Analysis, Forecasting, and System Identification of a Floating Offshore Wind Turbine Using Dynamic Mode Decomposition
Giorgio Palma, Andrea Bardazzi, Alessia Lucarelli, Chiara Pilloton, Andrea Serani, Claudio Lugni, Matteo Diez
Abstract
This article presents the data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the application of dynamic mode decomposition (DMD). The DMD has here been used (i) to extract knowledge from the dynamic system through its modal analysis, (ii) for short-term forecasting (nowcasting) from the knowledge of the immediate past of the system state, and (iii) for system identification and reduced-order modeling. All the analyses are performed on experimental data collected from an operating prototype. The nowcasting method for motions, accelerations, and forces acting on the floating system applies Hankel-DMD, a methodological extension that includes time-delayed copies of the states in an augmented state vector. The system identification task is performed by using Hankel-DMD with a control (Hankel-DMDc), which models the system as externally forced. The influence of the main hyperparameters of the methods is investigated with a full factorial analysis using error metrics analyzing complementary aspects of the prediction. A Bayesian extension of the Hankel-DMD and Hankel-DMDc is introduced by considering the hyperparameters as stochastic variables, enriching the predictions with uncertainty quantification. The results show the capability of the approaches for data-lean nowcasting and system identification, with computational costs being compatible with real-time applications. Accurate predictions are obtained up to 4 wave encounters for nowcasting and 20 wave encounters for system identification, suggesting the potential of the methods for real-time continuous-learning digital twinning and surrogate data-driven reduced-order modeling.