Module-Power Prediction from PL Measurements using Deep Learning
| Autor: | Vincent Christlein, Bernd Doll, Andreas Maier, Claudia Buerhop-Lutz, Christoph J. Brabec, Mathis Hoffmann, Ian Marius Peters, Johannes Hepp |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Power loss business.industry Computer Vision and Pattern Recognition (cs.CV) Deep learning Photovoltaic system Computer Science - Computer Vision and Pattern Recognition Mean absolute error Pattern recognition Convolutional neural network Regression Power (physics) Fraction (mathematics) Artificial intelligence business Mathematics |
| Zdroj: | 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC). |
| Popis: | 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 ± 4.0 % or 11.7 ± 9.5 W P . 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. |
| Databáze: | OpenAIRE |
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