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Development of a digital twin and model predictive control schemes for farms of floating wind turbines

School of Electronics, Electrical Engineering and Computer Science | PHD

Applications are now CLOSED
Funding
Funded
Reference Number
EEECS/2025/PS2
Application Deadline
28 February 2025
Start Date
1 October 2025

Overview

Floating offshore wind turbines are exposed to wind and wave-induced motions that tend to reduce their productivity, cause fatigue to the overall structure, jeopardise their long-term structural integrity, and may even have a destabilising effect with dire consequences, especially under adverse weather conditions. Stochastic model predictive control methodologies offer unparalleled reliability, safety and performance properties, but come with two important challenges: (i) they require prior knowledge of the system dynamics and probabilistic properties of disturbances which, in this case, are not fully available, (ii) the associated computational cost is a limiting factor that hampers their applicability. The highly nonlinear dynamics of floating offshore wind turbines only exacerbate this situation.

Model predictive control (MPC) is the bee's knees of control theory: it is an advanced optimisation-based control methodology that can handle nonlinear dynamics and actuation/state constraints. We will put forward a unique amalgamation of estimation and control theory based on the novel risk averse MPC scheme: streams of wave/wind/demand data will be used to adapt how conservative or flexible the control system must be. This control scheme will lead to a significant reduction of structural load and fatigue. Moreover, to date, the few attempts at using MPC for the control of floating turbines have fallen short in light of the immense associated computation cost.

In this project:

[1] A high-resolution digital twin will be developed; this will be used to model the interactions among the wind turbines as a result of their wake effects and to detect structural fatigue and issue alerts to the wind farm operator.

[2] We will design an advanced risk-averse model predictive control scheme that will be able to respond to unexpected changes of the weather conditions (e.g., wind and waves) and unexpected spikes in electricity demand.

[3] We will propose new numerical optimisation methods for MPC that will lead to manifold acceleration.

[4] We will harness the computational power of embedded hardware equipped with GPUs (e.g., NVIDIA Orin) to enable the real-time solution of complex, nonconvex, large-scale optimisation problems such as those arising from risk averse MPC formulations.

[5] We will develop distributed multi-layer architectures to handle the spatially distributed nature of floating turbines in an offshore farm calls for.

Funding Information

To be eligible for consideration for a Home DfE or EPSRC Studentship (covering tuition fees and maintenance stipend of approx. £19,237 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications.

To be classed as a Home student, candidates must meet the following criteria and the associated residency requirements:

• Be a UK National,
or • Have settled status,
or • Have pre-settled status,
or • Have indefinite leave to remain or enter the UK.

Candidates from ROI may also qualify for Home student funding.

Previous PhD study MAY make you ineligible to be considered for funding.

Please note that other terms and conditions also apply.

Please note that any available PhD studentships will be allocated on a competitive basis across a number of projects currently being advertised by the School.

A small number of international awards will be available for allocation across the School. An international award is not guaranteed to be available for this project, and competition across the School for these awards will be highly competitive.

Academic Requirements:

The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.

The applicants need to have a solid background in engineering mathematics and control theory.

Project Summary
Supervisor

Dr Pantelis Sopasakis

More Information

p.sopasakis@qub.ac.uk

Research Profile


Mode of Study

Full-time: 3 or 3.5 years


Funding Body
Funding TBC
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