Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images

Deitsch S, Christlein V, Berger S, Buerhop-Lutz C, Maier A, Gallwitz F, Riess C (2019)

Publication Language: English

Publication Status: In review

Publication Type: Journal article, Original article

Future Publication Type: Journal article

Publication year: 2019

Journal

Solar Energy Elsevier

Book Volume: 185

Pages Range: 455-468

URI: https://arxiv.org/pdf/1807.02894.pdf

DOI: 10.1016/j.solener.2019.02.067

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.

Authors with CRIS profile

Sergiu Deitsch Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Vincent Christlein Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Christian Rieß Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)

Involved external institutions

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

How to cite

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.

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