Parameters estimation of PV models using a novel hybrid equilibrium optimization algorithm

This study presents a novel algorithm, termed equilibrium optimizer-single candidate optimizer (EO-SCO), which combines the EO and SCO techniques. The objective of this approach is to achieve accurate and reliable parameter estimates for photovoltaic (PV) solar cells and modules. The EO-SCO, as outlined, functions through a two-phase approach. The first phase uses an equilibrium pool of elite particles to traverse the search space and find interesting places using EO, retaining solution diversity. The second phase integrates SCO to lead searching toward better vicinities and achieve a high-quality solution by employing its detecting and pattern movements to increase the proposed method's exploitation potential in the last steps. The described EO-SCO technique accurately determines PV model unknown parameters. The identification of these parameters is denoted as an objective function that must be reduced by reducing the disparities between the estimated and experimental data. The extensive findings and evaluations have confirmed that the proposed EO-SCO exhibits comparable performance to other cutting-edge technologies, particularly in relation to the quality and dependability of the solution. The findings from the simulation demonstrate that the newly proposed optimization methodology yields optimal solutions that outperform earlier techniques in terms of quality across diverse solar cell types, while also achieving the lowest root mean square error.