Automatized segmentation of photovoltaic modules in IR-images with extreme noise
Vetter A, Hepp J, Brabec C (2016)
Publication Language: English
Publication Status: Published
Publication Type: Journal article, Original article
Publication year: 2016
Journal
Publisher: Elsevier
Book Volume: 76
Pages Range: 439-443
DOI: 10.1016/j.infrared.2016.03.020
Abstract
Local electric defects may result in considerable performance losses in solar cells. Infrared thermography is an essential tool to detect these defects on photovoltaic modules. Accordingly, IR-thermography is frequently used in R&D labs of PV manufactures and, furthermore, outdoors in order to identify faulty modules in PV-power plants. Massive amount of data is acquired which needs to be analyzed. An automatized method for detecting solar modules in IR-images would enable a faster and automatized analysis of the data. However, IR-images tend to suffer from rather large noise, which makes an automatized segmentation challenging. The aim of this study was to establish a reliable segmentation algorithm for R&D labs. We propose an algorithm, which detects a solar cell or module within an IR-image with large noise. We tested the algorithm on images of 10 PV-samples characterized by highly sensitive dark lock-in thermography (DLIT). The algorithm proved to be very reliable in detecting correctly the solar module. In our study, we focused on thin film solar cells, however, a transfer of the algorithm to other cell types is straight forward.
Authors with CRIS profile
Andreas Vetter
Institute Materials for Electronics and Energy Technology (i-MEET)
Christoph Brabec
Institute Materials for Electronics and Energy Technology (i-MEET)
Involved external institutions
Germany (DE)How to cite
APA:
Vetter, A., Hepp, J., & Brabec, C. (2016). Automatized segmentation of photovoltaic modules in IR-images with extreme noise. Infrared Physics & Technology, 76, 439-443. https://dx.doi.org/10.1016/j.infrared.2016.03.020
MLA:
Vetter, Andreas, Johannes Hepp, and Christoph Brabec. "Automatized segmentation of photovoltaic modules in IR-images with extreme noise." Infrared Physics & Technology 76 (2016): 439-443.
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