<![CDATA[Partial shading often introduces multiple local maxima into the power–voltage (P-V) characteristics of photovoltaic (PV) systems, which makes conventional maximum power point tracking (MPPT) methods prone to converging to suboptimal operating points. To overcome this challenge, this study proposes an Enhanced Artificial Rabbit Optimization (ARO)-based MPPT method by incorporating a time-varying adaptive inertia-weight mechanism, enabling more accurate global maximum power point (GMPP) tracking under nonlinear irradiance conditions. The proposed method was experimentally validated on a laboratory-scale PV platform consisting of three series-connected PV modules, a SEPIC converter, and an STM32-based controller tested under four irradiance patterns. The GMPP reference values were determined through direct experimental P-V scanning. The experimental results indicate that the enhanced method consistently performed better than the conventional ARO baseline, achieving a maximum output power of 98.68 W with a tracking accuracy of 99.93%. Across all test cases, the extracted power and tracking accuracy improved by an average of 1.24% and 1.21%, respectively, without a noticeable increase in tracking time.]]>
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