الفهرس 
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Abstract
Wave energy is one of the most promising solutions to solve the world’s electricity problem. The amount of energy in waves near the world’s coastlines can generate an estimated power of 2.15 TW, enough to meet nearly 10% of the world’s demand [1]. Hence, several wave energy conversion systems (WECSs) have been developed to convert wave energy into electrical energy. In this thesis, the Archimedes wave swing device (AWS) is employed, which is one of the most widely used devices for harvesting electricity from waves. The vertical motion of the AWS due to random waves is converted into electrical energy via a linear permanent magnet synchronous generator (LPMSG). The thesis presents the research work on the linear and nonlinear models of AWS.
The linear model representation of the AWS is utilized in the first research work. This work introduces a new innovative, powerful technique to eliminate the windup issue in the integral term of the Proportional Integral (PI) controller during power system faults. The coot optimization algorithm (COA) combined with an anti-windup method provides an optimum design for the PI controllers used in the converters of the WECS, leading to a significant enhancement of the transient stability of a grid connected WECS. The WECS utilizes a generator-side converter (GSC) and a grid-side inverter (GSI) to ensure a successful connection to the power grid. The GSC is employed to minimize the generator power losses and extract the maximum real power. The GSI controls the terminal voltage at the point of common coupling and the DC-link voltage at their reference values. The COA is directly applied to the MATLAB/Simulink model to design the PI controllers optimally. Also, an anti-windup method is utilized to enhance the transient performance of the system. The proposed COA control strategy results are compared to those obtained using other recent optimization algorithms under different grid fault conditions.
The nonlinear model representation of the AWS is utilized in the second research work. In this work, a nonlinear model of a grid-connected AWS is utilized to explore the actual generation potential of this wave energy conversion system. Based on the author’s knowledge, this 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, minimize generator power losses, and maintain the grid and DC link voltages at their reference values of 1 p.u. 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 the control system, a grid-connected AWS was tested experimentally using a real-time RT-LAB simulator combined with the OP4510 equipment. The results validated the efficiency and superiority of the control system using AGWO-CS, and the results from the simulation and experiment were close.