Enhancement of photovoltaic array characteristics and global maximum power using Padovan transform-based image encryption strategy

During shading, the mismatch between the panels in the photovoltaic (PV) array mitigates the global maximum power (GMP). Besides, the mismatch in the irradiation levels of distinct rows of the PV array instigates multiple power peaks (MPPs) in the array characteristics. Distinguishing the local and global peaks among MPPs for tracking the GMP is highly challenging for maximum power point tracking (MPPT) controllers. So, to mitigate the MPPs and enhance the GMP, array reconfiguration is preferred. Nevertheless, most existing reconfiguration techniques exhibit poor shade dispersal, distorted electrical characteristics, multiple MPPs, increased mismatch, scalability issues, etc. To overcome these challenges, this paper proposes a new Padovan transform-based encryption strategy for array reconfiguration. The proposed method was evaluated for both symmetric and unsymmetrically sized arrays. Its performance has also been compared to that of 23 other strategies. The proposed reconfiguration strategy integrated with MPPT is validated experimentally using a prototype model. A nonparametric statistical hypothesis test with a p-value of 0.05 has been used for a pairwise fair comparison study among the examined approaches. The proposed approach constantly outperforms the current methods because of its unique shade dispersion generated through intelligent reconfiguration offering the GMP improvement of 34.429%, 12.51%, 5.05%, and 37.40%, 22.93%, 16.51% for 9 x 9 and 4 x 8 PV arrays, respectively. The many peaks in the characteristics of the partially shaded PV array result in mismatch losses and MPPT controllers that are stuck at local maximum. The global maximum power (GMP) is improved by reconfiguring the PV array using an intelligent encryption technique, which also avoids power peaks. Even with a basic, inexpensive, and traditional MPPT controller, the lowering of peaks makes it easier to track GMP.image