Environmental-Sensing and adaptive optimization of wave energy converter based on deep reinforcement learning and computational fluid dynamics

Abstract

This paper introduces a novel coupled model for real-time control of the point absorber wave energy converter (WEC) using parallelized deep reinforcement learning (DRL), where the WEC is situated within a numerical wave tank (NWT) built with the method of computational fluid dynamics (CFD). An in-house solver is developed to couple with the DRL and CFD to solve the interaction between WEC and the fluid environment. Validations on wave generation, wave-floater interaction, and power take-off (PTO) unit are carried out. Then, neglecting the detailed model for the PTO technologies, the DRL-based strategy dynamically adjusts the PTO force as a function of the wave features and floater motion. Based on the interaction data, the model-free DRL is outstanding in adaptability and robustness. Simulation results reveal that DRL control improves the wave energy absorption in irregular wave environments, resulting in improvement of 107.5 % compared to the resistive control, with better device protection performance than the model predictive control (MPC). An additional analysis of model-free characteristics of DRL demonstrates the optimization ability independent of floater modeling. This work is the first in-depth study of DRL control of WECs in CFD simulation, providing a more accurate simulation and an optimization process closer to the reality.