Adaptive Control Strategy for Oscillating Water Column Wave Energy Device Based on Deep Reinforcement Learning
Year: 2025
Type: article
Abstract: This paper proposes an adaptive control strategy for oscillating water column (OWC) wave energy devices based on deep reinforcement learning (DRL). By combining the feature extraction capability of deep neural networks with the decision optimization ability of reinforcement learning, a system is designed to dynamically adjust the turbine control strategy under varying wave conditions. Experimental results show that the proposed method effectively improves the energy conversion efficiency of the device, enhances its stability under extreme sea conditions, and significantly optimizes the system's long-term operational performance. The study demonstrates that deep reinforcement learning provides a feasible solution for intelligent control of wave energy devices.
DOI: 10.54097/xd61hr48
Author: Yuxuan Liu
Language: English
Topic: Wave and Wind Energy Systems
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408868875