Optimal reconfiguration of shaded PV based system using African vultures optimization approach

The photovoltaic (PV) system operation faces great challenges as its performance depends on the weather conditions like irradiance and temperature. One of the phenomena that has negative effect on the PV array is operation under partial shade condition (PSC) as it causes hot spots, increases the power loss, and reduces the generated power. Therefore, this work proposes recent methodology incorporated metaheuristic approach named African vultures optimization algorithm (AVOA) that is applied for the first time to reconfigure the PV array operated at PSC for maximizing the generated power. The merit of AVOA is its high ability to escape from the local optima. Five shade patterns of short wide (SW), long wide (LW), short narrow (SN), long narrow (LN), and lower triangle are analyzed. Moreover, comparison to total cross tied (TCT), SudoKu, harris hawks optimizer (HHO), Aquila optimizer (AO), and antlion optimizer (ALO) is conducted. Furthermore, comparative analysis in terms of fill factor (FF), power enhancement (Pe) with respect to TCT arrangement, power loss, and performance ratio (PR) is conducted. The proposed AVOA outperformed the others in terms of the power enhancement and performance ratio. The best Pe obtained via the proposed AVOA is 39.91% in the fifth shade pattern while the best PR is 82.9125% in the third pattern. Additionally, Wilcoxon sign rank, Friedman, ANOVA table, and multiple comparison tests are performed. The results demonstrated that, AVOA results are significantly different from HHO over the five studied cases. The reported p-values based on Friedman and ANOVA illustrated the existence of significant differences among algorithms. The best p-values for Friedman and ANOVA are 9.4725e−08 and 7.3013e−13 in the fourth and fifth patterns respectively. The results confirmed the preference of the proposed AVOA in achieving the best reconfiguration of the PV array at PSC. Chaotic mapping is recommended to adaptively set the proposed AVOA parameters.