Black Widow Optimization Algorithm Used to Extract the Parameters of Photovoltaic Cells and Panels

Abstract

The metaheuristic algorithms and their hybridization have been utilized successfully in the past to extract the parameters of photovoltaic (PV) cells and panels. The novelty of the paper consists of proposing the black widow optimization algorithm (BWOA) for the first time to identify the parameters of the two photovoltaic cells RTC France, amorphous silicon (aSi), and two photovoltaic panels PWP201, PVM 752 GaAs. The single-diode model (SDM) and double-diode model (DDM) for analyzing the PVs are considered. The performance of the BWOA is verified using four statistical tests: the root mean square error, which is the primary tool, the mean relative error, the mean bias error, and the coefficient of determination. The research results of this study are as follows: BWOA gave the same results, or very slightly better, for RTC and PWP201 for SDM in comparison with the best algorithms from the specialized literature; for all the other cases, BWOA has substantially better results, especially for PVM 752 GaAs, where the improvements in RMSE are: 16.5%, for PWP201: 6.25%, and for aSi: 5.3%, all for the DDM; the computing time is around 2 s, which is one of the lowest durations. A consistent study is made to optimize the accuracy and computational time in function of the number of iterations and population.