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

Abstract

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 shadingmore » 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Case Western Reserve Univ., Cleveland, OH (United States)
  2. Fraunhofer Inst. for Solar Energy Systems, Freiburg (Germany)
Publication Date:
Research Org.:
Case Western Reserve Univ., Cleveland, OH (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1557806
Grant/Contract Number:  
EE0007140
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Journal of Photovoltaics
Additional Journal Information:
Journal Volume: 9; Journal Issue: 5; Journal ID: ISSN 2156-3381
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; Data Science; Solar; Energy; Photovoltaics

Citation Formats

Ma, Xuan, Huang, Wei-Heng, Schnabel, Erdmut, Kohl, Michael, Brynjarsdottir, Jenny, Braid, Jennifer L., and French, Roger H. Data-Driven $I$–$V$ Feature Extraction for Photovoltaic Modules. United States: N. p., 2019. Web. doi:10.1109/JPHOTOV.2019.2928477.
Ma, Xuan, Huang, Wei-Heng, Schnabel, Erdmut, Kohl, Michael, Brynjarsdottir, Jenny, Braid, Jennifer L., & French, Roger H. Data-Driven $I$–$V$ Feature Extraction for Photovoltaic Modules. United States. doi:10.1109/JPHOTOV.2019.2928477.
Ma, Xuan, Huang, Wei-Heng, Schnabel, Erdmut, Kohl, Michael, Brynjarsdottir, Jenny, Braid, Jennifer L., and French, Roger H. Sun . "Data-Driven $I$–$V$ Feature Extraction for Photovoltaic Modules". United States. doi:10.1109/JPHOTOV.2019.2928477. https://www.osti.gov/servlets/purl/1557806.
@article{osti_1557806,
title = {Data-Driven $I$–$V$ Feature Extraction for Photovoltaic Modules},
author = {Ma, Xuan and Huang, Wei-Heng and Schnabel, Erdmut and Kohl, Michael and Brynjarsdottir, Jenny and Braid, Jennifer L. and French, Roger H.},
abstractNote = {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.},
doi = {10.1109/JPHOTOV.2019.2928477},
journal = {IEEE Journal of Photovoltaics},
number = 5,
volume = 9,
place = {United States},
year = {2019},
month = {9}
}

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