الفهرس 
AbstractOnly 14 pages are availabe for public view
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Abstract
The problem of the energy is one of the most serious and critical problems faced by mankind and because of the problem of lack of non-renewable energy sources, so one of the challenges of modern man is to find new sources of energy. In current decades, the Photovoltaic system attracts the attention of analysts because it is a sustainable renewable resource. Solar-powered Photovoltaic (PV) systems contribute to the most cost-effective and the cleanest electrical energy. The ambient conditions such as temperature and radiation change over time, therefore, the current-voltage (I-V) and the power-voltage (P-V) characteristic curves of solar panels are nonlinear, with a distinct maximum power point (MPP). Despite the inevitable environmental changes, solar panels have to operate at their MPP to continuously get maximum power from it. This is why the controllers of all solar power electronic converters employ some maximum power point tracking (MPPT) methods. In the case of un-shaded PV systems, there is only one power peak. Many peaks are created in the case of partial shading conditions (PSC), many Local Maximum Power Points (LMPPs) and one Global Maximum Power Point (GMPP). Conventional MPPT techniques like Hill Climbing (HC), Perturb and Observe (P&O) and Incremental Conductance were good enough to track the maximum power for the un-shaded PV systems. But in the case of PSC, conventional MPPT methods may stick to one of the LMPPs, this reduces the efficiency of the PV system. Artificial intelligence based MPPT techniques like artificial neural network (ANN), genetic algorithm (GA) and fuzzy logic controller (FLC) can reach the GMPP under PSC but there is overlapping and mutation in these techniques, this makes the average convergence speed in this techniques not low. Bio-inspired techniques like gray wolf optimization (GWO), particle swarm optimization (PSO), and cuckoo search algorithm (CSA) can catch the PV system’s GMPP under PSC effectively with low convergence time. This thesis introduces an improved PSO and improved CSA that based on the re-initialization of particles when the pattern of shading is changed. This makes the duty cycle changes upon a change in shading pattern unlike the conventional methods, the duty cycle is fixed even if the shading pattern changes. So, the conventional method cannot track the true GMPP but the proposed method can track the true GMPP. Also a hybrid method combines PSO and incremental conductance method is proposed. The incremental conductance method is employed in the first stage to search for the first local maximum power point quickly. Then PSO is implemented in the second stage. This makes the proposed method reach the GMPP faster than the conventional PSO. MATLAB/Simulink platform is used to implement the developed algorithms and their performances are evaluated. The results are tested under Standard Test Conditions (STC) and two cases of partial shading conditions. The simulation results show that the improved PSO has results of power, voltage and current better than the conventional method. Also the results of power, voltage and current in improved CSA is higher than the values in the conventional method. The hybrid method reduces oscillations and convergence speed compared to conventional PSO but the values of power, voltage and current are roughly the same in the conventional method and improved method. Moreover, comparing the PSO and CSA together, we find that the modified and conventional CSA is better than the modified and conventional PSO.