Optimal design of the modelling parameters of photovoltaic modules and array through metaheuristic with Secant method. (August 2022) - Gnetchejo, Patrick Juvet; Ndjakomo Essiane, Salomé; Dadjé, Abdouramani; Mbadjoun Wapet, Daniel; Ele, Pierre
Highlights: A modified Social Network Algorithm is used to extract the best parameters of a PV cell, module, and array. Secant method is inserted in the objective function to get the best estimated current. Comparison of MSNS-SEC with state-of-the-art methods is presented to show the efficiency of the proposed method. The differents result exhibit the effectiveness of the proposed method. The best RMSE obtained are 6.6851 × 10 −4 for the RTC cell, 1.5411 × 10 −3 for the Photowatt PWP and 0.0134 for the experimental 18 PV array. Abstract: Nowadays, the performance of photovoltaic (PV) panels is a priority research topic. Obtaining the best performance of these panels requires an adequate and accurate model. This paper employs a new approach based on a modified social network search algorithm combined with the Secant method (MSNS- SEC ) to produce the best parameters of a photovoltaic cell, module, and array. To improve the performance of the parameters to be estimated, a control parameter via a Gaussian and Cauchy distribution is randomly added to the search space to allow the agents to converge to the optimal solution. Then the Secant method is inserted into the objective function to calculate the best-estimated currents. The application of the proposed model on three different systems, namely a PV array, cell, and module, and the subsequent comparison with existing methods exhibit the high accuracy of the proposed method, with the best root mean square error of 6.6851 × 10 −4 for the RTC cell, 1.5411 × 10 −3 for the Photowatt PWP module and 0.0134 for the experimental 18 PV array.