Enhanced Electrical Characterization of Dusty Solar Modules: Integrating Outdoor Experiments With Single- and Double-Diode Models

This study addresses a research gap regarding the impact of dust accumulation on photovoltaic (PV) modules, with a specific focus on parameter extraction using single- and double-diode models (SDMs and DDMs) under dusty conditions. While dust effects on PV performance are well-studied, few have explored how existing models can accurately represent these effects. Experimental data from outdoor testing of small-scale modules subjected to artificially deposited dust were analyzed. The direct current parameters were then extracted using the SDM and DDM, with the application of the improved snake optimization algorithm to enhance the accuracy. Preliminary analysis shows that the fill factor of dusty panels gradually increases, surpassing that of clean panels, due to increased absorption of diffuse light from reflections off the nonuniform dust layer. Efficiency uniformly decreases under dust presence. Computational comparison reveals a significant impact of dust on the algorithm’s prediction quality, with maximum root mean square error decreases of 339.1% and 303.5% for DDM and SDM, respectively. The study observes that DDM effectively represents dust effects with fewer parameters than SDM, which includes more parameters conveying dust deposition effects. On average, DDM photocurrent values decrease by 24.2% due to dust, while shunt resistance decreases by 79.7%. For SDM, photocurrent decreases by 24.2%, shunt resistance by 80.1%, diode saturation current by 84.6%, and ideality factor by 10.5%. These findings suggest that current models inadequately represent dust effects, favoring SDM for its simplicity, while partial shading serves as a weak approximation.