Bath Engineering & Design Studentships: Physics-Informed Grey-Box Modelling for Oscillation Diagnosis in Future GB Power Networks

An alternative start date may be possible if agreed with your intended supervisors and the Doctoral College.

Project Background:

This PhD project at the University of Bath offers an opportunity to combine power-system engineering and physics-informed machine learning to address a challenge in the transition to clean electricity: diagnosing oscillations in networks with high renewable penetration.

Great Britain's electricity system is increasingly reliant on inverter-based resources (IBRs), including wind, solar, batteries and HVDC interconnectors. Their fast controllers can interact with the network to produce poorly damped oscillations, threatening equipment and security of supply. Identifying the mechanisms and principal contributors is essential for effective mitigation and secure renewable integration.

However, proprietary IBR controls and vendor-specific black-box models restrict access to the internal dynamics needed for conventional white-box analysis. Purely data-driven methods offer an alternative, but can lose accuracy and robustness when measurements are noisy, incomplete or collected under unfamiliar operating conditions.

The project will develop a model-informed grey-box diagnostic framework that combines measurements with reliable partial knowledge of the system. Network topology and parameters, physical constraints, reduced-order device models and known oscillation signatures will inform and constrain machine learning. The research will investigate how embedding this knowledge can significantly improve estimation accuracy, convergence speed and robustness while reducing data requirements.

The research will focus on three linked objectives:

• Generate and analyse different oscillation types using simulations and model-based analysis, identifying their mechanisms, dynamic features and the knowledge available to guide learning.
• Develop grey-box methods to estimate oscillation frequency, damping and mode shape, distinguish contributing plants from affected equipment, and support root-cause diagnosis.
• Validate performance on benchmark systems and GB-representative scenarios, using simulations and real-event measurements where available. Comparisons with white-box and purely data-driven methods will assess robustness to noise, limited measurements, topology changes, unseen operating conditions and multiple simultaneous modes.

The successful candidate will develop expertise in power-system dynamics, machine learning and system identification. Expected outcomes include scalable algorithms, documented software, benchmark datasets and a reproducible post-event investigation workflow, with potential extensions to online monitoring and early warning. The research will support targeted mitigation and help reduce avoidable renewable curtailment, contributing to the UK's 2050 net-zero target and the Faculty of Engineering & Design's research in sustainable energy systems.

Candidate requirements:

Applicants must have, or be about to obtain, a UK Honours degree 1st or 2.1, or international equivalent.

Applicants should hold, or expect to receive, a master's degree (MSc or equivalent) in Electrical and Electronic Engineering or a closely related discipline. The project would suit a motivated applicant with an interest in power-system stability, renewable energy integration and machine learning. Previous experience in power systems, machine learning or data-driven modelling would be advantageous. Familiarity with MATLAB or Python would also be beneficial.

Non-UK applicants, who are not currently studying in the UK, must meet the programme's English language requirement before the application deadline – no exceptions will be considered.

Informal enquiries are encouraged. Direct these to Dr Youhong Chen.

Please follow the below instructions carefully.

You must make a formal application via the University of Bath's online application form for a PhD in Electronic and Electrical Engineering.

Please note that you can apply for a maximum of two PhD projects on this programme.

In the 'Funding Your Studies' section, you must select 'University of Bath URSA' from one of the drop-down menus.

In the 'Your PhD project' section, you must quote the project title in the PhD project title field, and you must quote the lead supervisor's name in the field 'Name of intended supervisor at University of Bath.'

If you are applying for two projects, you must quote the project title for your second choice project in the field 'Project 2 Title' and the lead supervisor's name in 'Name of intended supervisor for 2nd choice project at University of Bath.

You must ensure that you follow the above steps correctly. Failure to complete these steps will cause errors in the automated processing of your application and may mean that you are not considered for a particular project.

Equality, Diversity and Inclusion

We value a diverse research environment and strive to be an inclusive university, where difference is celebrated and respected. We encourage applications from under-represented groups.

If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.

The Disability Service ensures that individuals with disabilities are provided the support that they need. If you state if your application that you have a disability, the Disability Service will contact you as part of this process to discuss your needs.

Keywords

Power system stability, power system dynamic, renewable energy integration, inverter-based resources, oscillation diagnosis, subsynchronous oscillations, grey-box modelling, physics-informed machine learning, artificial intelligence, data-driven modelling.

Funding Notes

Applicants will be considered for a studentship. The Faculty of Engineering and Design at the University of Bath has studentships available for Home and Overseas students. However, in line with UKRI policy, the number of studentships available to Overseas students are capped. Studentships cover tuition fees, provide a £1k per annum training support fee, and a maintenance stipend (£21,805 2026/27 rate). International students will be required to cover the cost of a Student visa and Immigration Health Surcharge from personal funds.