Data-driven Surge Modelling of Floating Offshore Wind Turbines with Various Input Selection for Condition Monitoring (International Conference on Energy, Intelligence Systems, and Cloud Computing (Ingenio 2026), 2026)
Floating offshore wind turbines (FOWTs) rely heavily onmooring systems to maintain position and ensure safe operation undercomplex environmental loading conditions. However, accurate modelingand condition monitoring of mooring dynamics remain challenging due tostrong nonlinear behavior, coupled multi-degree-of-freedom interactions,and the limited availability of high-quality experimental data. This studyproposes a data-driven modeling framework for condition monitoring ofFOWT mooring systems under limited data conditions. A time-delayneural network (TDNN) is developed to capture the nonlinear dynamicrelationships between environmental excitations, platform motions, andsurge response which closely correlates with mooring forces. The model isevaluated using the normalized root mean square error (NRMSE) underwave-only, wind-only, and wave–wind coupled scenarios. Results indicatethat appropriate selection of input features significantly influences prediction accuracy. In wave-only cases, pitch motion provides the most relevant additional information for improving surge prediction. In wind-onlycases, the combination of heave and pitch yields the best performance,as these motions contain useful low-frequency dynamics correlated withsurge. However, in wave–wind coupled cases, model performance deteriorates due to increased system complexity and limited data length, whichreduces generalization capability. Overall, the proposed framework highlights the importance of physically informed input selection and demonstrates its effectiveness for data-driven condition monitoring of FOWTmooring systems using limited experimental measurements.
Keywords: Data-driven modelling; Condition monitoring; Mooring system; Input feature selection