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:
- 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.
- 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.
- We will propose new numerical optimisation methods for MPC that will lead to manifold acceleration.
- We will harness the computational power of embedded hardware equipped with GPUs (e.g., NVIDIA Jetson) to enable the real-time solution of complex, nonconvex, large-scale optimisation problems such as those arising from risk averse MPC formulations.
- We will develop distributed multi-layer architectures to handle the spatially distributed nature of floating turbines in an offshore farm calls for.
Project Key Words: Farms of Floating Wind Turbines; Renewable Energy; Digital Twin; Model Predictive Control; Numerical Optimisation; GPUs
For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at
Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/
A minimum 2.1 honours degree or equivalent in Computer Science or Electrical and Electronic Engineering or relevant degree is required.
This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.
For candidates who do not meet the above residency requirements, a small number of international studentships may be available from the School. These are expected to be highly competitive, and a selection process will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project.