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  4. Contactless Inline IV Measurement of Solar Cells Using an Empirical Model
 
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2023
Journal Article
Titel

Contactless Inline IV Measurement of Solar Cells Using an Empirical Model

Abstract
The current-voltage measurement is the most important measurement in solar cell quality control. As the contacting process of cells results in mechanical stress and consumes a significant amount of measurement time, this work presents an IV characterization based on contactless measurements only. An empirical model is introduced that can derive the full IV curve and IV parameters as the open-circuit voltage, short-circuit current density, fill factor, and efficiency. As a basis, a series of photoluminescence and contactless electroluminescence images and spectral reflectance measurements are used. An advantage of the model's convolutional neural network design lies in the semantic compression of local image structures across the input data. Within an ablation study, it is shown that the empirical model is well suited to combine these data sources, which is the optimal input configuration for contactless IV derivation. The accuracy, e.g., with an error in efficiency of (Formula presented.) and correlation of over 99%, is similar to comparing two contacting IV measurement devices. The contactless IV curves also have a close fit to their contacted counterparts. Within simulations on module level, it is demonstrated that contactless binning performs as well as contacting binning and does not result in any additional mismatch loss.
Author(s)
Kunze, Philipp
Fraunhofer-Institut für Solare Energiesysteme ISE
Greulich, Johannes
Fraunhofer-Institut für Solare Energiesysteme ISE
Tummalieh, Ammar
Fraunhofer-Institut für Solare Energiesysteme ISE
Wirtz, Wiebke
Fraunhofer-Institut für Solare Energiesysteme ISE
Höffler, Hannes
Fraunhofer-Institut für Solare Energiesysteme ISE
Wöhrle, Nico
Fraunhofer-Institut für Solare Energiesysteme ISE
Glunz, Stefan orcid-logo
Fraunhofer-Institut für Solare Energiesysteme ISE
Rein, Stefan
Fraunhofer-Institut für Solare Energiesysteme ISE
Demant, Matthias
Fraunhofer-Institut für Solare Energiesysteme ISE
Zeitschrift
Solar RRL
Project(s)
Künstliche Intelligenz und Simulation zur inline Solarzellencharakterisierung in der Photovoltaik-Produktion; Teilvorhaben: Entwicklung und Evaluation eines physikalischen Digitalen Zwillings durch Kombination von KI- und Simulationsmodellen für Solarzellen
Funder
Bundesministerium für Wirtschaft und Klimaschutz -BMWK-
Konferenz
World Conference on Photovoltaic Energy Conversion 2022
DOI
10.1002/solr.202200599
10.24406/h-425814
File(s)
Solar RRL - 2022 - Kunze - Contactless Inline IV Measurement of Solar Cells Using an Empirical Model.pdf (3.87 MB)
Language
English
google-scholar
Fraunhofer-Institut für Solare Energiesysteme ISE
Tags
  • characterization

  • contactless IV

  • deep learning

  • empirical model

  • inline

  • machine learning

  • solar cells

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