IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v139y2019icp1040-1060.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119303003
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.02.135?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Koester, L. & Lindig, S. & Louwen, A. & Astigarraga, A. & Manzolini, G. & Moser, D., 2022. "Review of photovoltaic module degradation, field inspection techniques and techno-economic assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    2. Fouzi Harrou & Bilal Taghezouit & Sofiane Khadraoui & Abdelkader Dairi & Ying Sun & Amar Hadj Arab, 2022. "Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems," Energies, MDPI, vol. 15(18), pages 1-28, September.
    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.
    4. Aghaei, M. & Fairbrother, A. & Gok, A. & Ahmad, S. & Kazim, S. & Lobato, K. & Oreski, G. & Reinders, A. & Schmitz, J. & Theelen, M. & Yilmaz, P. & Kettle, J., 2022. "Review of degradation and failure phenomena in photovoltaic modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bakar, Khandoker Shuvo & Sahu, Sujit K., 2015. "spTimer: Spatio-Temporal Bayesian Modeling Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i15).
    2. Baştürk, Nalan & Grassi, Stefano & Hoogerheide, Lennart & Opschoor, Anne & van Dijk, Herman K., 2017. "The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i01).
    3. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.
    4. Eijffinger, Sylvester & Mahieu, Ronald & Raes, Louis, 2018. "Inferring hawks and doves from voting records," European Journal of Political Economy, Elsevier, vol. 51(C), pages 107-120.
    5. Martin Hernani Merino & Enver Gerald Tarazona Vargas & Antonieta Hamann Pastorino & José Afonso Mazzon, 2014. "Validation of Sustainable Development Practices Scale Using the Bayesian Approach to Item Response Theory," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 26(2), pages 147-162.
    6. Emmanuel Mensaklo & Chukiat Chaiboonsri & Kanchana Chokethaworn & Songsak Sriboonchitta, 2023. "Comparing Classical and Bayesian Panel Kink Regression Frameworks in Estimating the Impact of Economic Freedom on Economic Growth," Economies, MDPI, vol. 11(10), pages 1-24, October.
    7. Daniel W. Hill Jr., 2016. "Avoiding Obligation," Journal of Conflict Resolution, Peace Science Society (International), vol. 60(6), pages 1129-1158, September.
    8. Mark David Nieman, 2016. "Moments in time: Temporal patterns in the effect of democracy and trade on conflict," Conflict Management and Peace Science, Peace Science Society (International), vol. 33(3), pages 273-293, July.
    9. Paul M. Garrett & Yu-Wen Wang & Joshua P. White & Yoshihsa Kashima & Simon Dennis & Cheng-Ta Yang, 2022. "High Acceptance of COVID-19 Tracing Technologies in Taiwan: A Nationally Representative Survey Analysis," IJERPH, MDPI, vol. 19(6), pages 1-15, March.
    10. Corona, Francisco & Forrest, David & Tena, J.D. & Wiper, Michael, 2019. "Bayesian forecasting of UEFA Champions League under alternative seeding regimes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 722-732.
    11. Xu, Hao & Gardoni, Paolo, 2020. "Conditional formulation for the calibration of multi-level random fields with incomplete data," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    12. Shulgin, Sergey & Zinkina, Julia & Korotayev, Andrey, 2017. "“Neighbors in values”: A new dataset of cultural distances between countries based on individuals’ values, and its application to the study of global trade," Research in International Business and Finance, Elsevier, vol. 42(C), pages 966-985.
    13. Corona Francisco & Wiper Michael Peter & Horrillo Juan de Dios Tena, 2017. "On the importance of the probabilistic model in identifying the most decisive games in a tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(1), pages 11-23, March.
    14. Bayerstadler, Andreas & van Dijk, Linda & Winter, Fabian, 2016. "Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 244-252.
    15. Ingo Klein, 2022. "Some Technical Remarks on Negations of Discrete Probability Distributions and Their Information Loss," Mathematics, MDPI, vol. 10(20), pages 1-26, October.
    16. Murray Pollock & Paul Fearnhead & Adam M. Johansen & Gareth O. Roberts, 2020. "Quasi‐stationary Monte Carlo and the ScaLE algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1167-1221, December.
    17. Devin Caughey & James Dunham & Christopher Warshaw, 2018. "The ideological nationalization of partisan subconstituencies in the American States," Public Choice, Springer, vol. 176(1), pages 133-151, July.
    18. Fernández-i-Marín, Xavier, 2016. "ggmcmc: Analysis of MCMC Samples and Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i09).
    19. Badinger, Harald & Mühlböck, Monika & Nindl, Elisabeth & Reuter, Wolf Heinrich, 2014. "Theoretical vs. empirical power indices: Do preferences matter?," European Journal of Political Economy, Elsevier, vol. 36(C), pages 158-176.
    20. Bouranis, Lampros & Friel, Nial & Maire, Florian, 2018. "Model comparison for Gibbs random fields using noisy reversible jump Markov chain Monte Carlo," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 221-241.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:139:y:2019:i:c:p:1040-1060. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.