Parameter estimation of pv solar cells and modules using coati optimization algorithm

Solar energy has emerged as a sustainable alternative to fossil fuels, with photovoltaic (PV) systems playing a crucial role in converting sunlight into electricity. Optimizing the performance of PV modules requires accurate parameter estimation. This study proposes an advanced optimization approach based on the Coati Optimization Algorithm (COA), incorporating opposition-based learning and chaos theory to enhance parameter estimation accuracy and overall energy efficiency. The proposed method was tested on three PV models: SDM, DDM, and a general PV module, and compared against existing algorithms such as JSO, HHO, WOA, and GWO. The results demonstrate that the COA-based approach significantly improves performance by reducing error rates, enhancing parameter tuning, and increasing computational efficiency. Furthermore, the Friedman test revealed improvements over the basic COA in SDM, DDM, and PV modules by 8.1%, 10.79%, and 9.6%, respectively. These findings indicate that the proposed method offers meaningful contributions to the advancement of PV technology and the promotion of sustainable energy utilization.