05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Institute: search.filters.UBSInstitut.Institut für Signalverarbeitung und SystemtheorieStart date: 2021End date: 2021

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    ItemOpen Access
    Image preprocessing for outdoor luminescence inspection of large photovoltaic parks
    (2021) Kölblin, Pascal; Bartler, Alexander; Füller, Marvin
    Electroluminescence (EL) measurements allow one to detect damages and/or defective parts in photovoltaic systems. In principle, it seems possible to predict the complete current/voltage curve from such pictures even automatically. However, such a precise analysis requires image corrections and calibrations, because vignetting and lens distortion cause signal and spatial distortions. Earlier works on crystalline silicon modules used the cell gap joints (CGJ) as calibration pattern. Unfortunately, this procedure fails if the detection of the gaps is not accurate or if the contrast in the images is low. Here, we enhance the automated camera calibration algorithm with a reliable pattern detection and analyze quantitatively the quality of the process. Our method uses an iterative Hough transform to detect line structures and uses three key figures (KF) to separate detected busbars from cell gaps. This method allows a reliable identification of all cell gaps, even in noisy images or if disconnected edges in PV cells exist or potential induced degradation leads to a low contrast between active cell area and background. In our dataset, a subset of 30 EL images (72 cell each) forming grid (5×11) lead to consistent calibration results. We apply the calibration process to 997 single module EL images of PV modules and evaluate our results with a random subset of 40 images. After lens distortion correction and perspective correction, we analyze the residual deviation between ideal target grid points and the previously detected CGJ after applied distortion and perspective correction. For all of the 2200 control points in the 40 evaluation images, we achieve a deviation of less than or equal to 3 pixels. For 50% of the control points, a deviation of of less than or equal to 1 pixel is reached.
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    Machine learning for end-use electrical energy monitoring
    (2021) Barsim, Karim Said; Yang, Bin (Prof. Dr.-Ing.)
    Promoting end-users awareness of their usage and consumption of energy is one of the main measures towards achieving energy efficiency in buildings, which is one of the main targets in climate-aware energy transition programs. End-use energy disaggregation and monitoring is a practical and efficient approach towards achieving the targeted awareness of energy users by providing them with real-time fine-grained feedback about their own usage of energy. In this work, we address the case of electrical energy and the problem of end-use load monitoring and disaggregation in a variety of machine learning paradigms. This work starts from unsupervised energy disaggregation based on simple constraints and assumptions without the need for labeled training data. We then study and propose semi-supervised disaggregation approaches that learn from labeled observations, but are also capable of compensating for the scarcity of labeled data by leveraging unlabeled measurements. Finally, we propose a generic neural architecture for data-driven disaggregation upon availability of an abundance of training data. Results from this work not only assert the feasibility of end-use energy disaggregation, but also propose efficient models that adapt to the availability of labeled data, and are capable of monitoring different categories of end-use loads.