Automatized analysis of IR‐images of photovoltaic modules and its use for quality control of solar cells. Issue 6 (3rd November 2016)

• Record Type: Journal Article
• Authors: Hepp, Johannes; Machui, Florian; Egelhaaf, Hans‐J.; Brabec, Christoph J.; Vetter, Andreas
• Is Part Of: Energy science & engineering. Volume 4:Issue 6(2016)
• Page Start: 363
• Page End: 371
• Publication Date: 2016-11-03
• Subjects: Imaging -- IR‐thermography -- PV -- quality control -- segmentation -- solar cell; Energy industries -- Periodicals; Energy development -- Periodicals; Power resources -- Periodicals; 621.042
• DOI: 10.1002/ese3.140
• Languages: English
• ISSNs: 2050-0505

Abstract

Abstract: It is well known that the performance of solar cells may significantly suffer from local electric defects. Accordingly, infrared thermography (i.p. lock‐in thermography) has been intensely applied to identify such defects as hot spots. As an imaging method, this is a fast way of module characterization. However, imaging leads to a huge amount of data, which needs to be investigated. An automatized image analysis would be a very beneficial tool but has not been suggested so far for lock‐in thermography images. In this manuscript, we describe such an automatized analysis of solar cells. We first established a robust algorithm for segmentation (or recognition) for both, the PV‐module and the defects (hot spots). With this information, we then calculated a parameter from the IR‐images, which could be well correlated with the maximal power ( P mpp ) of the modules. The proposed automatized method serves as a very useful foundation for faster and more thorough analyses of IR‐images and stimulates the further development of quality control on solar modules. Abstract : In this manuscript, we describe an automatized analysis of infrared images of solar cells. We first established a robust algorithm for segmentation (or recognition) for both, the PV‐module and the defects (hot spots). With this information, we then calculated a parameter from the IR‐images, which could be well correlated with the maximal power ( P mpp ) of the modules.