الفهرس 
CHAPTER 1 INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The world’s attention has recently been drawn to renewables, since most advanced countries aspire to replace all fossil fuels with renewables. This interest is focused on producing more safe, clean, and sustainable electric power sources.
This thesis is concerned with potential optimization algorithms for wind turbines control to engage, positively, to the electrical grid during different operating conditions. Comprehensive dynamic simulation models for wind turbines are combined to simulate their interaction during network parameters variations. The influence of energy storage devices on wind turbine response is also explored in this thesis as a remedy for wind energy’s intermittent nature. The case study actual and chronological data are obtained from Egyptian Zafarana wind farm and used as a benchmark system.
The first main aim of this research is to assess the effectiveness of utilizing different optimization techniques; namely the Hybrid Particle Swarm Algorithm and Grey Wolf Optimizer (PSOGWO), the Genetic Algorithm (GA), the Particle Swarm Algorithm (PSO) and the Archimedes Optimization Algorithm (AOA) in improving the system stability of wind system and enhancing the low voltage ride through (LVRT) capability during abnormal operating conditions.
The second main aim of this research is to assess the effectiveness of integrating an Energy Storage System (ESS) presented in inserting Superconducting Magnetic Energy Storage (SMES) to the wind system and studying its effect on enhancing the output power of the system and improving the system response.
The SMES system parameters are controlled using a control system that is based on Proportional Integral (PI) controllers. To get PI controller parameters that guarantee the optimum design of the controllers, the fitness function is optimized using the modern optimization tactic entitled Archimedes optimization algorithm (AOA).
The suggested control system is tested under various fault scenarios to determine its validity. This validity is tested using simulation results generated by MATLAB software. The simulation results demonstrate that the suggested control system performs efficiently under a variety of fault circumstances. The proposed controller’s adequacy is confirmed by comparing its results to those obtained using other traditional approaches such as the Genetic Algorithm (GA) and the Particle Swarm Optimization technique (PSO).
All throughout the thesis, it was discovered that the use of new optimization approaches, as well as the integration of energy storage devices in the wind system, had a significant influence on the performance efficiency of the wind system.