Estimating Of Photovoltaic Cells/Modules Parameters Using A Triangular Adaptive Differential Evolution Algorithm

Background: Photovoltaic (PV) systems have become a promising renewable energy technology for electricity sources. The PV parameter estimation plays a vital role in modeling PV systems. Even though many optimization algorithms have been presented to obtain PV parameters, it is still challenging to investigate high-performance algorithms. Aim: This study aimed to propose a triangular adaptive differential evolution (TADE) algorithm to give a precise estimate of PV parameters. Methods: RTC-France PV cell, Photowatt-PWP 201 PV module, and KC200GT PV module were used as the case studies by using diode circuit models. The root mean square error (RMSE) between measured and estimated data was adopted to define PV parameter objective functions. A Friedman test was used to assess the reliability of algorithms. The parameter estimation results were cross-checked to confirm the accuracy of TADE algorithm performances. The PV module operating under various weather conditions was also performed to evaluate the TADE algorithm. Results and Discussion: The results verified that in most of the cases, the TADE algorithm surpassed other state-of-the-art optimization algorithms. For the double-diode model, the TADE algorithm obtained the RTC-France PV cell parameters with the RMSE value of 9.8243x10(-04), the most accurate of all algorithms. Experimental results also showed that the TADE algorithm presented an excellent capability and accuracy in discovering the PV parameters and provided the best estimates for I-V and P-V experimental data of real PV cells and modules. Conclusions: The results have proven that the TADE algorithm has a great performance in terms of accuracy, reliability, and convergence speed for estimating PV parameters, even in different weather conditions.