Analysis and Forecasting of the Dynamics of a Floating Wind Turbine Using Dynamic Mode Decomposition

• URL: http://arxiv.org/abs/2411.07263v1
• Date: Fri, 08 Nov 2024 18:38:29 GMT
• Title: Analysis and Forecasting of the Dynamics of a Floating Wind Turbine Using Dynamic Mode Decomposition
• Authors: Giorgio Palma, Andrea Bardazzi, Alessia Lucarelli, Chiara Pilloton, Andrea Serani, Claudio Lugni, Matteo Diez,
• Abstract summary: This article presents a data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the Dynamic Mode Decomposition (DMD) A forecasting algorithm for the motions, accelerations, and forces acting on the floating system is developed. Results show the approach's capability for short-term future estimates of the system's state, which can be used real-time prediction and control.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article presents a data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the Dynamic Mode Decomposition (DMD). The DMD is here used to provide a modal analysis and extract knowledge from the dynamic system. A forecasting algorithm for the motions, accelerations, and forces acting on the floating system, as well as the height of the incoming waves, the wind speed, and the power extracted by the wind turbine, is developed by using a methodological extension called Hankel-DMD, that includes time-delayed copies of the states in an augmented state vector. All the analyses are performed on experimental data collected from an operating prototype. The quality of the forecasts obtained varying two main hyperparameters of the algorithm, namely the number of delayed copies and the length of the observation time, is assessed using three different error metrics, each analyzing complementary aspects of the prediction. A statistical analysis exposed the existence of optimal values for the algorithm hyperparameters. Results show the approach's capability for short-term future estimates of the system's state, which can be used for real-time prediction and control. Furthermore, a novel Stochastic Hankel-DMD formulation is introduced by considering hyperparameters as stochastic variables. The stochastic version of the method not only enriches the prediction with its related uncertainty but is also found to improve the normalized root mean square error up to 10% on a statistical basis compared to the deterministic counterpart.

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