Accurate and Fast Convergence Technique for Enhanced Efficiency and Performance of Photovoltaic Systems

Global maximum power point tracking (GMPPT) techniques applied in photovoltaic (PV) systems under partial shading conditions (PSCs) require significant variations in the duty cycle of power electronic converters. Combined with energy storage elements in the converter, these variations lead to substantial power oscillations, complicating accurate sampling. Furthermore, a time interval is needed to ensure reliable power measurement, which affects tracking efficiency. Although various techniques have been proposed to reduce the number of interactions required to reach the maximum power point (MPP), strategies for shortening the tracking time remain underexplored. In this context, this work proposes a low-complexity acceleration strategy to reduce the tracking time in maximum power point tracking (MPPT) algorithms for PV systems. The method can be readily integrated into any MPPT technique with minimal code modification. It operates by identifying local maxima and minima in the instantaneous power curve and computing the mean value when a minimum follows a maximum. Five MPPT techniques based on different operating principles were experimentally evaluated under both uniform and non-uniform irradiance conditions. Results show that the proposed strategy reduces tracking time by 22.5% to 81.67%, improves dynamic efficiency, and does not compromise MPP accuracy. In some cases, efficiency gains increased up to 102.67%, indicating a substantial improvement in overall system performance. In all evaluated scenarios, the algorithms could reach the MPP more quickly, resulting in a significant positive impact on the overall efficiency of the PV system over its lifespan.