Research into structural INTEGRity in floating offshore wind using models based on Artificial Intelligence


Project
Context
The development of commercial floating wind farms poses challenges that do not arise in individual prototypes. It is necessary to go from the technology working properly for a limited period, to guaranteeing its operation for its entire lifespan. So, there is a big technological leap from oversizing the first prototype to demonstrate its technical feasibility, to optimising its design to demonstrate its economic viability.
- Lack of real information on failure rates.
- Lack of correct market orientation
- Lack of comprehensive approaches that would allow a single model to be able to analyse data and make decisions.
OBJECTIVES AND ACTIVITIES:
1
Advanced monitoring systems for application in floating wind energy.
Special emphasis will be placed on certain systems and/or components:
- Machine and powertrain monitoring.
- Monitoring of dynamic mooring lines.
- Floating structure monitoring.
2
Data management architecture for processing large amounts of information.
- Generation of a large amount of data from the monitoring and control system.
- The location of infrastructures is remote.
- Shared information of different nature and interests: windfarm operator, operation and maintenance personnel, or substation staff, among others.
3
Artificial Intelligence-based models for the modelling of various physical phenomena.
- IoT technologies.
- Data storage capacity.
- Computational processing capabilities.
- Hybridisation of artificial intelligence models with physical models.
- Enables the combination of machine learning models with mathematical models representing real-world physical phenomena.
4
Coatings for corrosion protection for the characterisation of degradation .
- Processes for applying ceramic coatings.
- Organic and hybrid coatings.
- Eco-sustainable metallic coatings.
Impacts
Each project workstream is expected to have the following impacts:

Increased lifespan of floating offshore wind farms.

Cost reduction and plant optimisation through predictive maintenance. s.

Generation of a large amount of data from the monitoring and control system.

Improvement of the accuracy and understanding of AI and physical models.

Development of more efficient and environmentally friendly coating
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Alberto Sánchez



















