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      Identifying defects on solar cells using magnetic field measurements and artificial intelligence trained by a finite-element-model

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          Abstract

          Renewable energies have an increasing share in the energy supply. In order to ensure the security of this supply, the reliability of the systems is therefore increasingly important. In photovoltaic modules or in manufacturing, defective solar cells due to broken busbars, cross-connectors or faulty solder joints must be detected and repaired quickly and reliably. This paper shows how the magnetic field imaging method can be used to detect defects in solar cells and modules without contact during operation. For the evaluation of the measurement data several neural networks were used, which were trained with the help of results from finite element simulations. Different training data sets were set up in the simulation model by varying the electrical conductivities of the different parts of the solar cell. The influence of the neural network type and the variation of the training data sets as well as an advantage of a combination of simulated and experimental training data are presented and discussed.

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          Most cited references10

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          FCOS: Fully Convolutional One-Stage Object Detection

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            FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults

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              Drone-Based Daylight Electroluminescence Imaging of PV Modules

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                Author and article information

                Journal
                epjpv
                https://www.epj-pv.org
                EPJ Photovoltaics
                EPJ Photovolt.
                EDP Sciences
                2105-0716
                27 February 2023
                27 February 2023
                2023
                : 14
                : ( publisher-idID: epjpv/2023/01 )
                : 12
                Affiliations
                [1 ] Leipzig University of Applied Sciences, Faculty of Engineering, , Leipzig, Germany,
                [2 ] DENKweit GmbH, , Halle, Germany,
                [3 ] Hochschule Anhalt – University of Applied Sciences, , Köthen, Germany,
                Author notes
                Article
                pv220054
                10.1051/epjpv/2023005
                264c0a13-8fa7-4f87-bd77-7df7502fa8c2
                © K. Buehler et al., Published by EDP Sciences, 2023

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 30 June 2022
                : 18 October 2022
                : 16 January 2023
                Page count
                Figures: 8, Tables: 5, Equations: 3, References: 14, Pages: 11
                Categories
                Modelling
                Special Issue on ‘WCPEC-8: State of the Art and Developments in Photovoltaics’, edited by Alessandra Scognamiglio, Robert Kenny, Shuzi Hayase and Arno Smets
                Regular Article
                Custom metadata
                EPJ Photovoltaics 14, 12 (2023)
                yes
                2023
                2023
                2023

                Sustainable & Green chemistry,Materials technology,Semiconductors,Materials for energy,Technical & Applied physics,Renewable energy
                AI training,Solar cell defect detection,magnetic field imaging,neural networks,machine learning /,finite-element-analysis

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