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

Highlights: • A smart real-time controller is developed. • The smart controller uses artificial neural network to predict short-term wave forces. • The neural network is trained by the deep learning algorithm. • The prediction accuracy of the neural network is satisfactory. • The smart controller enhances the energy absorption substantially. 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.