Data-Driven $$I$$–$$V$$ Feature Extraction for Photovoltaic Modules

Journal Article · Sun Sep 01 00:00:00 EDT 2019 · IEEE Journal of Photovoltaics

DOI:https://doi.org/10.1109/JPHOTOV.2019.2928477· OSTI ID:1839793

Ma, Xuan  ^[1]; Huang, Wei-Heng  ^[2]; Schnabel, Erdmut  ^[3]; Kohl, Michael  ^[3]; Brynjarsdottir, Jenny  ^[1]; Braid, Jennifer L.  ^[2]; French, Roger H.  ^[2]

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1. Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, USA
2. Solar Durability and Lifetime Extension Research Center, Department of Materials Science and Engineering, Case Western Reserve University, Cleveland, OH, USA
3. Fraunhofer Institute for Solar Energy Systems, Freiburg, Germany

In research on photovoltaic (PV) device degradation, current–voltage ($$I$$–$$V$$) datasets carry a large amount of information in addition to the maximum power point. Performance parameters such as short-circuit current, open-circuit voltage, shunt resistance, series resistance, and fill factor are essential for diagnosing the performance and degradation of solar cells and modules. To enable the scaling of $$I$$–$$V$$ studies to millions of $$I$$–$$V$$ curves, we have developed a data-driven method to extract $$I$$–$$V$$ curve parameters and distributed this method as an open-source package in R. In contrast with the traditional practice of fitting the diode equation to $$I$$–$$V$$ curves individually, which requires solving a transcendental equation, this data-driven method can be applied to large volumes of $$I$$–$$V$$ data in a short time. Our data-driven feature extraction technique is tested on $$I$$–$$V$$ curves generated with the single-diode model and applied to $$I$$–$$V$$ curves with different data point densities collected from three different sources. This method has a high repeatability for extracting $$I$$–$$V$$ features, without requiring knowledge of the device or expected parameters to be input by the researcher.We also demonstrate howthis method can be applied to large datasets and accommodates nonstandard $$I$$–$$V$$ curves including those showing artifacts of connection problems or shading where bypass diode activation produces multiple “steps.” These features together make the data-driven $$I$$–$$V$$ feature extraction method ideal for evaluating time-series I–V data and analyzing power degradation mechanisms in PV modules through cross comparisons of the extracted parameters.

• Research Organization: Case Western Reserve Univ., Cleveland, OH (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• Grant/Contract Number: EE0007140
• OSTI ID: 1839793
• Alternate ID(s): OSTI ID: 1557806
• Journal Information: IEEE Journal of Photovoltaics, Journal Name: IEEE Journal of Photovoltaics Vol. 9 Journal Issue: 5; ISSN 2156-3381
• Publisher: Institute of Electrical and Electronics Engineers
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
Data Science
Solar
Energy
Photovoltaics