Module-Power Prediction from PL Measurements using Deep Learning

• URL: http://arxiv.org/abs/2108.13640v1
• Date: Tue, 31 Aug 2021 06:43:03 GMT
• Title: Module-Power Prediction from PL Measurements using Deep Learning
• Authors: Mathis Hoffmann, Johannes Hepp, Bernd Doll, Claudia Buerhop-Lutz, Ian Marius Peters, Christoph Brabec, Andreas Maier, Vincent Christlein
• Abstract summary: 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. We depict that regression maps computed from the embeddings of the trained network can be used to compute the localized power loss.
• Score: 6.470549137572311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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