Identifying defects on solar cells using magnetic field measurements and artificial intelligence trained by a finite-element-model
Leipzig University of Applied Sciences, Faculty of Engineering, Leipzig, Germany
2 DENKweit GmbH, Halle, Germany
3 Hochschule Anhalt – University of Applied Sciences, Köthen, Germany
* e-mail: firstname.lastname@example.org
Received in final form: 18 October 2022
Accepted: 16 January 2023
Published online: 27 February 2023
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.
Key words: Solar cell defect detection / magnetic field imaging / neural networks / machine learning / / AI training / finite-element-analysis
© 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.