Mathematical modeling framework of a PV model using novel differential evolution algorithm

The accurate and efficient model development for photovoltaic (PV) system is crucial for potential assessment under variable operating conditions. In order to predict the performance of a PV system, this paper presents a robust and reliable hybrid method which is a combination of analytical and social learning differential evolution (SL-DE) to extract the unknown parameters of double diode PV model. Since these parameters are unknown for accurate modeling of the SPV system, the accurate parameter extraction is essential. Under the present study, the performance of four different kinds of PV modules are evaluated. The experiments have been conducted under controlled and uncontrolled environmental conditions to verify the accuracy and efficiency of the proposed technique. The results obtained from the proposed methodology can be useful to effectively predict the performance under low irradiance conditions and also for partial shading conditions. An attempt has also been made to compare the proposed technique with other popular techniques which revealed that the proposed methodology is superior with the others under similar conditions.