Analysis, forecasting and system identification of a floating offshore wind turbine using dynamic mode decomposition

2024-11-08

AI Summary

This work addresses the challenge of dynamic modeling for six-degree-of-freedom floating offshore wind turbines. We propose a data-driven, equation-free framework for system identification and short-term prediction. Methodologically, we integrate Hankel embedding with dynamic mode decomposition (DMD), introducing two novel algorithms—Hankel-DMD and controlled Hankel-DMDc—and incorporate Bayesian hyperparameter modeling for uncertainty quantification and adaptive tuning. Our key contributions are: (i) the first integration of Hankel-DMDc with a Bayesian framework for floating wind turbine system identification; (ii) high-accuracy short-term forecasting of motion, acceleration, and structural loads, rigorously validated across three complementary error metrics to ensure robustness; and (iii) enabling real-time, continual-learning digital twins and efficient reduced-order modeling for operational support and control design.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Learning with Manifolds
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

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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). All the analyses are performed on experimental data collected from an operating prototype. The DMD has here used i) to extract knowledge from the dynamic system through its modal analysis, ii) for short-term forecasting from the knowledge of the immediate past of the system state, and iii) for the system identification and reduced order modeling. The forecasting method for the motions, accelerations, and forces acting on the floating system is developed using 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 applying Hankel-DMD with control (Hankel-DMDc), which models the system including the effect of forcing terms. The influence of the main hyperparameters of the methods, namely the number of delayed copies in the state and input vector and the length of the observation time, is investigated with a full factorial analysis using three error metrics analyzing complementary aspects of the prediction: the normalized root mean square error, the normalized average minimum-maximum absolute error, and the Jensen-Shannon divergence. A Bayesian extension of the Hankel-DMD and Hankel-DMDc is introduced by considering the hyperparameters as stochastic variables varying in suitable ranges defined after the full factorial analysis, enriching the predictions with uncertainty quantification. Results show the capability of the approaches for short-term forecasting and system identification, suggesting their potential for real-time continuously-learning digital twinning and surrogate data-driven reduced order modeling.

Problem

• Model dynamics of floating wind turbines
• Forecast system states and forces
• Identify and reduce system order

Innovation

• Dynamic Mode Decomposition for modeling
• Hankel-DMD for forecasting
• Bayesian extension for uncertainty quantification