الفهرس | Only 14 pages are availabe for public view |
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. |