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Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning
Lukas Bommes, Mathis Hoffmann, Claudia Buerhop-Lutz +5
Increasing deployment of photovoltaic (PV) plants requires methods for automatic detection of faulty PV modules in modalities, such as infrared (IR) images. Recently, deep learning…
Module-Power Prediction from PL Measurements using Deep Learning
Mathis Hoffmann, Johannes Hepp, Bernd Doll +5
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, rela…
Computer Vision Tool for Detection, Mapping and Fault Classification of PV Modules in Aerial IR Videos
Lukas Bommes, Tobias Pickel, Claudia Buerhop-Lutz +3
Increasing deployment of photovoltaics (PV) plants demands for cheap and fast inspection. A viable tool for this task is thermographic imaging by unmanned aerial vehicles (UAV). In…
Deep Learning-based Pipeline for Module Power Prediction from EL Measurements
Mathis Hoffmann, Claudia Buerhop-Lutz, Luca Reeb +8
Automated inspection plays an important role in monitoring large-scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of d…
Fast and robust detection of solar modules in electroluminescence images
Mathis Hoffmann, Bernd Doll, Florian Talkenberg +3
Fast, non-destructive and on-site quality control tools, mainly high sensitive imaging techniques, are important to assess the reliability of photovoltaic plants. To minimize the r…