<![CDATA[Between 2015 and 2024, global solar photovoltaic (PV) capacity rose significantly from 223.204 GW to 1624 GW, contributing to the reduction of greenhouse gas emissions associated with fossil-fuel-based power generation. Solar PV is recognized for its environmental benefits and is increasingly seen as a viable alternative for a long-term sustainable energy supply. However, the power output of PV systems is highly dependent on atmospheric conditions, particularly solar irradiation and temperature, which can cause fluctuations and reduce overall efficiency. To address this, maximum power point tracking (MPPT) techniques are employed to optimize energy extraction under varying environmental conditions. This study presents a comparative analysis of four MPPT algorithms, perturb-and-observe (P&O), incremental conductance (InC), fuzzy logic control (FLC), and artificial neural network (ANN) for grid-tied PV systems using MATLAB/Simulink. Each algorithm was evaluated under dynamic conditions to determine its tracking efficiency and responsiveness. The results show that while conventional methods like P&O and InC are simpler, they are less effective under rapidly changing conditions. FLC demonstrates faster convergence but requires greater computational resources. The intelligent controllers demonstrated superior performance: FLC achieved the highest power output of 1.019×10⁶ W with a corresponding voltage of 1.422×10⁴ V, while the ANN algorithm followed closely with 9.650×10⁵ W and 1.200×10⁴ V, respectively. The comparative insights gained from this analysis offer practical guidance for selecting MPPT controllers in real-world solar energy applications.]]>
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