Enhancing the Resilience of Offshore Wind Electrical Systems through Digital Twin Tools

About the Project

The UK’s expansion of offshore wind is changing the dynamics of the electricity grid, particularly due to the lack of generation inertia worsening power system stability. Control of such a complex system relies on detailed understanding and real-time modelling of the nonlinear dynamics resulting from the interactions between the drivetrain, the electrical generator, and multiple power electronics converters and their control systems.

Developing control strategies for wind generation planning, real-time operation, control, fault detection and maintenance requires accurate models of every component which need to update with real-time measurements from the components creating a Digital Twin of the wind generation system. The digital twin (DT) concept, based on an accurate real-time simulation of the real system, has emerged as a powerful tool for condition monitoring and predictive maintenance.

DT are multi-physics, multiscale, high-fidelity simulations that emulate in real-time the state of a corresponding physical twin based on historical and real-time sensor data. The comparison of physical and virtual data throughout the product lifecycle can provide valuable information on the state-of-health of a physical structure. While extensive research is being undertaken on DTs for structural health monitoring in offshore wind, there is little if any application of the DT concept to electrical equipment. This is mainly due to the difficulties of multi-time scale modelling in the electrical domain where dynamics can range from sub-milliseconds transients following power electronics switching transient to thermal and mechanical induced gradual ageing and degradation taking place over the lifetime of the machine.

Using a combination of physics-based, and data-driven AI-based approaches employing neural-networks and machine learning, this project will develop and validate a multi-time scale DT concept for advanced condition monitoring and maintenance of permanent magnet generators and converters for offshore wind.

This project aims to develop a suite of Virtual Digital Twins for real-time simulation, control, and condition monitoring of the electrical powertrain, including rotor aerodynamics, structural dynamics, generator, converters, transformers, and filters. The impact will be improved system stability, enhanced maintenance, and reduced operational risk. The project will be supported by the Offshore Renewable Energy Catapult which will provide access to their validation turbines and industrially relevant data.

Training & Development

Your training will begin with an intensive six-month programme at the University of Hull, drawing on the expertise and facilities of all four academic partners. It is supplemented by Continuing Professional Development (CPD), which is embedded throughout your 4-year research scholarship giving you a broad understanding of the breadth and depth of current and emerging offshore wind sector needs.

As a postgraduate student based at the University of Sheffield, you will have access to a wide range of relevant courses in power electronics, motor drives, control and signal processing. Specialised training on relevant software will be made available (FPGA programming, Real-time modelling tools e.g. Opal-RT, Speedgoat, Typhoon HIL etc.)

Entry Requirements

If you have received a First-class Honours degree, or a 2:1 Honours degree and a Masters, or a Distinction at Masters level with any undergraduate degree (or international equivalents) in Computer Science, Engineering, Physics, or Mathematics and Statistics, we would like to hear from you.

If you have not previously been educated in English, you will be required to provide evidence of your English language ability. We require an IELTS (or equivalent) score of 7.0 overall, with no less than 6.0 in each skill.

Guaranteed Interview Scheme

We offer a Guaranteed Interview Scheme for home fee status candidates who identify as Black or Black mixed or Asian or Asian mixed if they meet the programme entry requirements. This positive action is to support recruitment of under-represented ethnic groups to our programme and is an opt-in process.

How to Apply

Please familiarise yourself with the AURA CDT website before you apply. The Frequently asked questions page and Candidate resources page are essential reading.

As part of the recruitment process, please submit a short 5-minute film of you delivering a presentation on “How do your experiences and qualities provide a background to contribute to research and innovation for the project you have applied for”.

You will be assessed on the content of your presentation, not your film editing skills, but please film in an appropriate, quiet location. The presentation could be a slide presentation with voice over, or you may wish to present simply talking to the camera, use the method you are most comfortable with. Please use tools and technology that are accessible to you e.g. your mobile phone, Keynote or Powerpoint.

You may only apply for ONE project offered through the CDT, via this page. After filling in your personal details, please select ‘Doctoral Training Course’ as the qualification you are applying for, and ‘AURA II CDT’ for the specific doctoral training course.

Please upload the following to your application:

• Complete transcripts and degree certificates (where possible). The documents should be provided in English and the original language.
• Your CV.
• A completed Supplementary Application Form. This includes space for you to provide a link where the shortlisting panel may view your film.

Save the supplementary application form as a pdf, labelled: Last name_first name PhD application form, and upload it when asked to add your Supporting Statement. Do not send your form directly to the CDT.

Interviews

First-round interviews will be held online during early to mid-February 2026. The interview panel will comprise the project supervisors and a CDT representative. Where the project involves external or industry supervisors then they may form part of the interview panel and your application documents will be shared with them.

If you are successful, you will progress to a second interview towards the end of February 2026. This will be with key academics from the CDT from across our four partner institutions (Durham, Hull, Loughborough, Sheffield) and your application documents will be shared with them.

Documents shared with external staff will have the guaranteed interview scheme section removed from your supplementary application form.

Interested?

Queries should be directed to Dr Antonio Griffo (a.griffo@sheffield.ac.uk) or the CDT (auracdt@hull.ac.uk)

Funding Notes

The AURA CDT is funded by EPSRC, allowing us to provide scholarships that cover fees plus a stipend set at the UKRI nationally agreed rates. These are currently £20,780 per annum at 2025/26 rates and will increase in line with the EPSRC guidelines for the subsequent years (subject to progress).

Our CDT scholarships are available to Home (UK) students. To be considered a Home student, you must have no restrictions on how long you can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the scholarship.