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Sustainability 2018, 10(3), 608; https://doi.org/10.3390/su10030608

A Statistical Tool to Detect and Locate Abnormal Operating Conditions in Photovoltaic Systems

Department of Electrical and Information Engineering, Polytechnic University of Bari, St. E. Orabona 4, I-70125 Bari, Italy
Received: 30 January 2018 / Revised: 16 February 2018 / Accepted: 21 February 2018 / Published: 27 February 2018
(This article belongs to the Collection Power System and Sustainability)
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Abstract

The paper is focused on the energy performance of the photovoltaic systems constituted by several arrays. The main idea is to compare the statistical distributions of the energy dataset of the arrays. For small-medium-size photovoltaic plant, the environmental conditions affect equally all the arrays, so the comparative procedure is independent from the solar radiation and the cell temperature; therefore, it can also be applied to a photovoltaic plant not equipped by a weather station. If the procedure is iterated and new energy data are added at each new run, the analysis becomes cumulative and allows following the trend of some benchmarks. The methodology is based on an algorithm, which suggests the user, step by step, the suitable statistical tool to use. The first one is the Hartigan’s dip test that is able to discriminate the unimodal distribution from the multimodal one. This stage is very important to decide whether a parametric test can be used or not, because the parametric tests—based on known distributions—are usually more performing than the nonparametric ones. The procedure is effective in detecting and locating abnormal operating conditions, before they become failures. A case study is proposed, based on a real operating photovoltaic plant. Three periods are separately analyzed: one month, six months, and one year. View Full-Text
Keywords: ANOVA; Hartigan’s dip test; homoscedasticity’s test; non-parametric test; unimodality; Kruskal-Wallis; Mood’s Median test ANOVA; Hartigan’s dip test; homoscedasticity’s test; non-parametric test; unimodality; Kruskal-Wallis; Mood’s Median test
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Vergura, S. A Statistical Tool to Detect and Locate Abnormal Operating Conditions in Photovoltaic Systems. Sustainability 2018, 10, 608.

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