Georeferencing of Photovoltaic Modules from Aerial Infrared Videos using Structure-from-Motion

2 code implementations • 6 Apr 2022 • Lukas Bommes, Claudia Buerhop-Lutz, Tobias Pickel, Jens Hauch, Christoph Brabec, Ian Marius Peters

Comparison with an accurate orthophoto of one of the large-scale plants yields a root mean square error of the estimated module geocoordinates of 5. 87 m and a relative error within each plant row of 0. 22 m to 0. 82 m. Finally, we use the module geocoordinates and extracted IR images to visualize distributions of module temperatures and anomaly predictions of a deep learning classifier on a map.

Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning

2 code implementations • 6 Dec 2021 • Lukas Bommes, Mathis Hoffmann, Claudia Buerhop-Lutz, Tobias Pickel, Jens Hauch, Christoph Brabec, Andreas Maier, Ian Marius Peters

Instead, we frame fault detection as more realistic unsupervised domain adaptation problem where we train on labelled data of one source PV plant and make predictions on another target plant.

Anomaly Detection Contrastive Learning +2

Computer Vision Tool for Detection, Mapping and Fault Classification of PV Modules in Aerial IR Videos

2 code implementations • 14 Jun 2021 • Lukas Bommes, Tobias Pickel, Claudia Buerhop-Lutz, Jens Hauch, Christoph Brabec, Ian Marius Peters

In this work, we develop a computer vision tool for the semi-automatic extraction of PV modules from thermographic UAV videos.

Deep Learning-based Pipeline for Module Power Prediction from EL Measurements

1 code implementation • 30 Sep 2020 • Mathis Hoffmann, Claudia Buerhop-Lutz, Luca Reeb, Tobias Pickel, Thilo Winkler, Bernd Doll, Tobias Würfl, Ian Marius Peters, Christoph Brabec, Andreas Maier, Vincent Christlein

However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string.