<![CDATA[Standalone photovoltaic (SPV) systems play a critical role in delivering clean energy to remote areas; however, maintaining consistent maximum power point tracking (MPPT) under dynamic environmental conditions remains a significant challenge. This paper proposes a hybrid artificial neural network–particle swarm optimization (ANN-PSO) based MPPT algorithm, integrated with a high-gain boost converter (HGBC), to overcome these limitations. The hybrid approach leverages the predictive capacity of ANN and the global optimization strength of PSO to achieve accurate and rapid tracking of the maximum power point under fluctuating irradiance. In addition, the high-gain converter improves voltage amplification and reduces power losses, improving overall system efficiency. The simulation results in MATLAB/Simulink confirm that the proposed system achieves a 99.7% tracking efficiency, faster convergence than conventional MPPT techniques, and significantly reduced power ripple. These results indicate that the proposed strategy can improve energy harvesting and operational stability in SPV applications. In addition, it offers a scalable and cost-effective solution suitable for off-grid electrification, particularly in rural and underdeveloped regions, contributing to global renewable energy goals.]]>
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