Abstract
As the Floating Offshore Wind Turbine industry approaches the transition from demonstrators to commercial scale farms, condition monitoring systems will be integral to risk management. Scalable condition monitoring solutions exist for both Fixed Offshore Wind and floating Oil and Gas, however a holistic solution addressing aerodynamic and hydrodynamic challenges on a floating wind unit is lacking.
Where sensors should be minimized, or for unmeasurable parameters, digital twins offer an alternative for predicting major component condition, relying instead on numerical models based on floater global response and metocean conditions.
This paper demonstrates the potential of reconstructed load signals, state-of-the-art sensors and first principles physics-based digital twins using approaches from the Wind and Oil and Gas sectors. Existing motion-based integrity monitoring methods are adapted to a real-time reduced-order model of a floating wind unit. Specifically, a state-observer model is used to reproduce position and mooring tension, enabling mooring failure identification and fatigue tracking.
The methods are demonstrated through a 15 MW synthetic case study, including examination of the impact of noise and sensor loss on reliability. The results show that the integration of digital twins into sensor-based monitoring systems can reconstruct missing data with sufficient certainty to facilitate early detection of failures.
