IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2017i1p30-d124185.html
   My bibliography  Save this article

Statistics to Detect Low-Intensity Anomalies in PV Systems

Author

Listed:
  • Silvano Vergura

    (Department of Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy)

  • Mario Carpentieri

    (Department of Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy)

Abstract

The aim of this paper is the monitoring of the energy performance of Photovoltaic (PV) plants in order to detect the presence of low-intensity anomalies, before they become failures or faults. The approach is based on several statistical tools, which are applied iteratively as the data are acquired. At every loop, new data are added to the previous ones, and a proposed procedure is applied to the new dataset, therefore the analysis is carried out on cumulative data. In this way, it is possible to track some specific parameters and to monitor that identical arrays in the same operating conditions produce the same energy. The procedure is based on parametric (ANOVA) and non-parametric tests, and results effective in locating anomalies. Three cumulative case studies, based on a real operating PV plant, are analyzed.

Suggested Citation

  • Silvano Vergura & Mario Carpentieri, 2017. "Statistics to Detect Low-Intensity Anomalies in PV Systems," Energies, MDPI, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:gam:jeners:v:11:y:2017:i:1:p:30-:d:124185
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/1/30/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/1/30/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Silvestre, Santiago & Kichou, Sofiane & Chouder, Aissa & Nofuentes, Gustavo & Karatepe, Engin, 2015. "Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions," Energy, Elsevier, vol. 86(C), pages 42-50.
    2. Klaassen, Chris A. J. & Mokveld, Philip J. & van Es, Bert, 2000. "Squared skewness minus kurtosis bounded by 186/125 for unimodal distributions," Statistics & Probability Letters, Elsevier, vol. 50(2), pages 131-135, November.
    3. Silvano Vergura, 2016. "A Complete and Simplified Datasheet-Based Model of PV Cells in Variable Environmental Conditions for Circuit Simulation," Energies, MDPI, vol. 9(5), pages 1-12, April.
    4. Rohatgi, Vijay K. & Székely, Gábor J., 1989. "Sharp inequalities between skewness and kurtosis," Statistics & Probability Letters, Elsevier, vol. 8(4), pages 297-299, September.
    Full references (including those not matched with items on IDEAS)

    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. Silvano Vergura, 2018. "Hypothesis Tests-Based Analysis for Anomaly Detection in Photovoltaic Systems in the Absence of Environmental Parameters," Energies, MDPI, vol. 11(3), pages 1-18, February.
    2. Silvano Vergura, 2018. "A Statistical Tool to Detect and Locate Abnormal Operating Conditions in Photovoltaic Systems," Sustainability, MDPI, vol. 10(3), pages 1-15, February.
    3. Ogasawara, Haruhiko, 2017. "Extensions of Pearson’s inequality between skewness and kurtosis to multivariate cases," Statistics & Probability Letters, Elsevier, vol. 130(C), pages 12-16.
    4. Madi, Saida & Kheldoun, Aissa, 2017. "Bond graph based modeling for parameter identification of photovoltaic module," Energy, Elsevier, vol. 141(C), pages 1456-1465.
    5. Dhimish, Mahmoud & Holmes, Violeta & Dales, Mark, 2017. "Parallel fault detection algorithm for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 113(C), pages 94-111.
    6. Collin Barker & Sam Cipkar & Tyler Lavigne & Cameron Watson & Maher Azzouz, 2021. "Real-Time Nuisance Fault Detection in Photovoltaic Generation Systems Using a Fine Tree Classifier," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    7. Hong, Sung-Pil & Kim, Kyung min & Byeon, Geunyeong & Min, Yun-Hong, 2017. "A method to directly derive taste heterogeneity of travellers’ route choice in public transport from observed routes," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 41-52.
    8. Yen Lee & David Kaplan, 2018. "Generating Multivariate Ordinal Data via Entropy Principles," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 156-181, March.
    9. Teuscher, F. & Guiard, V., 1995. "Sharp inequalities between skewness and kurtosis for unimodal distributions," Statistics & Probability Letters, Elsevier, vol. 22(3), pages 257-260, February.
    10. Karagiorgis, Ariston & Drakos, Konstantinos, 2022. "The Skewness-Kurtosis plane for non-Gaussian systems: The case of hedge fund returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    11. Han, Youhua & Liu, Yang & Lu, Shixiang & Basalike, Pie & Zhang, Jili, 2021. "Electrical performance and power prediction of a roll-bond photovoltaic thermal array under dewing and frosting conditions," Energy, Elsevier, vol. 237(C).
    12. Shen, Yu & He, Zengxiang & Xu, Zhen & Wang, Yiye & Li, Chenxi & Zhang, Jinxia & Zhang, Kanjian & Wei, Haikun, 2022. "Modeling of photovoltaic modules under common shading conditions," Energy, Elsevier, vol. 256(C).
    13. Das, Saborni & Hazra, Abhik & Basu, Mousumi, 2018. "Metaheuristic optimization based fault diagnosis strategy for solar photovoltaic systems under non-uniform irradiance," Renewable Energy, Elsevier, vol. 118(C), pages 452-467.
    14. Robert Jane & Gordon Parker & Gail Vaucher & Morris Berman, 2020. "Characterizing Meteorological Forecast Impact on Microgrid Optimization Performance and Design," Energies, MDPI, vol. 13(3), pages 1-23, January.
    15. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
    16. Singh, Rashmi & Sharma, Madhu & Rawat, Rahul & Banerjee, Chandan, 2018. "An assessment of series resistance estimation techniques for different silicon based SPV modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 199-216.
    17. Søren Asmussen, 2022. "On the role of skewness and kurtosis in tempered stable (CGMY) Lévy models in finance," Finance and Stochastics, Springer, vol. 26(3), pages 383-416, July.
    18. Kichou, Sofiane & Silvestre, Santiago & Guglielminotti, Letizia & Mora-López, Llanos & Muñoz-Cerón, Emilio, 2016. "Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification," Renewable Energy, Elsevier, vol. 99(C), pages 270-279.
    19. Vincenzo Stornelli & Mirco Muttillo & Tullio de Rubeis & Iole Nardi, 2019. "A New Simplified Five-Parameter Estimation Method for Single-Diode Model of Photovoltaic Panels," Energies, MDPI, vol. 12(22), pages 1-20, November.
    20. Jong-Chan Kim & Jun-Ho Huh & Jae-Sub Ko, 2019. "Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid," Sustainability, MDPI, vol. 11(21), pages 1-27, October.

    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:gam:jeners:v:11:y:2017:i:1:p:30-:d:124185. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.