Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

Fouzi Harrou, Ying Sun, Bilal Taghezouit, Ahmed Saidi, Mohamed-Elkarim Hamlati

Research output: Contribution to journalArticlepeer-review

153 Scopus citations

Abstract

This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array's residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.
Original languageEnglish (US)
Pages (from-to)22-37
Number of pages16
JournalRenewable Energy
Volume116
DOIs
StatePublished - Sep 18 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality.

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