Estimation of photovoltaic module model's parameters using an improved electromagnetic like algorithm
This paper offers an electromagnetism like (IEM) algorithm to estimate the five parameters of a single diode PV module's model. IEM uses local search and improves movement step to increase the convergence to optimal solutions. The key of improvement is performed by adding a nonlinear equation to adjust the length of the particle in each iteration. Moreover, the total force formula is simplified to speed up the exploration for an optimal solution. Analyses are carried out by experimental data points at various operational conditions to show the stability and reliability of the proposed methods. The results of the proposed IEM algorithm show a better convergence speed and high accuracy compared with other models in the literature, which involves various statistical errors. The values of average root mean square error, mean bias error, standard deviation, average absolute error, and average test statistic of the proposed method are 589% 0.51% 0.19% 46% and 0.53, respectively. As a conclusion, the IEM algorithm presents better performance than other methods in the literature in terms of accuracy and convergence.
Neural Computing and Applications