Photovoltaic (PV) System under normal and shading operating condition Identification Based on Weiner- Hammerstein Model using genetic algorithm

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.‎