Optimum PV reconfiguration approach based on SOA for improving the harvest power under PS situations

Photovoltaic (PV) systems-based harvest energy becomes huge support for the electric grids in recent days and also becomes the sustainable replacement for conventional energy sources. But the efficacy of PV-based electricity is degraded due to the effect of partial shading conditions. The reconfiguration technique is one effective strategy to diminish the impact of shadow on the photovoltaic (PV) array. This entails rearranging the PV array’s switching matrix by the temperature and shading levels. A Seagull Optimization Algorithm (SOA) is proposed in this work to identify the optimal pattern for rearranging the matrix and optimising power output from the PV array. A 9 × 9 PV array operating under four common shadow patterns—Short Wide (SW), Long Wide (LW), Short Narrow (SN), and Long Narrow (LN)—is subjected to the algorithm. Mismatch power, fill factor, percentage power loss, and percentage power enhancement are used to assess the algorithm’s effectiveness. Comparisons are made between the results from the proposed SOA and those from the Total-Cross-Tied (TCT), Competence Square, Butterfly Optimisation Algorithm, and Harris Hawks Optimization. The highest increase in Global Maximum Power has been attained by the proposed SOA corresponding to the TCT array is 36% and 30% in the SW and LW shadow arrangement, while the least increase of 7% occurs in the SN and LN arrangements. The evaluated results show the proposed SOA’s competence and effective in optimising the shaded array reconfiguration.