A single-diode model for photovoltaic panels in variable environmental conditions: Investigating dust impacts with experimental evaluation. (October 2021)

• Record Type: Journal Article
• Authors: Gholami, Aslan; Ameri, Mohammad; Zandi, Majid; Gavagsaz Ghoachani, Roghayeh
• Is Part Of: Sustainable energy technologies and assessments. Volume 47(2021)
• Publication Date: 2021-10
• Subjects: Single-diode model -- Photovoltaic -- Dust accumulation -- Irradiation level -- Cell temperature; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2021.101392
• Languages: English
• ISSNs: 2213-1388

Abstract

Highlights: Presenting a Single-Diode Model to predict the electrical behavior of PV systems. Providing a non-iterative analytical approach to extract model parameters. Investigating the effect of dust accumulation on the model parameters. Experimental evaluation of the model under 500 different environmental conditions. Improving 25–35% accuracy while decreasing the computational time. Abstract: The current study utilized a single-diode equivalent electrical circuit with a non-iterative parameter extraction approach and innovative modifications to predict the photovoltaic modules' electrical behavior in variable environmental conditions. The model's outputs were validated using both the presented data in the module's datasheet and experimental investigations under more than 500 different real conditions. For the first time, besides irradiation and temperature, dust accumulation effects on the single-diode model's main parameters have been investigated to predict the module's electrical behavior under real environmental conditions. Comparing and simulating more than 13, 200 different cases, it was shown that by considering dust effects, respectively, the series resistance, parallel resistance, ideality factor, reverse saturation current, and photocurrent source could be defined with up to 25%, 40%, 9%, 35%, and 40% more accuracy. The trend and dependencies of the prediction improvements for each parameter were also investigated thoroughly. Finally, compared with experimental results in more than 500 different cases showed that, on average, the proposed model improved the prediction of the module's output electrical behavior by 25% to 35%. The proposed model could be easily adjusted and applied to predict photovoltaic modules' output behavior under diverse environmental conditions.