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Open AccessArticle

Statistics to Detect Low-Intensity Anomalies in PV Systems

Department of Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy
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Energies 2018, 11(1), 30; https://doi.org/10.3390/en11010030
Received: 6 December 2017 / Revised: 18 December 2017 / Accepted: 20 December 2017 / Published: 23 December 2017
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. View Full-Text
Keywords: ANOVA; non-parametric test; unimodality; homoscedasticity; kurtosis; skewness ANOVA; non-parametric test; unimodality; homoscedasticity; kurtosis; skewness
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MDPI and ACS Style

Vergura, S.; Carpentieri, M. Statistics to Detect Low-Intensity Anomalies in PV Systems. Energies 2018, 11, 30. https://doi.org/10.3390/en11010030

AMA Style

Vergura S, Carpentieri M. Statistics to Detect Low-Intensity Anomalies in PV Systems. Energies. 2018; 11(1):30. https://doi.org/10.3390/en11010030

Chicago/Turabian Style

Vergura, Silvano; Carpentieri, Mario. 2018. "Statistics to Detect Low-Intensity Anomalies in PV Systems" Energies 11, no. 1: 30. https://doi.org/10.3390/en11010030

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