In this paper, we use a nonlinear model of a grid-connected Archimedes wave swing (AWS) to explore the actual generation potential of this wave energy conversion system. Our model has never been used before for a grid-connected AWS. The control system employs six proportional-integral (PI) controllers to maximize the energy harvest from waves, to minimize the generator power losses, and to maintain both the grid and DC link voltages at their reference values. The PI controller parameters were selected using a hybrid augmented grey wolf optimizer and cuckoo search (AGWO-CS) algorithm. The results obtained from MATLAB Simulink for different types of grid fault, with successful and unsuccessful reclosing of the circuit breakers, were compared with results from the particle swarm optimization (PSO) and COOT algorithms. To verify the accuracy of our control system, a grid-connected AWS was tested experimentally using a real-time RT-LAB simulator combined with OP4510. The results validated the efficiency and superiority of the control system using AGWO-CS and the results from simulation and experiment were similar.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability