Maximization of energy absorption for a wave energy converter using the deep machine learning

Li, Liang and Yuan, Zhiming and Gao, Yan (2018) Maximization of energy absorption for a wave energy converter using the deep machine learning. Energy, 165 (Part A). pp. 340-349. ISSN 1873-6785 (https://doi.org/10.1016/j.energy.2018.09.093)

[thumbnail of Li_etal_Energy_2018_Maximization_of_energy_absorption_for_a_wave_energy_converter]
Preview
 Text. Filename: Li_etal_Energy_2018_Maximization_of_energy_absorption_for_a_wave_energy_converter.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (362kB)| Preview

Abstract

A controller is usually used to maximize the energy absorption of wave energy converter. Despite the development of various control strategies, the practical implementation of wave energy control is still difficult since the control inputs are the future wave forces. In this work, the artificial intelligence technique is adopted to tackle this problem. A multi-layer artificial neural network is developed and trained by the deep machine learning algorithm to forecast the short-term wave forces. The model predictive control strategy is used to implement real-time latching control action to a heaving point-absorber. Simulation results show that the average energy absorption is increased substantially with the controller. Since the future wave forces are predicted, the controller is applicable to a full-scale wave energy converter in practice. Further analysis indicates that the prediction error has a negative effect on the control performance, leading to the reduction of energy absorption.

ORCID iDs

Li, Liang ORCID logoORCID: https://orcid.org/0000-0002-8528-3171, Yuan, Zhiming ORCID logoORCID: https://orcid.org/0000-0001-9908-1813 and Gao, Yan;
CORE (COnnecting REpositories)