Dr Bruce Stephen

Strathclyde Chancellor's Fellow

Electronic and Electrical Engineering

Personal statement

I am a Senior Lecturer and Strathclyde Chancellors Fellow in the Department of Electronic and Electrical Engineering. I joined the University in 1998 and came to work in the Department of Electronic and Electrical Engineering in April 1999. Previously, I had studied Aeronautical Engineering at Glasgow University at undergraduate level and then undertook postgraduate study in the department Computer Science at Strathclyde, gaining an MSc there in 1998 followed by a PhD in EEE 2005.


Impacts of measurement errors on real-time thermal rating estimation for overhead lines
Fan Fulin, Stephen Bruce, Bell Keith, Infield David, McArthur Stephen
IEEE Transactions on Power Delivery, pp. 1-11 (2022)
A Gaussian process based fleet lifetime predictor model for unmonitored power network assets
Jiang Xu, Stephen Bruce, Chandarasupsang Tirapot, McArthur Stephen DJ, Stewart Brian G
IEEE Transactions on Power Delivery, pp. 1-9 (2022)
Parameterisation of domain knowledge for rapid and iterative prototyping of knowledge-based systems
Young Andrew, West Graeme, Brown Blair, Stephen Bruce, Duncan Andrew, Michie Craig, McArthur Stephen DJ
Expert Systems with Applications Vol 208 (2022)
A quantile dependency model for predicting optimal centrifugal pump operating strategies
Stephen Bruce, Brown Blair, Young Andrew, Duncan Andrew, Helfer-Hoeltgebaum Henrique, West Graeme, Michie Craig, McArthur Stephen D J
Machines Vol 10 (2022)
Capturing symbolic expert knowledge for the development of industrial fault detection systems : manual and automated approaches
Young Andrew, West Graeme, Brown Blair, Stephen Bruce, Duncan Andrew, Michie Craig, McArthur Stephen
International Journal of Condition Monitoring and Diagnostic Management Vol 25, pp. 67-75 (2022)
Hybrid fault prognostics for nuclear applications : addressing rotating plant model uncertainty
Blair J, Stephen B, Brown Blair David, Forbes A, McArthur S
PHM Society European Conference 7th European Conference of the Prognostics and Health Management Society 2022, pp. 58-67 (2022)

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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
Invited Talk - New Thames Valley Vision Workshop
Invited speaker
Invited Talk - SGEM Load Modelling Workshop
External Referee - Research Foundation Flanders (FWO)
External Examiner
Hisatoshi Ikeda
IEEE Transactions on Power Systems (Journal)
Peer reviewer

More professional activities


Control Room Analytics
MacKinnon, Calum (Researcher) Telford, Rory (Researcher) Stephen, Bruce (Principal Investigator) Jennett, Kyle (Administrator) Blair, Jennifer (Researcher)
Supporting Analytics for Nuclear Asset Data Lifecycle | Blair, Jennifer
Stephen, Bruce (Principal Investigator) Brown, Blair David (Co-investigator) Blair, Jennifer (Research Co-investigator)
01-Jan-2020 - 01-Jan-2023
Supporting Analytics for Nuclear Asset Data Lifecycle
Stephen, Bruce (Principal Investigator) Brown, Blair David (Co-investigator)
01-Jan-2020 - 30-Jan-2024
Industrial Case Account - University of Strathclyde 2020 | Blair, Jennifer
Stephen, Bruce (Principal Investigator) Brown, Blair David (Co-investigator) Blair, Jennifer (Research Co-investigator)
01-Jan-2020 - 01-Jan-2023
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
Onyx Work Order System
McMillan, David (Principal Investigator) Salo, Erik (Co-investigator) Stephen, Bruce (Co-investigator)
08-Jan-2019 - 08-Jan-2019

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