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Non-Destructive Failure Detection and Visualization of Artificially and Naturally Aged PV Modules

Author

Listed:
  • Gabriele C. Eder

    (OFI Austrian Research Institute for Chemistry and Technology, Arsenal Object 213, Franz-Grill-Str. 5, 1030 Vienna, Austria)

  • Yuliya Voronko

    (OFI Austrian Research Institute for Chemistry and Technology, Arsenal Object 213, Franz-Grill-Str. 5, 1030 Vienna, Austria)

  • Christina Hirschl

    (CTR Carinthian Tech Research AG, Europastr.12, 9524 Villach, Austria)

  • Rita Ebner

    (Center for Energy, AIT Austrian Institute of Technology, Giefinggasse 2, A-1210 Vienna, Austria)

  • Gusztáv Újvári

    (Center for Energy, AIT Austrian Institute of Technology, Giefinggasse 2, A-1210 Vienna, Austria)

  • Wolfgang Mühleisen

    (CTR Carinthian Tech Research AG, Europastr.12, 9524 Villach, Austria)

Abstract

Several series of six-cell photovoltaic test-modules—intact and with deliberately generated failures (micro-cracks, cell cracks, glass breakage and connection defects)—were artificially and naturally aged. They were exposed to various stress conditions (temperature, humidity and irradiation) in different climate chambers in order to identify (i) the stress-induced effects; (ii) the potential propagation of the failures and (iii) their influence on the performance. For comparison, one set of test-modules was also aged in an outdoor test site. All photovoltaic (PV) modules were thoroughly electrically characterized by electroluminescence and performance measurements before and after the accelerated ageing and the outdoor test. In addition, the formation of fluorescence effects in the encapsulation of the test modules in the course of the accelerated ageing tests was followed over time using UV-fluorescence imaging measurements. It was found that the performance of PV test modules with mechanical module failures was rather unaffected upon storage under various stress conditions. However, numerous micro-cracks led to a higher rate of degradation. The polymeric encapsulate of the PV modules showed the build-up of distinctive fluorescence effects with increasing lifetime as the encapsulant material degraded under the influence of climatic stress factors (mainly irradiation by sunlight and elevated temperature) by forming fluorophores. The induction period for the fluorescence effects of the polymeric encapsulant to be detectable was ~1 year of outdoor weathering (in middle Europe) and 300 h of artificial irradiation (with 1000 W/m 2 artificial sunlight 300–2500 nm). In the presence of irradiation, oxygen—which permeated into the module through the polymeric backsheet—bleached the fluorescence of the encapsulant top layer between the cells, above cell cracks and micro-cracks. Thus, UV-F imaging is a perfect tool for on-site detection of module failures connected with a mechanical rupture of solar cells.

Suggested Citation

  • Gabriele C. Eder & Yuliya Voronko & Christina Hirschl & Rita Ebner & Gusztáv Újvári & Wolfgang Mühleisen, 2018. "Non-Destructive Failure Detection and Visualization of Artificially and Naturally Aged PV Modules," Energies, MDPI, vol. 11(5), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1053-:d:143114
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    References listed on IDEAS

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    1. Jinpeng Liu & Yun Long & Xiaohua Song, 2017. "A Study on the Conduction Mechanism and Evaluation of the Comprehensive Efficiency of Photovoltaic Power Generation in China," Energies, MDPI, vol. 10(5), pages 1-22, May.
    2. Daniel Gómez-Lorente & Ovidio Rabaza & Fernando Aznar-Dols & María José Mercado-Vargas, 2017. "Economic and Environmental Study of Wineries Powered by Grid-Connected Photovoltaic Systems in Spain," Energies, MDPI, vol. 10(2), pages 1-14, February.
    3. Muehleisen, Wolfgang & Eder, Gabriele C. & Voronko, Yuliya & Spielberger, Markus & Sonnleitner, Horst & Knoebl, Karl & Ebner, Rita & Ujvari, Gusztav & Hirschl, Christina, 2018. "Outdoor detection and visualization of hailstorm damages of photovoltaic plants," Renewable Energy, Elsevier, vol. 118(C), pages 138-145.
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    Cited by:

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    2. Waqar Akram, M. & Li, Guiqiang & Jin, Yi & Chen, Xiao, 2022. "Failures of Photovoltaic modules and their Detection: A Review," Applied Energy, Elsevier, vol. 313(C).
    3. Wilfried van Sark, 2019. "Photovoltaic System Design and Performance," Energies, MDPI, vol. 12(10), pages 1-6, May.
    4. Waqas Ahmed & Muhammad Umair Ali & M. A. Parvez Mahmud & Kamran Ali Khan Niazi & Amad Zafar & Tamas Kerekes, 2023. "A Comparison and Introduction of Novel Solar Panel’s Fault Diagnosis Technique Using Deep-Features Shallow-Classifier through Infrared Thermography," Energies, MDPI, vol. 16(3), pages 1-16, January.
    5. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
    6. Mühleisen, W. & Hirschl, C. & Brantegger, G. & Neumaier, L. & Spielberger, M. & Sonnleitner, H. & Kubicek, B. & Ujvari, G. & Ebner, R. & Schwark, M. & Eder, G.C. & Voronko, Y. & Knöbl, K. & Stoicescu,, 2019. "Scientific and economic comparison of outdoor characterisation methods for photovoltaic power plants," Renewable Energy, Elsevier, vol. 134(C), pages 321-329.
    7. Abdullah Ahmed Al-Dulaimi & Muhammet Tahir Guneser & Alaa Ali Hameed & Fausto Pedro García Márquez & Norma Latif Fitriyani & Muhammad Syafrudin, 2023. "Performance Analysis of Classification and Detection for PV Panel Motion Blur Images Based on Deblurring and Deep Learning Techniques," Sustainability, MDPI, vol. 15(2), pages 1-32, January.
    8. Rahman, Md Momtazur & Khan, Imran & Alameh, Kamal, 2021. "Potential measurement techniques for photovoltaic module failure diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).

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