Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images

Beitrag in einer Fachzeitschrift
(Originalarbeit)


Details zur Publikation

Autorinnen und Autoren: Deitsch S, Christlein V, Berger S, Buerhop-Lutz C, Maier A, Gallwitz F, Rieß C
Zeitschrift: Solar Energy
Jahr der Veröffentlichung: 2019
Band: 185
Seitenbereich: 455-468
ISSN: 0038-092X
Sprache: Englisch


Abstract

Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects. In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1,968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a slightly lower average accuracy of 82.44%, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.


FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Christlein, Vincent
Lehrstuhl für Informatik 5 (Mustererkennung)
Deitsch, Sergiu
Lehrstuhl für Informatik 5 (Mustererkennung)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Rieß, Christian Dr.-Ing.
Lehrstuhl für Informatik 1 (IT-Sicherheitsinfrastrukturen)


Einrichtungen weiterer Autorinnen und Autoren

Bayerisches Zentrum für Angewandte Energieforschung e.V. (ZAE Bayern)
Technische Hochschule Nürnberg "Georg Simon Ohm"


Forschungsbereiche

Pattern Recognition & Machine Learning
Lehrstuhl für Informatik 5 (Mustererkennung)


Zitierweisen

APA:
Deitsch, S., Christlein, V., Berger, S., Buerhop-Lutz, C., Maier, A., Gallwitz, F., & Rieß, C. (2019). Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images. Solar Energy, 185, 455-468. https://dx.doi.org/10.1016/j.solener.2019.02.067

MLA:
Deitsch, Sergiu, et al. "Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images." Solar Energy 185 (2019): 455-468.

BibTeX: 

Zuletzt aktualisiert 2019-20-08 um 10:53