An improved electric circuit model for photovoltaic modules based on sensitivity analysis

• Published in: Solar energy 2013-04, Vol.90, p.29-42
• Main Authors: Siddiqui, M.U., Arif, A.F.M., Bilton, A.M., Dubowsky, S., Elshafei, M.
• Format: Article
• Language: English
• Subjects: Applied sciences; Devices; Direct energy conversion and energy accumulation; Electric circuits; Electrical design; Electrical engineering. Electrical power engineering; Electrical power engineering; Energy; Equipments, installations and applications; Equivalence; Equivalent circuit; Exact sciences and technology; Five parameter model; Mathematical analysis; Mathematical models; Miscellaneous; Modules; Natural energy; Optimization; Parameter estimation; Parameter optimization; Photoelectric conversion; Photovoltaic cells; Photovoltaic conversion; Power networks and lines; PV module; Sensitivity analysis; Solar cells. Photoelectrochemical cells; Solar energy
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2012.12.021

► A new optimization based methodology for estimating the model parameters. ► Sensitivity analysis to identify the relative importance of the model parameters. ► A new seven parameters model for PV devices is proposed. ► Only manufacturer supplied data is needed to use proposed model. Fast and accurate electrical models of PV systems are crucial for their design. Electrical models of PV devices range from very simple scaling equations to complex models requiring large amount of experimental data. The purpose of the present work is two-fold. First, an optimization methodology to estimate the model parameters for the five parameter single-diode equivalent electrical circuit model of PV devices using only manufacturer supplied data is presented and compared to three electrical models from literature for six different PV modules. Second, a sensitivity analysis is carried out to identify the relative importance of the five model parameters. Using the results of sensitivity analysis, the electrical model is improved by including two additional parameters. The suggested model modification results in marked improvement in the prediction accuracy for all six modules considered.