Parameter Identification of Different Solar Photovoltaic Models Utilizing Coati and Osprey Optimization Algorithms

The usage of photovoltaic (PV) energy has increased remarkably in the latest decades, prompting wide research efforts to better recognize its mechanics. One of the primary areas of concentration in this research is the electrical modelling of PV cells and modules. Various circuits were suggested for this goal, comprising the single-diode model (SDM) and the double-diode model (DDM). The key issue is to determine the most proper circuit parameters. This study suggests a new methodology for estimating model parameters using the Coate optimization algorithm (COA) and the Osprey optimization algorithm (OOA). The COA and OOA techniques have been utilized with different PV models, including SDM and DDM, to characterize solar cells, such as the RTC France and Photowatt-PWP201. To assess the effectiveness of the suggested approach, comparative analysis has been performed with other well-known meta-heuristic techniques. The data show that the suggested methodology outperforms utilized methodologies for identifying optimal values of the unknown parameters. The results show that the algorithm's performance is critically dependent on the solar cell type, with OOA showing superior accuracy for RTC cells but showing instability for PWP201 configurations, where COA proves to be more reliable, highlighting the need to choose a cell-specific algorithm in PV parameter estimation.