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    Artificial intelligence-based maximum power point tracking controller for pv modules under partial shading conditions
    (Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2020) Alhajomar, Fuad; Kulaksız, Ahmet Afşin
    The conventional maximum power point tracking (MPPT) approaches are effective under uniform irradiance conditions; however, they fail to find the global maximum power point (GMPP) under partial shading conditions (PSCs) and are trapped in one of the local maximum power points, resulting in loss of power. To handle this issue, many modern MPPT strategies have been proposed. However, the majority of the proposed strategies are limited and suffer from various degrees of complexity and power dissipation. The scope of this thesis is to propose a new hybrid MPPT algorithm for PV systems working under PSCs and to conduct an experimental investigation to examine the performance of the proposed algorithm. The proposed new hybrid approach is based on a modified firefly algorithm (FA) and perturbation & observation (P&O) algorithm. In this approach, the firefly algorithm was modified and employed for global searching through two loops. The first loop determines the location of the GMPP and called identifying loop, and the second loop brings the operating point of the system near the GMPP and is called approximating loop. Through a third loop called the tracking loop, the perturbation & observation algorithm is used for local searching. The model of the proposed algorithm is built in the environments of MATLAB/SIMULINK and PROTEUS while the experimental study is practically conducted using a 32-bit ARM Cortex-M3 Microcontroller. The simulation and experimental results are collected under irregular irradiance conditions and partial shading conditions. The results demonstrate that the proposed algorithm exhibits superior performance in the task of finding and tracking the GMPP with an efficiency reaching 99%, shows high sensitivity in capturing any change in atmospheric conditions, reduces the steady-state oscillation around the optimal operating point and its convergence time to the GMPP is 1.3 second under complex PSCs. Furthermore, the proposed algorithm is completely independent of the characteristics of the photovoltaic panels and can be implemented using a simple digital controller without the need for sophisticated embedded systems. The results promise that the thesis study can have important implications for developing highly efficient photovoltaic generation systems considering a wide range of operating conditions.
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    Rapid control prototyping based on 32-bit ARM cortex-M3 microcontroller for photovoltaic MPPT algorithms
    (INT JOURNAL RENEWABLE ENERGY RESEARCH, 2019) Alhajomar, Fuad; Gökkuş, Göksel; Kulaksız, Ahmet Afşin
    Since the beginning of the war in Syria, most of the electricity infrastructure has been destroyed, leaving millions with unreliable energy. In such regions vulnerable to energy insecurity, an alternative means of electricity production is sought. As an attractive option, the interest is directed to solar energy. However, because of a lack of expertise in solar energy conversion and the high cost of smart technology in these regions, people have typically used photovoltaic systems in primitive ways, in which the efficiency of solar energy conversion is low. There is, therefore, a need for inexpensive, easy-to-implement, yet highly efficient and high performing solutions. STMicroelectronics 32-bit ARM as a maximum power point tracking (MPPT) controller offers a potential solution to the problem of low conversion efficiency in stand-alone solar systems. In this study, using Matlab-Simulink and STMicrelectronics-32 bit ARM board, simulation and practical test is set up to evaluate the performance of the Perturbation & Observation, Incremental Conductance, and Fuzzy Logic MPPT algorithms, in order to determine the most appropriate algorithm to use in small scale solar energy systems. Therefore, the objective of this study is to explore rapid control prototyping tools for saving time and effort to the experts in the implementation process of the proposed systems. The results indicate the effectiveness of the fuzzy logic algorithm to draw more energy, decrease oscillation and provide a fast response under variable weather conditions. Furthermore, the three algorithms were able to find and track MPP.