Module-Power Prediction from PL Measurements using Deep Learning

Hoffmann M, Hepp J, Doll B, Buerhop-Lutz C, Peters IM, Brabec C, Maier A, Christlein V (2021)

Publication Status: Accepted

Publication Type: Conference contribution, Conference Contribution

Future Publication Type: Journal article

Publication year: 2021

Publisher: IEEE

Event location: Fort Lauderdale, FL, USA

URI: https://arxiv.org/pdf/2108.13640.pdf

DOI: 10.1109/PVSC43889.2021.9519005

Abstract

The individual causes for power loss of photovoltaic modules are investigated for quite some time. Recently, it has been shown that the power loss of a module is, for example, related to the fraction of inactive areas. While these areas can be easily identified from electroluminescense (EL) images, this is much harder for photoluminescence (PL) images. With this work, we close the gap between power regression from EL and PL images. We apply a deep convolutional neural network to predict the module power from PL images with a mean absolute error (MAE) of 4.4% or 11.7WP. Furthermore, we depict that regression maps computed from the embeddings of the trained network can be used to compute the localized power loss. Finally, we show that these regression maps can be used to identify inactive regions in PL images as well.

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Involved external institutions

Helmholtz-Institut Erlangen-Nürnberg für Erneuerbare Energien (HI-ERN) Germany (DE) Forschungszentrum Jülich / Research Centre Jülich (FZJ) Germany (DE)

How to cite

APA:

Hoffmann, M., Hepp, J., Doll, B., Buerhop-Lutz, C., Peters, I.M., Brabec, C.,... Christlein, V. (2021). Module-Power Prediction from PL Measurements using Deep Learning. In Proceedings of the IEEE Photovoltaic Specialists Conference. Fort Lauderdale, FL, USA: IEEE.

MLA:

Hoffmann, Mathis, et al. "Module-Power Prediction from PL Measurements using Deep Learning." Proceedings of the IEEE Photovoltaic Specialists Conference, Fort Lauderdale, FL, USA IEEE, 2021.

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