TY - GEN
T1 - Mooring force estimation for floating offshore wind turbines with augmented Kalman Filter
T2 - 5th International Offshore Wind Technical Conference
AU - Yung, Kobe Hoi-Yin
AU - Xiao, Qing
AU - Incecik, Atilla
AU - Thompson, Peter
N1 - This is an author accepted manuscript of Yung, K. H-Y., Xiao, Q., Incecik, A., & Thompson, P. (2024). Mooring force estimation for floating offshore wind turbines with augmented Kalman Filter: a step towards digital twin. In Proceedings of the ASME 2023 International Offshore Wind Technical Conference (IOWTC2023) American Society of Mechanical Engineers (ASME). The final version of record can be found at https://doi.org/10.1115/IOWTC2023-119374
PY - 2024/1/26
Y1 - 2024/1/26
N2 - During the recent research studies Digital Twin (DT) simulation models for Structural Health Monitoring (SHM) based on data-driven mode have been developed, which can provide accurate simulation and prediction of mooring forces of Floating Offshore Wind Turbines (FOWTs). However, the performance of this kind modelling is highly affected by the quantity of real data training set and it is limited to some specific configuration and the recorded environmental conditions. More importantly, the data-driven DT cannot interpret the physical meaning of structural dynamic interactions.Therefore, a new Physics-Based estimator is proposed in this work. The fully coupled FOWT simulations are carried out in QBlade Ocean and the simulation results are used to prepare the Reduced-Order Model by system identification for different sea states. The proposed estimator is based on the Augmented Kalman Filter in which the unknown mooring force is augmented as a state. The prediction of state is adjusted with the measurable platform motion data. It demonstrates the ability of filtering the noise in measurements and capturing the dynamic behaviour of FOWT with acceptable low computational cost. This real-time state estimator also provides the foundation of developing the DT modelling framework of FOWT and enables us to scale-up FOWTs in the next stage.
AB - During the recent research studies Digital Twin (DT) simulation models for Structural Health Monitoring (SHM) based on data-driven mode have been developed, which can provide accurate simulation and prediction of mooring forces of Floating Offshore Wind Turbines (FOWTs). However, the performance of this kind modelling is highly affected by the quantity of real data training set and it is limited to some specific configuration and the recorded environmental conditions. More importantly, the data-driven DT cannot interpret the physical meaning of structural dynamic interactions.Therefore, a new Physics-Based estimator is proposed in this work. The fully coupled FOWT simulations are carried out in QBlade Ocean and the simulation results are used to prepare the Reduced-Order Model by system identification for different sea states. The proposed estimator is based on the Augmented Kalman Filter in which the unknown mooring force is augmented as a state. The prediction of state is adjusted with the measurable platform motion data. It demonstrates the ability of filtering the noise in measurements and capturing the dynamic behaviour of FOWT with acceptable low computational cost. This real-time state estimator also provides the foundation of developing the DT modelling framework of FOWT and enables us to scale-up FOWTs in the next stage.
KW - digital twin
KW - floating offshore wind turbine (FOWT)
KW - mooring
UR - https://event.asme.org/IOWTC
UR - https://asmedigitalcollection.asme.org/OMAE/proceedings/IOWTC2023/87578/V001T01A014/1195024
U2 - 10.1115/IOWTC2023-119374
DO - 10.1115/IOWTC2023-119374
M3 - Conference contribution book
SN - 9780791887578
BT - Proceedings of ASME 2023 5th International Offshore Wind Technical Conference, IOWTC 2023
CY - New York, NY
Y2 - 18 December 2023 through 19 December 2023
ER -