Experimental validation of data-driven reactive control strategy for wave energy converters: A Gaussian process regression approach

Abstract

Wave energy is a renewable energy source that can generate electricity without emitting carbon dioxide using wave energy converters (WECs). WECs face several challenges, such as effectively harnessing wave energy, ensuring operational safety, and implementing cost-saving measures. In this study, a data-driven reactive control strategy for WECs using Gaussian process regression is experimentally validated. The control strategy aimed to maximize energy absorption while satisfying physical constraints without a WEC dynamic model. In this study, the Gaussian process regression model represents a WEC dynamic model using an approximation function based on the input/output data. Experimental validation results confirmed that the proposed data-driven reactive control strategy effectively controlled the WEC and improved its performance using the Gaussian model. Moreover, performance was improved under different wave conditions compared to those used during training. The control strategy can be useful in situations where conventional methods cannot realize ideal control parameters, such as when substantial model errors exist or the WEC dynamic model changes over time. The data-driven approach that uses Gaussian process regression reduces learning costs compared to other machine-learning methods. It also has stable control performance because it is independent of the training dataset. The potential of data-driven approaches for optimizing WEC control strategies was highlighted.