Turbulent Flow of Water-Based Optimization Using New Objective Function for Parameter Extraction of Six Photovoltaic Models

With the development of new energy power systems, estimation parameters photovoltaic (PV) models has become increasingly important. Weather changes are random; therefore, in PV output periodic and nonlinear. Traditional prediction methods based on linearity, relying only a time series is not feasible. Consequently, metaheuristic algorithms have received considerable attention to extract solar cell achieve excellent performance. In this study, Turbulent Flow Water-based Optimization (TFWO) used estimate three traditional models, namely, Single-Diode Solar Cell Model (SDSCM), Double-Diode (DDSCM), Three-Diode (TDSCM), addition modified SDSCM (MSDSCM), DDSCM (MDDSCM), TDSCM (MTDSCM). Moreover, polynomial equation five degrees for sum squared errors (PE5DSSE) between measured calculated currents was as objective function extracting models. The proposed delivered improved accuracy than common functions. Experimental results revealed effectiveness TFWO compared with six counterparts, “Tunicate Swarm Algorithm (TSA), Grey wolf optimizer (GWO), particle swarm optimization (MPSO), Cuckoo Search algorithm (CSA), Moth flame (MFO) Teaching Learning (TLBO),) all optimal extracted using best PE5DSSE values.