Comparing MPPT Algorithms for Improved Partial-Shaded PV Power Generations

Abstract

olar energy, accepted as an alternative energy source, is attracting commercial interest and scholars and researchers for improving efficiency and lowering the losses within the system. One of these significant losses is due to partial and complex shading. This study concentrates on reducing losses to enhance the efficiency of solar systems. Maximum Power Point Tracking (MPTT) uses several alternative algorithms for efficient operations. We have selected four algorithms supporting MPPT, namely P&O, PSO, Adaptive cuckoo, and Dragonfly. These algorithms are applied on photovoltaic (PV) systems in four different scenarios: uniform irradiance, partial shading, complex partial shading, and multiple local maximum power points. According to this study, results show that the algorithms' performance vary significantly based on these scenarios. It has been shown that PSO has the longest tracking time compared to other but tracks the maximum power best when exposed to uniform irradiance. In contrast, DFO takes the shortest tracking time and performs best in I-V curves but do not have a maximum power point at the knee. Both adaptive cuckoo and PSO perform well in tracking the global maximum power point, particularly in partial shadings. The study provides insights into the strengths and weaknesses of each algorithm in different scenarios and can guide the selection of an appropriate algorithm for a given PV system.