Data Driven I-v Feature Extraction

The Data Driven I-V Feature Extraction is used to extract Current-Voltage (I-V) features from I-V curves. I-V curves indicate the relationship between current and voltage for a solar cell or Photovoltaic (PV) modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) contain important information of the performance for PV modules. The traditional method uses the single diode model to model I-V curves and extract I-V features. This package does not use the diode model, but uses data-driven a method which select different linear parts of the I-V curves to extract I-V features. This method also uses a sampling method to calculate uncertainties when extracting I-V features. Also, because of the partially shaded array, "steps" occurs in I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140.


Reference manual

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0.1.0 by Xuan Ma, 2 years ago

Browse source code at

Authors: Wei-Heng Huang [aut] , Xuan Ma [aut, cre] , Jiqi Liu [ctb] , Menghong Wang [ctb] , Alan J. Curran [ctb] , Justin S. Fada [ctb] , Jean-Nicolas Jaubert [ctb] , Jing Sun [ctb] , Jennifer L. Braid [ctb] , Jenny Brynjarsdottir [ctb] , Roger H. French [aut, cph]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports MASS, segmented

Suggests rmarkdown, testthat, knitr

Imported by SunsVoc.

See at CRAN