A new hybrid method based on differential evolution to determine the temperature-dependent parameters of single-diode photovoltaic cells

With renewable energy currently making the headlines, photovoltaic technology has shown significant potential as one of the best energy sources. It thus becomes necessary to predict the performance of photovoltaic systems by modeling it accurately and optimally. We propose a new hybrid algorithm for extracting PV cell parameters to improve their performance and efficiency when subjected to temperature variations. A metaheuristic approach is combined with an analytical approach to improve the accuracy and robustness. We call this approach improved differential evolution (IDE). The performance of the proposed method is evaluated for selected cell data. For validation, several analyses and comparisons are made with other methods, and the results illustrate the accuracy and precision of IDE. The proposed technique can estimate the parameters in an optimal way at any temperature, together with high convergence speed and a short simulation time.