الفهرس 
CHAPTER (1) INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
A photovoltaic system is considered among the most promising renewable energy sources. Photovoltaic systems (PV) are characterized among other renewable energies by being clean, sustainable, environment friendly, having zero carbon dioxide emissions, and having relatively stable energy prices as no fuel is required. Also, they are flexible for deregulated market transactions. So, many power system developers are interested in improving PV system operation, control, and performance by developing their control and interface systems. The new trend of controlling is the optimization control by using different optimization algorithms.
This thesis proposes a relatively new hybrid optimization technique to tune the PV system controller gains to achieve better performance under normal conditions, dynamic irradiance, and faulty conditions by improving the voltage error signal. This is achieved by obtaining minimum over-shoot and minimum steady-state error. The introduced hybrid optimization technique combines both the Teaching Learning Based Optimization (TLBO) which has high exploration and the Equilibrium Optimizer (EO) which has high exploitation.
Moreover, a comparison with various optimization approaches is presented to validate the efficacy of the proposed approach (TLBO-EO). The results show the superiority of the proposed hybrid optimization technique in improving the PV system performance compared to other previously reported algorithms.