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Statistical evaluation of PV system performance and failure data among different climate zones

Author

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  • Halwachs, M.
  • Neumaier, L.
  • Vollert, N.
  • Maul, L.
  • Dimitriadis, S.
  • Voronko, Y.
  • Eder, G.C.
  • Omazic, A.
  • Mühleisen, W.
  • Hirschl, Ch.
  • Schwark, M.
  • Berger, K.A.
  • Ebner, R.

Abstract

The research project INFINITY focuses on design improvements for PV system components operating in different climatic regions. This topic gained pertinence in recent years due to increased installation rates worldwide with the highest growth rates in non-moderate climates. As PV components are not optimized to the respective climate zone, the target of the project is to gather failure data from PV installations in various climate zones. A standardized data input was achieved by using an extended format of the IEA PVPS Task 13 failure survey, allowing to enter PV system and failure information in rich detail. The components' and failure structure were elaborated by combining input from reliability workshop proceedings, PVPS Task 13 and project partners' experience in PV reliability testing. These structures were mapped into a database to collect and prepare the input for further statistical analysis. Collected data was filtered, offering a tidy data set of 1048 samples from 340 sub-systems from 45 countries worldwide. After data filtering, a power loss evaluation, a cluster analysis and a trend analysis were performed. This paper presents results of comparing PV module failures in different climate zones starting with a range of PV systems installed in the 1970s, to recently installed systems.

Suggested Citation

  • Halwachs, M. & Neumaier, L. & Vollert, N. & Maul, L. & Dimitriadis, S. & Voronko, Y. & Eder, G.C. & Omazic, A. & Mühleisen, W. & Hirschl, Ch. & Schwark, M. & Berger, K.A. & Ebner, R., 2019. "Statistical evaluation of PV system performance and failure data among different climate zones," Renewable Energy, Elsevier, vol. 139(C), pages 1040-1060.
    • Handle: RePEc:eee:renene:v:139:y:2019:i:c:p:1040-1060
    DOI: 10.1016/j.renene.2019.02.135
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    References listed on IDEAS

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    1. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
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    3. Sun, Yanwei & Li, Ying & Wang, Run & Ma, Renfeng, 2022. "Measuring dynamics of solar energy resource quality: Methodology and policy implications for reducing regional energy inequality," Renewable Energy, Elsevier, vol. 197(C), pages 138-150.
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    5. Kumar Ganti, Praful & Naik, Hrushikesh & Kanungo Barada, Mohanty, 2022. "Environmental impact analysis and enhancement of factors affecting the photovoltaic (PV) energy utilization in mining industry by sparrow search optimization based gradient boosting decision tree appr," Energy, Elsevier, vol. 244(PA).
    6. Gonçalves, Juliana E. & van Hooff, Twan & Saelens, Dirk, 2020. "Understanding the behaviour of naturally-ventilated BIPV modules: A sensitivity analysis," Renewable Energy, Elsevier, vol. 161(C), pages 133-148.
    7. Kumar, Manish & Kumar, Arun, 2019. "Experimental validation of performance and degradation study of canal-top photovoltaic system," Applied Energy, Elsevier, vol. 243(C), pages 102-118.
    8. Mahmoud Dhimish, 2020. "Performance Ratio and Degradation Rate Analysis of 10-Year Field Exposed Residential Photovoltaic Installations in the UK and Ireland," Clean Technol., MDPI, vol. 2(2), pages 1-14, May.
    9. Dupont, Elise & Koppelaar, Rembrandt & Jeanmart, Hervé, 2020. "Global available solar energy under physical and energy return on investment constraints," Applied Energy, Elsevier, vol. 257(C).

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