Learnings

Outcomes

NGET provided a use case dataset to the supplier, University of Bath, containing NERs on faults linked to lightning strikes as captured by the delayed auto-reclosing switching scheme. The supplier then performed an initial deep-dive analysis utilising other credible and official lighting data platforms e.g., the Meteorological Office Lightning Electromagnetic Emission Location by Arrival time difference (LEELA) datasets, Earth Networks datasets, European Centre for Medium-Range Weather Forecasts (ECMWF), Copernicus Climate

Change Service, and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The case study analysis has already begun to yield decision-making supporting outcomes such as:

• Hourly surface temperature datasets allowing for more granular analysis of short-term variations
• Daily datasets (mean, minimum, and maximum temperatures) that provide aggregated values for long-term trend studies
• The monthly thunder hour data over the past 10 years (calculated mean annual thunder hours for 2015–2024) have been analysed, revealing that thunderstorms generally begin to increase in May, peaking in July and August, and subsequently decline from October onwards
• The hourly lightning activity data over the 10-year period (2015- 2024) reveals distinct spatial and temporal patterns in thunderstorm occurrences across the UK, showing that while lightning activity is generally low, certain areas exhibit higher frequencies of thunderstorms during specific times
• The southeast region of England consistently experiences the most intense and frequent thunderstorm activity, particularly during the late afternoon and evening hours, notably between 15:00 and 18:00
• The findings provide NGET with an improved evidence base for understanding how lightning risk varies 

TRL 

The project commenced at TRL 3, focusing on developing a fundamental understanding of lightning-related risks to transmission infrastructure, integrating multidisciplinary datasets, and investigating the feasibility of advanced machine learning and data analytics approaches. At this stage, the key outputs were new knowledge, methodologies, and analytical capabilities rather than operational solutions.

Through the course of the project, these concepts were progressively developed, validated, and translated into practical tools and decision-support capabilities. The project successfully reached TRL 7, demonstrating the technology and associated methodologies in an operationally relevant environment. This provided NGET with validated approaches to support network planning, asset management, and operational decision-making, enhancing its ability to identify, predict, and manage lightning-related risks.

Completion of the project at TRL 7 delivered a proven framework that can be integrated into business processes to support climate resilience planning, targeted investment decisions, improved fault attribution, and enhanced network reliability. Achieving TRL 7 represents a significant progression from early-stage research to successful operational demonstration, establishing a strong foundation for future deployment and long-term consumer and network benefits.

Total NIA Expenditure on project

Total project budget: £ 563,814. This comprises £478,656 allocated across project milestones, together with £29,079 of internal costs and £56,078 of indirect costs. 

Further recommendation of the work

Future work could focus on:

• Refining machine learning models through the incorporation of additional operational and environmental datasets.

·       Extending the analysis using newly available lightning and climate datasets to improve long-term trend assessments.

• Developing predictive lightning risk forecasting capabilities for operational use.
•  Enhancing asset-specific vulnerability assessments for different transmission asset classes.
•  Investigating the impact of future climate scenarios on lightning-related network risks and resilience requirements.
•  Validating the methodologies using a broader range of fault events and geographical regions.
  Developing user-friendly dashboards and decision-support tools to improve accessibility of the project outputs for planners, asset managers and operational teams.

Lessons Learnt

Providing a structured use-case dataset on lightning-related faults enabled rapid generation of meaningful insights through the integration of multiple credible external data sources. The analysis highlighted the value of combining high-resolution temporal data with geospatial and meteorological datasets to uncover clear seasonal, temporal, and regional lightning patterns across the UK, including increased activity during summer months and higher concentrations in the southeast. Importantly, linking lightning data with fault records proved both feasible and valuable for improving fault attribution and identifying key risk drivers. Overall, this work demonstrated the importance of interdisciplinary data integration in enabling more informed, risk-based decision-making, enhanced forecasting capability, and improved planning for network resilience and asset management.

Dissemination 

1.   A Dynamic Approach for Assessing Lightning Risk to Electricity Transmission Networks Considering Environmental Factors, Xinyuan He, Xue Bai, Xiaoyu Wang, Aisha Ali, Bhavya Shetty, Martin Fullerkrug and Chenghong Gu. Under review, CIGRE, Paris, 2026. 

2.  Multi-scale causal analysis of processes causing lightning and implications to energy infrastructure.

Xiaoyu Wang, Xinyuan He, Aisha Ali, Martin Fullekrug, and Chenghong Gu , EGU Conference, Vienna , 2026.

3.  Mingyi Xu, Xiushu Qie, Ye Tian, Martin Fullekrug, Chenghong Gu, Xue Bai, Shuqing Ma, Yan Liu, Chenxi Zhao, Xinyuan He, Bohan Li, Laiz Souto, Tinashe Chikohora, Douglas Dodds,  A Nowcasting Method for Severe Convective Weather Based on Array Radar and Lightning Jumps,  2025/4 , EGU General Assembly Conference Abstracts, Pages EGU25-9498

4.  Chenghong Gu, Martin Fullekrug, Xue Bai, Xinyuan He, Bohan Li, Xiaoyu Wang, Aisha Ali, Bhavya Shetty, KERAUnIC: An Innovative Project to Study the Impact of Lightning on UK Transmission Networks, Risk and Resilience Day 2026, University of Edinburgh
5.   Chenghong Gu, Xue Bai, Martin Fullekrug, Xinyuan He, Tinashe Chikohora, and Bohan Li, Lightning Risks to Electricity Assets Under Climate Change: UK Case Study, the 5th Macao International Conference on Smart City Technologies, University of Macau, 2025

6.   Xue Bai – Lightning Threats to Low-Carbon Energy Infrastructure: Mechanism, Hotspots, and Mitigations, CTR Wilson meeting 21 Nov 2024, Bath

7.   Impact of Lightning on UK Electricity Transmission System, CTR Wilson meeting 13 Nov 2025, poster, Bath

8.    Xue Bai (Bath): Lightning Climatology and Infrastructure Vulnerabilities in the UK. The MOAP Energy and Climate Strategic Forum: Energy Infrastructure workshop, Birmingham, 12 March 2025