"Identification and experimental validation of photovoltaic parameters in desert environments using evolutionary algorithms"

Monitoring of current-voltage (I-V) characteristics in photovoltaic (PV) modules is essential for optimizing energy yield. Typically, manufacturers provide a large number of parameters for PV modules under different measurement conditions. However, these conditions often differ from real-world outdoor environments, where laboratory tests mostly use solar simulators.In this study, we present a precise computational approach for estimating the parameters of a polycrystalline photovoltaic (PV) module, specifically the IF-P155-36 model, using experimental data collected in harsh desert environments. Our method relies on the five-parameter cell model and employs various evolutionary algorithms (EAs) to accurately estimate unknown parameters.A comparison of the observed output I-V characteristics with those produced by computer simulation using MATLAB demonstrates the efficacy and robustness of the suggested approach. The proposed method attains the lowest Mean Absolute Square Error (MASE) value of 0.069267085, highlighting its superior performance in accurately estimating PV module parameters. This finding underscores the potential of our method to enhance the reliability and efficiency of photovoltaic systems in practical outdoor environments.