Research & Development
Projet
SUBSEE 4D
A digital twin to facilitate the operation of floating wind farms
- Duration: 36 months (2020-2023)
- Budget: €730K
Context
Despite a significant offshore experience coming from the O&G industry, several specificities of offshore renewable energy systems induce uncertainties of their subsea dynamics. Today, as any emerging technology, the efforts are focused on the system efficiency and robustness as part of the design stage. Very few works are dedicated to the in-service follow-up and the maintenance strategy which represent a critical point both technically and financially for the very next commercial farms.
The development of a numerical tool to optimise submerged systems and reduce uncertainties on fatigue life would facilitate the operation of floating wind farms.
Objective
- To optimise and plan maintenance operations, as well as making submerged systems more reliable, by developing a digital twin solution including software for in-service monitoring of mooring lines, developed by France Energies Marines, which will be offered to a floating wind farm operator for further customisation and deployment on a pilot project
Main achievements
Creation and deployment of in-service monitoring software for floating offshore wind turbine in operation
Numerical modelling of the real system using a global numerical model
Implementation of sensors deployment strategy for structural integrity monitoring
Development of mathematical tools based on machine learning approaches for anomaly prediction and classification
Min outputs
- Representative numerical model of a floating offshore wind turbine
- Database of system dynamic responses obtained using an aeroservo-hydro-elastic numerical model
- Neural networks for offshore wind turbine motion prediction
- Automatic classifier for detecting and classifying anomalies in the system
Conclusion
SUBSEE 4D has developed a digital twin solution to facilitate the operation of floating offshore wind turbines. This tool is based on a numerical representation using a simulation model and machine learning approaches.
Partners
This project was led by Cervval.
Funding
This project has received financial support from the European Regional Development Fund, the Brittany region, Brest Métropole and Corimer.
Accreditation
This project was certified by the clusters Pôle Mer Bretagne Atlantique and Pôle Images & Réseaux.
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