An improved RCGA for Parameter extraction of three-diode PV model

In recent years, it was noticed an increased involvement of the renewable energy resources such as the solar photovoltaics (PV's) to share into covering the increased load requirements in modern power systems. Electrical ciruits are developed to model PV cells/modules. Parameter extraction of the appropriate PV modules is one of the hot research topics that attract researchers' for possible assessing the performance and operation and control issues of PVs. Parameters estimation of PV cells is a highly nonlinear optimization problem, in this regard several optimization algorithms are exploited to tackle this challenge. In this study, the improved real coded genetic algorithm (IRGA) spreads an enhanced performance of the well-known genetic algorithms. The IRGA investigated parameter extraction of the most accurate diode PV module model for PV commercial modules. The attained result of the IRGA shows the high accuracy of the estimated parameters. This can be validated from the examination of the identified parameters to plot the I-v curve and show the accompaniment of the estimated and measured values. Moreover, the IRGA convergence curve shows up a competitive convergence rate and robustness when assessed with other recent optimization algorithms.