Maximum Energy Capturing Approach for Heaving Wave Energy Converters Using an Estimator-Based Finite Control Set Model Predictive Control

Abstract

The control problem in wave energy continues to remain an open question. This is mainly attributed to the difficulties associated with developing effective, yet economically viable, wave energy-harnessing control strategies, such as resource irregularity, the multidisciplinary nature of the system, and dynamic model uncertainties and ambiguities. Herein, a maximum energy-capturing approach for heaving wave energy converters (WECs) using an estimator-based finite control set model predictive control (FCS-MPC) is proposed. The proposed control strategy utilizes an elaborate nonlinear wave-to-wire model of a heaving WEC. The FCS-MPC is formulated such that a control command trajectory is not required; instead, it searches for the optimum control law—in the form of switching functions—that maximizes the WEC converted electrical energy while imposing soft constraints on the states of the power take-off (PTO) mechanism. Current transducers are deployed to measure the PTO three-phase currents and both mechanical and electrical variables required by the FCS-MPC strategy are estimated using an electrical-based extended Kalman filter (E-EKF). Simulations were performed to assess the effectiveness of the proposed control strategy. Results presented herein clearly show that the proposed referenceless FCS-MPC managed to produce 10%–23% more energy compared with benchmark resistive loading-based techniques with both fixed and variable wave frequency capabilities while utilizing 18%–45% less PTO resources.

Keywords

• Wave energy converter
• model predictive control
• finite control set
• extended Kalman filter
• permanent magnet linear generator
• point absorber