This paper presents the identification of a Weiner Hammerstein model for photovoltaic (PV) systems under normal and shading operating conditions using a genetic algorithm. System identification is based on measured signals of a physical process, and the aim is to arrive at a model description of this process in the form of a dynamical system. In recent years, block-oriented models have been widely used to model non-linear systems. The Wiener-Hammerstein model consists of two linear dynamic blocks, with a nonlinear static block between them. In the simulations, different types of systems were identified by the proposed Weiner-Hammerstein model, which was optimized using a genetic algorithm. This approach is concerned with the estimation of a photovoltaic (PV) system based on observed data. The nonlinear input and output are taken from the irradiance and DC output current data of the real system, respectively. The simulation results revealed the effectiveness and robustness of the proposed model using a genetic algorithm. The simulation results show an MSE value of 0.000774 for normal operation of the PV system and 0.009863 for the shading effect between the estimated and reference information rates.
