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    Enhancement performance and efficiency of photovoltaic system based on hybrid maximum power point tracking
    (İstanbul Gelişim Üniversitesi Lisansüstü Eğitim Enstitüsü, 2023) Al-Mayyahi, Mina Tuqa Mahdi
    In recent years, renewable energy sources have been used in the fight against climate change, and reducing carbon dioxide emissions (CO2) is extremely promising. Solar photovoltaic (PV) generation systems are highly dependent on the operating conditions in which they are deployed to be technologically and economically viable. The instability of the weather is the main obstacle for this source. Therefore, stabilizing the PV system's output with maximum harvesting energy requires a strong control strategy. Partial shading (PS) results in substantial power losses and significantly complicates the nonlinear control task. Many metaheuristic algorithm-based Maximum Power Point Tracking (MPPT) control systems described in the literature have flaws, such as slow tracking, long settling times, and oscillations at the global point under PS conditions. This research aims to determine the most reliable MPPT approaches for PV systems and proposes two modified hybrid metaheuristic methods. These methods are the hybrid Arithmetic Optimization Algorithm (AOA) based on incremental conductance algorithm and fractional PI controller, AOA based, and AOA based artificial neural network (ANN). The first MPPT enhances the PV systems' performance by maximizing the amount of power extracted from solar panels, enabling faster and more precise tracking and reducing oscillations to a deficient level. In the second MPPT strategy, the hybrid AOA and ANN MPPT Controller is used to improve the performance of tracking the maximum power point in photovoltaic systems that are partially shaded. The hybrid AOA-ANN technique is used to study and compare the recently developed MPPT strategies of Perturb and Observe (P&O), incremental conductance (IC), Grey Wolf Optimization (GWO), MPPT-based PID controller, and MPPT-based fractional PID controller. The proposed MPPT strategies are run through a computer program called Matlab. Simulation verification has shown that the AOA-ANN-based MPPT controller is a good idea. The EN50530 standard test shows that the AOA-based fractional PI controller has an average tracking efficiency of about 99.3%. At the same time, the hybrid AOAANN control technique stands out for how easy it is to use, how strong it is, and how it can track steady-state power with an efficiency of up to 99.55%.