Parameters Extraction of a Photovoltaic Cell Model Using a Co-evolutionary Heterogeneous Hybrid Algorithm

Abstract

This paper proposes a new hybrid algorithm with a combination between the wind driven optimization (WDO) algorithm and the differential evolution with integrated mutation per iteration (DEIM) algorithm. The proposed algorithm, a wind driven optimization based on differential evolution with integrated mutation per iteration (WDO-based on DEIM) algorithm, is utilized to extract the unknown parameters in both of a single-diode photovoltaic (PV) cell model and a double-diode PV cell model. To show the effectiveness of the proposed model, its performance is validated internally by comparing the generated current-voltage (I-V) characteristic curves by the proposed algorithm with the actual I-V characteristic curves, and externally with those obtained by the WDO and DEIM algorithms. The results show the superiority of the proposed model. According to the normalized-root-mean-square error (nRMSE), the mean absolute percentage error (MAPE) and the coefficient of determination ($R^{2}$) of the achieved results, the proposed WDO-based on DEIM algorithm outperforms the aforementioned algorithms. Finally, the average efficiency of the WDO-based on DEIM algorithm is 95.31%, while it is 81.08% for the WDO algorithm and 88.37% for DEIM algorithm in the single-diode PV cell model. While, it is 96.78% based on WDO-based on DEIM algorithm and it is 92.30% for the WDO algorithm and 91.42% for DEIM algorithm in the double-diode PV cell model.