Model Predictive Energy-Maximising Tracking Control for a Wavestar-Prototype Wave Energy Converter

Li, Doudou; Patton, Ron

Abstract

To date, one of the main challenges in the wave energy field is to achieve energy-maximizing control in order to reduce the levelized cost of energy (LCOE). This paper presents a model predictive velocity tracking control method based on a hierarchical structure for a Wavestar-like deivce in the WEC-SIM benchmark. The first part of the system structure aims to estimate the wave excitation moment (WEM) by using a Kalman filter. Then, an extended Kalman filter (EKF) is chosen to obtain the amplitude and angular frequency of the WEM in order to compute the reference velocity. Following this, a low-level model predictive control (MPC) method is designed to ensure the wave energy converter (WEC) tracks the optimal reference velocity for maximum energy extraction from irregular waves. Two Gaussian Process (GP) models are considered to predict the future wave excitation moment and future reference velocity, which are needed in MPC design. The proposed strategy can give a new vision for energy-maximizing tracking control based on MPC.

Citation

Li, D., & Patton, R. (2023). Model Predictive Energy-Maximising Tracking Control for a Wavestar-Prototype Wave Energy Converter. Journal of Marine Science and Engineering, 11(7), Article 1289. https://doi.org/10.3390/jmse11071289

• Journal Article Type: Article
• Acceptance Date: Jun 20, 2023
• Online Publication Date: Jun 25, 2023
• Publication Date: Jul 1, 2023
• Deposit Date: Jul 10, 2023
• Publicly Available Date: Jul 11, 2023
• Journal: Journal of Marine Science and Engineering
• Electronic ISSN: 2077-1312
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 7
• Article Number: 1289
• DOI: https://doi.org/10.3390/jmse11071289
• Keywords: Kalman filter; Extended Kalman filter; Gaussian Process (GP) model; Velocity tracking; Model predictive control
• Public URL: https://hull-repository.worktribe.com/output/4331028