Next Article in Journal
Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach
Previous Article in Journal
Calibration of Mine Ventilation Network Models Using the Non-Linear Optimization Algorithm
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Energies 2018, 11(1), 30;

Statistics to Detect Low-Intensity Anomalies in PV Systems

Department of Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy
Author to whom correspondence should be addressed.
Received: 6 December 2017 / Revised: 18 December 2017 / Accepted: 20 December 2017 / Published: 23 December 2017
Full-Text   |   PDF [1895 KB, uploaded 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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Vergura, S.; Carpentieri, M. Statistics to Detect Low-Intensity Anomalies in PV Systems. Energies 2018, 11, 30.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top