A robust photovoltaic array reconfiguration strategy based on coyote optimization algorithm for enhancing the extracted power under partial shadow condition

Abstract

The operation of photovoltaic (PV) array under shade faces great challenges due to the power loss that reduces the extracted power. Reconfiguration process is one of the most promising solutions to reduce the effect of shadow on the array. However, the physical array relocation methods like total cross-tied (TCT) and Su Do Ku have some limitations due to their complicated arrangements. Therefore, this paper presents a recent metaheuristic approach of coyote optimization algorithm (COA) to solve the reconfiguration process of the partially shaded PV array. The main target is maximizing the global maximum power (GMP) extracted from the array. The proposed COA is applied on 9×9 PV array operated under four standard shadow patterns which are short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN). The obtained configurations via the proposed COA method are compared to TCT, Su Do Ku, flower pollination algorithm (FPA), marine predators algorithm (MPA), and butterfly optimization algorithm (BOA) based arrangements. The best enhancement of GMP obtained via the proposed COA with respect to TCT configuration occurs in SW shadow pattern of 26.58% while the least on is 7.68% placed in SN pattern. The obtained results confirmed the competence and superiority of the proposed COA in reconfiguring the shaded array optimally.