الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Over the past few years, the increasing effects of climate change caused by the use of harmful conventional resources have become a major cause for concern. Extensive research and development in renewable energy has gained prominence as it harnesses natural resources like sunlight, wind, and water to create clean and sustainable power, leading the way to a greener future. This reduces our reliance on fossil fuels, actively combating climate change and promoting a more resilient and environmentally friendly energy infrastructure.
Leveraging wind energy as a dependable power source can significantly contribute to supplying electricity to the grid. Establishing wind farms, whether onshore or offshore, encompasses considerations like site availability, bird migration routes, and wind speeds. Healthy competition within the renewable energy sector plays a crucial role in fostering innovation and enhancing efficiency, resulting in reduced costs and improved accessibility to clean energy solutions. This competitive landscape stimulates the advancement of novel control technologies, facilitating a sustainable shift towards renewable energy.
This thesis presents a comprehensive approach to enhance the performance of offshore wind farms (OWFs) featuring a permanent magnet synchronous generator (PMSG) coupled with a variable-speed wind turbine (VSWT) and connected to the grid through a high-voltage direct current transmission system (HVDC). The study employs the gorilla tropical optimization (GTO) metaheuristic method to fine-tune proportional-integral (PI) controller gains for both the VSC-based PMSG-VSWT and the VSC-based HVDC transmission systems, ensuring improved system recovery and stability following network disturbances. Various strategies for extracting maximum power while maintaining stability under symmetrical and unsymmetrical fault circumstances are compared, with GTO consistently demonstrating superior results.
Additionally, the thesis introduces a novel application of a hybrid particle swarm optimizer and gravitational search algorithm (HPSOGSA) to optimize the control of offshore wind farms’ voltage source converter connected to HVDC transmission lines. Specifically, the HPSOGSA is employed to design fractional-order proportional-integral-derivative (FOPID) controller parameters, minimizing the system’s objective function based on integral squared error. Comparative analysis with a PI controller designed using a genetic algorithm and grey wolf optimization algorithm showcases the superiority of the HPSOGSA-based FOPID controller in enhancing offshore wind farm operations under different transient conditions. The fault ride-through capabilities of the proposed control strategy are thoroughly evaluated, affirming its effectiveness. MATLAB/Simulink is utilized for the implementation and testing of both control mechanisms.