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

Project Description:

This PhD scholarship is offered by the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience; a partnership between the Universities of Durham, Hull, Loughborough and Sheffield. The project is supported by industry partner the Offshore Renewable Energy Catapult. The successful applicant will undertake six-month of training with the rest of the CDT cohort at the University of Hull before continuing their PhD research at the University of Sheffield.

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 multiphysics, multiscale, high-fidelity simulations that emulate in real-time the state of a corresponding physical twin based on historical and real-time sensors data. The comparison of physical and virtual data throughout 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 and development

You will benefit from a taught programme, giving you a broad understanding of the breadth and depth of current and emerging offshore wind sector needs. This begins with an intensive six-month programme at the University of Hull for the new student intake, 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.

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 the international equivalents) in Computer Science, Engineering, Physics, or Mathematics and Statistics, we would like to hear from you.

If your first language is not English, or you require Tier 4 student visa to study, you will be required to provide evidence of your English language proficiency level that meets the requirements of the Aura CDT’s academic partners. This course requires academic IELTS 7.0 overall, with no less than 6.0 in each skill.

If you have any queries about this project, please contact Prof Antonio Griffo (a.griffo@sheffield.ac.uk)  

You may also address queries about the CDT to auracdt@hull.ac.uk.

Watch our short video to hear from Aura CDT students, academics and industry partners:

Funding

The Offshore Wind CDT is funded by the 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).

Eligibility

Research Council funding for postgraduate research has residence requirements. Our CDT scholarships are available to Home (UK) Students. To be considered a Home student, and therefore eligible for a full award, a student must have no restrictions on how long they 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 (with some further constraint regarding residence for education). For full eligibility information, please refer to the EPSRC website.

We also allocate a number of scholarships for International Students per cohort.

Guaranteed Interview Scheme

The CDT is committed to generating a diverse and inclusive training programme and is looking to attract applicants from all backgrounds. 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 these under-represented ethnic groups to our programme and is an opt in process.

Find out more

How to apply

Applications for this project will open in Autumn 2025 for September 2026 entry.

Interviews will be held online with an interview panel comprising of project supervisory team members from the host university where the project is based.  Where the project involves external supervisors from university partners or industry sponsors then representatives from these partners may form part of the interview panel and your supplementary application form will be shared with them (with the guaranteed interview scheme section removed).

If you have any queries about this project, please contact Prof Antonio Griffo (a.griffo@sheffield.ac.uk)  

You may also address queries about the CDT to auracdt@hull.ac.uk.

References and further reading

[1] F. Tao et al. “Digital Twin in Industry: State-of-the-Art”, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 15, NO. 4, APRIL 2019 

[2] Industrial Internet Consortium, “Digital twins for Industrial Applications.” Available: https://www.iiconsortium.org/pdf/IIC_Digital_Twins_Industrial_Apps_White_Paper_2020-02-18.pdf 

 [3] A. Ebrahimi “Challenges of developing a digital twin model of renewable energy generators”, 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), DOI: 10.1109/ISIE.2019.8781529 

[4] https://uk.mathworks.com/discovery/digital-twin.html 

[5] F. Alvarez-Gonzalez, A. Griffo, “Real-time Hardware-in-the-loop simulation of permanent magnet synchronous motor drives under stator faults”, IEEE Transactions on Industrial Electronics, 64(9), 6960-6969