Nonlinear Model Predictive Control of Heaving Wave Energy Converter with Nonlinear Froude–Krylov Forces

Tania Demonte Gonzalez (), Enrico Anderlini, Houssein Yassin and Gordon Parker
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Tania Demonte Gonzalez: Mechanical Engineering–Engineering Mechanics Department, Michigan Technological University, Houghton, MI 49931, USA
Enrico Anderlini: Department of Mechanical Engineering, University College London, London WC1N 1AX, UK
Houssein Yassin: Mechanical Engineering–Engineering Mechanics Department, Michigan Technological University, Houghton, MI 49931, USA
Gordon Parker: Mechanical Engineering–Engineering Mechanics Department, Michigan Technological University, Houghton, MI 49931, USA

Energies, 2024, vol. 17, issue 20, 1-16

Abstract: Wave energy holds significant promise as a renewable energy source due to the consistent and predictable nature of ocean waves. However, optimizing wave energy devices is essential for achieving competitive viability in the energy market. This paper presents the application of a nonlinear model predictive controller (MPC) to enhance the energy extraction of a heaving point absorber. The wave energy converter (WEC) model accounts for the nonlinear dynamics and static Froude–Krylov forces, which are essential in accurately representing the system’s behavior. The nonlinear MPC is tested under irregular wave conditions within the power production region, where constraints on displacement and the power take-off (PTO) force are enforced to ensure the WEC’s safety while maximizing energy absorption. A comparison is made with a linear MPC, which uses a linear approximation of the Froude–Krylov forces. The study comprehensively compares power performance and computational costs between the linear and nonlinear MPC approaches. Both MPC variants determine the optimal PTO force to maximize energy absorption, utilizing (1) a linear WEC model (LMPC) for state predictions and (2) a nonlinear model (NLMPC) incorporating exact Froude–Krylov forces. Additionally, the study analyzes four controller configurations, varying the MPC prediction horizon and re-optimization time. The results indicate that, in general, the NLMPC achieves higher energy absorption than the LMPC. The nonlinear model also better adheres to system constraints, with the linear model showing some displacement violations. This paper further discusses the computational load and power generation implications of adjusting the prediction horizon and re-optimization time parameters in the NLMPC.

Keywords: wave energy converter; optimization; point absorber; marine energy; nonlinear control; model predictive control; nonlinear model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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