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Scientific and economic comparison of outdoor characterisation methods for photovoltaic power plants

Author

Listed:
  • 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, L.

Abstract

Over the last years, the development of innovative, fast and non-destructive characterisation techniques for the detection of PV-module failures and advanced analysis of yield losses in photovoltaic power plants has become a key challenge in scientific research. Besides standard on-site thermographic screening, several novel and easily applied methods were developed and successfully tested. They differ mainly in their possible field of application, their applicability in the detection of different failure types and their cost of setup and operation. In order to evaluate the boundary conditions of application for the various methods, different defective PV-modules were comparatively investigated with commercially available and innovative outdoor analysis methods. The results were evaluated with regard to error detection rate and economics. Based on this comparison, efficient operation and maintenance (O&M) measures for early and general failure detection within large power plants can be deducted.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:134:y:2019:i:c:p:321-329
    DOI: 10.1016/j.renene.2018.11.044
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    References listed on IDEAS

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    1. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    2. 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.
    3. 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.
    4. Belaout, A. & Krim, F. & Mellit, A. & Talbi, B. & Arabi, A., 2018. "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, Elsevier, vol. 127(C), pages 548-558.
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    2. Mik, Krzysztof & Bugaj, Marcin & Chaja, Patryk, 2021. "The evaluation of the snail track affected photovoltaic modules by different methods after 3-year operating in central Poland," Renewable Energy, Elsevier, vol. 163(C), pages 504-516.
    3. Bouaichi, Abdellatif & El Amrani, Aumeur & Ouhadou, Malika & Lfakir, Aberrazak & Messaoudi, Choukri, 2020. "In-situ performance and degradation of three different photovoltaic module technologies installed in arid climate of Morocco," Energy, Elsevier, vol. 190(C).
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    6. Tadanori Tanahashi & Shu-Tsung Hsu, 2022. "Solar cell cracks within a photovoltaic module: Characterization by AC impedance spectroscopy," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-25, November.

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