Modeling and predicting oscillating surge wave energy converter behavior using dynamic mode decomposition

Modeling wave energy converters (WECs) to accurately predict their hydrodynamic behavior has been a notoriously difficult challenge for the wave energy field, particularly in polychromatic sea states. A key challenge is that accurate, physics-based WEC modeling is too computationally expensive to be used for future-state prediction and optimal control, two areas of active research in the wave energy field. We propose the use of dynamic mode decomposition (DMD) to provide a purely data-driven technique to overcome these issues. In this study, we use DMD to develop a linear model of a grid-scale Oscillating Surge WEC (OSWEC) operating in polychromatic seas. Our goal is to model and predict the behavior of the OSWEC for use in optimal control schemes, such as model predictive control (MPC), without requiring an infeasible computational cost or sacrificing accuracy. We show that by using DMD, we can accurately model and predict OSWEC behavior in response to polychromatic sea states with appropriate model rank and training time. These findings provide insight on the use of DMD on systems with limited time-resolved data and present a framework for applying similar analysis to lab-scale experiments, high-fidelity simulations, or data from OSWECs operating in the field.