Dr Bruce Stephen

Publications

Forecasting the remaining useful life of filters in nuclear power plants

Young Andrew, Devereux Michael, Brown Blair, Stephen Bruce, West Graeme, McArthur Stephen

Nuclear Technology (2024)

https://doi.org/10.1080/00295450.2024.2342187

Photometric stereo data for the validation of a structural health monitoring test rig

Blair Jennifer, Stephen Bruce, Brown Blair, McArthur Stephen, Gorman David, Forbes Alistair, Pottier Claire, McAlorum Jack, Dow Hamish, Perry Marcus

Data in Brief Vol 53 (2024)

https://doi.org/10.1016/j.dib.2024.110164

Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection

Higgins Martin, Stephen Bruce, Wallom David

IET Cyber-Physical Systems: Theory & Applications Vol 8, pp. 257-271 (2023)

https://doi.org/10.1049/cps2.12057

Probabilistic assessment of community-scale vehicle electrification using GPS-based vehicle mobility data : a case study in Qatar

Fan Fulin, Bayram I Safak, Zafar Usman, Bayhan Sertac, Stephen Bruce, Galloway Stuart

IEEE Open Journal of Vehicular Technology Vol 4, pp. 796-808 (2023)

https://doi.org/10.1109/OJVT.2023.3323626

Machine learning explanations by design : a case study explaining the predicted degradation of a roto-dynamic pump

Amin Omnia, Brown Blair, Stephen Bruce, McArthur Stephen, Livina Valerie

NDT 2023 - 60th Annual British Conference on NDT (2023)

Course correction : An internal project report summarising the concept, methodology and implementation

Tsioumpri Eleni, Bukhsh Waqquas, Stephen Bruce, Bell Keith

(2023)

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Research Interests

My main research focuses on developing and applying data driven methods to industry problems, often where there is limited data or a lack of domain knowledge. These applications have been right across power systems from generation (nuclear, solar and wind), through networks (both transmission and distribution) out to end use and supply. The resulting software solutions have been used to automate and predict condition assessment, detect anomalous conditions and simulate future behaviours to support asset management and future planning business objectives. Translating this to other fields, I was co-founder of the spin out company Silent Herdsman Ltd, who developed an intelligent precision livestock management platform for the dairy industry.

I am currently principal investigator on the EPSRC funded Analytical Middleware for Informed Distribution Networks (AMIDiNe) project working to develop data driven power systems models that provide a more accurate view of uncertainties surrounding loads on unmonitored distribution networks in order to better inform where barriers to Net Zero may exist. Previously, I was an investigator on the EU FP7 Orchestrating Renewable Integrated Generation in Neighbourhoods (ORIGIN) project and co-investigator on the EPSRC BuildTEDDI Aging Population Attitudes to Sensor Controlled Home Energy (APAtSCHE), Aggregators as diGital Intermediaries in Local Electricity markets (AGILE) and Transactive Energy Supply Arrangements projects. 

Professional Activities

QFF Quarterly Forecasting Forum, June 2018

Organiser

8/6/2018

Invited Talk - New Thames Valley Vision Workshop

Invited speaker

18/9/2014

Invited Talk - SGEM Load Modelling Workshop

Speaker

8/5/2014

External Referee - Research Foundation Flanders (FWO)

External Examiner

2/2014

Hisatoshi Ikeda

Host

31/1/2014

Journal of Statistical Computation and Simulation (Journal)

Peer reviewer

1/2014

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Projects

DTP 2224 University of Strathclyde | Cassidy, Maria

Stephen, Bruce (Principal Investigator) McArthur, Stephen (Co-investigator) Cassidy, Maria (Research Co-investigator)

01-Jan-2023 - 01-Jan-2027

EPSRC IAA: Predictive Analytics for Mining Assets

Stephen, Bruce (Co-investigator) Stewart, Brian (Research Co-investigator)

01-Jan-2022 - 31-Jan-2025

Control Room Analytics

MacKinnon, Calum (Researcher) Telford, Rory (Researcher) Stephen, Bruce (Principal Investigator) Jennett, Kyle (Administrator) Blair, Jennifer (Researcher)

01-Jan-2021

Supporting Analytics for Nuclear Asset Data Lifecycle (Jennifer Blair)

Stephen, Bruce (Principal Investigator) Brown, Blair David (Co-investigator)

01-Jan-2020 - 30-Jan-2024

Supporting Analytics for Nuclear Asset Data Lifecycle (Jennifer Blair) | Blair, Jennifer

Stephen, Bruce (Principal Investigator) Brown, Blair David (Co-investigator) Blair, Jennifer (Research Co-investigator)

01-Jan-2020 - 01-Jan-2024

Analytical Middleware for Informed Distribution Networks (AMIDiNe)

Stephen, Bruce (Principal Investigator) Browell, Jethro (Co-investigator) Galloway, Stuart (Co-investigator) Wallom, David (Co-investigator)

The programme of research that constitutes AMIDiNe will devise analytics that link point measurement to whole system to address the increasingly problematic management of electrical load on distribution networks as the UK transitions to a low carbon energy system. Traditionally, distribution networks had no observability and power flowed from large generation plant to be consumed by customers in this 'last mile'. Now, and even more so in future, those customers are generators themselves and the large generators that once supplied them have been supplanted by intermittent renewables. This scenario has left the GB energy system in position where it is servicing smaller demands at a regional or national level but faces abrupt changes in the face of weather and group changes in load behaviour, therefore it needs to be more informed on the behaviour of distribution networks. The UK government's initiative to roll out Smart Meters across the UK by 2020 has the potential to illuminate the true nature of electricity demand at the distribution and below levels which could be used to inform network operation and planning. Increasing availability of Smart Meter data through the Data Communications Company has the potential to address this but only when placed within the context of analytical and physical models of the wider power system - unlike many recent 'Big Data' applications of machine learning, power systems applications encounter lower coverage of exemplars, feature well understood system relations but poorly understood behaviour in the face of uncertainty in established power system models.

AMIDiNe sets out its analytics objectives in 3 interrelated areas, those of understanding how to incorporate analytics into existing network modelling strategies, how go from individual to group demand behavioural anticipation and the inverse problem: how to understand the constituent elements of demand aggregated to a common measurement point.

Current research broadly involving Smart Metering focuses on speculative developments of future energy delivery networks and energy management strategies. Whether the objective is to provide customer analytics or automate domestic load control, the primary issue lies with understanding then acting on these data streams. Challenges that are presented by customer meter advance data include forecasting and prediction of consumption, classification or segmentation by customer behaviour group, disambiguating deferrable from non-deferrable loads and identifying changes in end use behaviour.

Moving from a distribution network with enhanced visibility to augmenting an already 'smart' transmission system will need understanding of how lower resolution and possibly incomplete representations of the distribution network(s) can inform more efficient operation and planning for the transmission network in terms of control and generation capacity within the context of their existing models. Improving various distribution network functions such as distribution system state estimation, condition monitoring and service restoration is envisaged to utilise analytics to extrapolate from the current frequency of data, building on successful machine learning techniques already used in other domains. Strategic investment decisions for network infrastructure components can be made on the back of this improved information availability. These decisions could be deferred or brought forward in accordance with perceived threats to resilience posed by overloaded legacy plant in rural communities or in highly urbanised environments; similarly, operational challenges presented by renewable penetrations could be re-assessed according to their actual behaviour and its relation to network voltage and emergent protection configuration constraints.

01-Jan-2019 - 30-Jan-2022

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