the assessment of consequences on electricity assets and system-level supply. Metrics will be designed to identify the hot spots of lightning across geospatial scales under different energy pathways and climate scenarios. This will be delivered in the form of an Excel spreadsheet. Recommendations for improving the protection of existing assets and heatmaps for future lightning threats will be delivered in a report. Milestone 6: Impact assessment on assetsMilestone 7: Impact assessment on system supplyWork package 5: A QGIS-based visualisation platform A QGIS-based or equivalent platform will be developed to integrate and visualise the data and results from work packages above.Milestone 7: QGIS platform for analysis and visualisation Assess past and future lightning trends in the UK to estimate the uncertainty of the predictions to develop novel lightning (climate) model corrections and analyses.Conduct secondary lightning forecasting.Map existing & future UK electricity infrastructure under different pathways of net zero for long-term lightning strike (hotspots) risk assessment.Use the emerging real-time lightning data from lightning imager on board the geostationary Meteosat Third Generation (MTG) satellite to produce short-time lightning predictions.Use MTG satellite in geostationary orbit for the novel lightning imaging sensor to quantify lightning in real time and thereby enable novel monitoring of weather and climate variability, with unprecedented precision (commissioning phase expected in spring 2024).Combine and check any corelation between synchronized local environmental data and failure data.Use machine learning and other mathematical techniques (such as wavelet-based decomposition) to analyse the recorded current and voltage fault profiles even where a Delayed Auto Reclose (DAR) action is triggered without supply interruption.Conduct a system-level analysis considering the failure of key assets, scenarios of supply, demand, and weather conditions, to identify potential consequences particularly when the system is under stress.Design a set of metrics (categorised by infrastructure type, consequences, severity, probabilities) to measure the severity (heat map) of asset/system impact.Develop/upgrade a geographical information systems (GIS) platform to integrate developed/novel geospatial data.Integrate other platforms such as ArcGIS ensuring flexibility for users to analyse lightning strike impact under various scenario and conduct statistical analysis through Python Console to visualise results interactively.Inform hardening existing towers and grids by investigating their protection schemes, measures and standards, and assess whether they are sufficient for protecting these assets against future lightning threats. Inform resilient and robust system planning, by providing knowledge and hotspots in future lightning in the GB under different scenarios to influence selection on appropriate materials, topology, materials, network layouts, lightning schemes etc.Inform new lightning protection standards based on how future lightning types, distributions, probabilities compare with existing standards such as BS EN 62305. | |
| Abstract | There is an urgent need to understand, quantify, and assess the lightning risk threat on the fast-expanding electricity network assets. Previous related projects focused on real-time fault management for distribution networks offering limited transmission level insights. Yet, growing evidence has it that climate change is influencing lightning, in terms of formation, severity, patterns, frequency, and distribution. This project aims to develop novel strategies to assess lightning risks for NGET transmission infrastructure assets considering past climatological data and adding superimposed long-term climate site-specific trend projections. Because climate change influences lightning occurrence and patterns, the project can inform the design and location of new energy infrastructure, ensuring appropriate lightning protection. The project will improve system planning, regulatory compliance, lead to reduced damage, downtime and maintenance costs. | |
| 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 | |
