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The presence of bypass diodes that mitigate the negative effects of partial shading (PS) conditions produces multiple peak characteristics at the output of a photovoltaic (PV) array. Conventional maximum power point tracking (MPPT) methods develop errors under certain circumstances and detect the local maximum power point (LMPP) instead of the global maximum power point (GMPP). Several artificial intelligence (AI)-based methods have been used to modify the performance of conventional controllers. However, these methods have either not completely solved the PS problem or resulted in considerably complicated and unreliable methodologies, as well as require further development to be used in high-speed applications. This study aims to design, develop, and verify a novel rapid, reliable, and cost-effective method called adaptive radial movement optimization (ARMO) to diminish the effect of the PS problem in the MPP detection for PV systems with additional dynamic applications. The main advantages of ARMO are its improved tracking speed and a significant reduction in output fluctuations during the tracking period. An extensive experimental verification has been conducted to provide a fair evaluation of the proposed method compared with conventional and recently developed methods under similar conditions

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