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Statistics to Detect Low-Intensity Anomalies in PV Systems

Author

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Rohatgi, Vijay K. & Székely, Gábor J., 1989. "Sharp inequalities between skewness and kurtosis," Statistics & Probability Letters, Elsevier, vol. 8(4), pages 297-299, September.
    4. 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.
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