Electrical parameters estimation of single diode pv module model using hybrid metaheuristic algorihm

Abstract EN

In this paper, an improved hybrid algorithm called differential evolution with integrated mutation per iteration (DEIM) is proposed to extract five parameters of single-diode PV module model obtained by combining differential evolution (DE) algorithm and electromagnetic-like (EML) algorithm.

The EML algorithm's attraction-repulsion idea is employed in DEIM in order to enhance the mutation process of DE.

The proposed algorithm is validated with other methods using experimental I-V data.

The results of presented method reveal that simulated I-V characteristics have a high degree of agreement with experimental ones.

The proposed model has an average root mean square error of 0.062A, an olute error of 0.0452A, a mean bias error of 0.006A, a coefficient of determination of 0.992, a standard test deviation around 0.04540, and 15.33sec as execution time.

The results demonstrate that the proposed method is better in terms of the accuracy and execution time (convergence) when compared with other methods where provide less In this paper, an improved hybrid algorithm called differential evolution with integrated mutation per iteration (DEIM) is proposed to extract five parameters of single-diode PV module model obtained by combining differential evolution (DE) algorithm and electromagnetic-like (EML) algorithm.

The EML algorithm's attraction-repulsion idea is employed in DEIM in order to enhance the mutation process of DE.

The proposed algorithm is validated with other methods using experimental I-V data.

The results of presented method reveal that simulated I-V characteristics have a high degree of agreement with experimental ones.

The proposed model has an average root mean square error of 0.062A, an olute error of 0.0452A, a mean bias error of 0.006A, a coefficient of determination of 0.992, a standard test deviation around 0.04540, and 15.33sec as execution time.

The results demonstrate that the proposed method is better in terms of the accuracy and execution time (convergence) when compared with other methods where provide less errors.