Search

Summary: 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 × 9 and 4 × 8 PV arrays, respectively. [ABSTRACT FROM AUTHOR]

Summary: The solar photovoltaic (PV) array output is reduced significantly by the frequently occurring inevitable partial shading conditions. In consequence, the array exhibits multiple peaks in its characteristics that cause the conventional maximum power point tracking (MPPT) algorithms to get stuck at the local maximum. So, to track the global maximum power (GMP) among the multiple peaks, a novel radial basis function (RBF)‐based neural network approach has been proposed for predicting the optimal GMP. Additionally, a novel and intelligent encryption‐based ruler transform (RT) reconfiguration approach is proposed to disperse the shading effect enhancing the GMP and mitigating the multiple peaks. The effectiveness of the proposed RBF‐MPPT and novel RT‐reconfiguration strategies has been tested and analyzed for a 5 × 7 PV array under distinct dynamic, uniform, and nonuniform shading conditions. The results of the proposed RBF have been compared with the conventional incremental conductance (INC) algorithm before and after reconfiguration of the PV array. Further, the ease of GMP tracking by a simple conventional INC due to the reduction of peaks after the array reconfiguration under shading conditions has been demonstrated and discussed in detail. After reconfiguration, the GMP is enhanced by 37.35%, 31.41%, 30.86%, 21.46%, 13.69%, and 8.88%, using the proposed RBF for the considered five shading conditions. The steady‐state oscillations are also considerably mitigated by employing the proposed reconfiguration and RBF strategies. [ABSTRACT FROM AUTHOR]

The photovoltaic array reconfiguration approach is the most promising solution to enhance the global maximum power (GMP) and improve the array characteristics by mitigating the variation between distinct rows, thereby reducing the computational burden on maximum power point tracking systems. However, the majority of the existing reconfiguration approaches inherently have many drawbacks such as scalability issues, poor shade dispersion, distorted array characteristics, numerous power peaks, augmented mismatch, etc. In order to address these issues, this article proposes a novel integer‐based Fibonacci transform technique using the idea of image encryption for reconfiguration. The proposed strategy is tested for both symmetric and asymmetric photovoltaic (PV) arrays under 19 shading cases. Further, its performance is compared with the 23 existing techniques. Besides, a nonparametric Wilcoxon signed‐rank sum test with a significant difference (p‐value) of 0.05 is used to perform a pairwise unbiased comparison of all the techniques. Due to the discriminate shade dispersion through intelligent reconfiguration, the proposed strategy delivers consistently superior performance yielding the respective GMP enhancement of 34.43%, 12.5%, 5.06%, and 37.39%, 22.94%, 16.5% for 9 × 9, and 4 × 8 PV arrays under distinct cases. The proposed algorithm is further validated using the Levenberg–Marquardt neural network‐based MPPT controller for a 7 kWp standalone PV system. [ABSTRACT FROM AUTHOR]

Reconfiguration plays an indispensable role in augmenting the output of the photovoltaic (PV) array under partial shading conditions. Various reconfiguration schemes have been proposed in the literature to disperse the shade in partially shaded PV arrays. Nevertheless, most of these schemes can be employed only for less‐rated arrays, are not compatible with all array sizes, fails in effective shade dispersion over the entire array, and so forth. To overcome these shortcomings, a calligraphy‐based diagonal scan pattern is proposed to reconfigure the PV array to improve the row currents of the PV array thereby reducing the mismatch between them. The superiority of the proposed scheme is examined by comparing it with conventional Series‐Parallel, Total‐Cross‐Tied, and the existing image processing based‐Chaotic Baker Map, Odd‐Even, and Odd‐Even‐Prime configurations. Besides, the system is extensively studied for various symmetrical 9 × 9, 8 × 8, 4 × 4, and unsymmetrical 8 × 6 PV arrays under 34 distinct shading cases with various performance indices. An experimental laboratory prototype model of a 4 × 4 PV array reconfiguration system is developed and studied under distinct shading conditions. Subsequently, an inclusive economic analysis is also conducted, and it is noted that there is a notable enhancement in electricity units and revenue generation by executing the proposed reconfiguration scheme. [ABSTRACT FROM AUTHOR]