الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The energy crisis is one of the most significant issues threatening our
world. Nowadays, the world is going towards renewable energy to
overcome the disastrous effects of fossil fuels on human health and the
environment. Solar energy is considered one of the most promising
renewable energy resources as it is an abundant, clean, and green energy
source.
In this thesis, photovoltaic (PV) systems are explained in detail as they
present the direct transformation from solar irradiation into electrical
energy. In order to increase the efficiency of PV systems under uniform
solar irradiance and Partial Shading Conditions (PSC), Maximum Power
Point Tracking (MPPT) technique is needed to track the Maximum Power
Point (MPP) dynamically. Fractional Open Circuit Voltage (FOCV),
Fractional Short Circuit Current (FSCC), Perturb and Observe (P&O),
Incremental Conductance (INC), Fuzzy Logic Controller (FLC), Artificial
Neural Network (ANN), Neuro-Fuzzy (NF), Particle Swarm Optimization
(PSO), and Cuckoo Search Algorithm (CSA) based MPPT techniques are
used. All of these MPPT techniques are simulated under various operating
conditions using the MATLAB / Simulink program (version R2017a) to
show which technique can track the MPP efficiently.
As PSC is considered one of the most severe challenges facing the PV
system, different PV array configurations such as Series-Parallel (SP),
Bridge-Link (BL), and Total Cross-Tied (TCT) are utilized to show the
impact of changing the PV configuration in mitigation PSC.
The simulation results show that the PSO algorithm performs better than
other prementioned techniques under various operating conditions. It is
also apparent from the simulation results that the TCT configuration can
mitigate the impact of PSCs.