Solar Array Optimization Using Cryptographic Fibonacci Transformation for Global Power Enhancement and Ease of MPPT Controllers - Raj, Rayappa David Amar; Naik, Kanasottu Anil (Energy technology (Weinheim, Germany), 2023)
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. A novel integer‐based Fibonacci transformation using the image encryption concept for photovoltaic array reconfiguration is proposed for maximizing the global power and minimizing the local power peaks for increased ease of accurate tracking of global power by maximum power point tracking controllers. The proposed reconfiguration model is further validated using Levenberg–Marquardt neural network‐based controller.