An accurate parameters identification of solar PV models using a modified exponential distribution optimization - Tummala A.S.L.V.; Khan B.; Ali A.; Verma A.; Chawla M.P.S. (My University, 2025)
This paper presents a new metaheuristic algorithm called Exponential Distribution Optimization, which is based on mathematics and can effectively identify parameters for a solar photovoltaic (PV) mathematical model that closely approximates a real-life PV cell or module. The algorithm works in tandem with the Newton–Raphson method to address nonlinear equations, generating an objective function that minimizes the root mean square error (RMSE) between experimentally measured and estimated currents. To improve its convergence capability, the algorithm incorporates a nonlinear weight updating mechanism that adjusts the weights assigned to each member during the optimization process and this adaptive mechanism enables the algorithm to efficiently converge to the global optimal solution. The proposed approach is evaluated using two widely recognized benchmark models, and its performance is compared with that of other recent and contemporary algorithms in the literature. The proposed algorithm, modified Exponential Distribution Optimization Algorithm (mEDOA) achieves an RMSE values of 7.7298E-04 for the single diode PV model, 7.444E-04 for the double diode PV model and 7.3531E-04 for the triple diode PV model. The evaluation results demonstrate that the mEDOA surpasses other algorithms in consistency, accuracy, robustness, and speed. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.