Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/9605
Title: Fault detection method for grid-connected photovoltaic plants
Authors: Chine, W. 
Mellit, Adel 
Massi Pavan, Alessandro 
Kalogirou, Soteris A. 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Diagnostic;Fault detection;GCPV plant;Power loss
Issue Date: Jun-2014
Source: Renewable Energy, 2014, vol. 66, pp. 99-110
Volume: 66
Start page: 99
End page: 110
Journal: Renewable Energy 
Abstract: In this work, an automatic fault detection method for grid-connected photovoltaic (GCPV) plants is presented. The proposed method generates a diagnostic signal which indicates possible faults occurring in the GCPV plant. In order to determine the location of the fault, the ratio between DC and AC power is monitored. The software tool developed identifies different types of faults like: fault in a photovoltaic module, fault in a photovoltaic string, fault in an inverter, and a general fault that may include partial shading, PV ageing, or MPPT error. In addition to the diagnostic signal, other essential information about the system can be displayed each 10min on the designed tool. The method has been validated using an experimental database of climatic and electrical parameters regarding a 20kWp GCPV plant installed on the rooftop of the municipality of Trieste, Italy. The obtained results indicate that the proposed method can detect and locate correctly different type of faults in both DC and AC sides of the GCPV plant. The developed software can help users to check possible faults on their systems in real time.
URI: https://hdl.handle.net/20.500.14279/9605
ISSN: 09601481
DOI: 10.1016/j.renene.2013.11.073
Rights: © Elsevier
Type: Article
Affiliation : Jijel University 
Unité de Développement des Équipements Solaires 
University of Trieste 
Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

CORE Recommender
Show full item record

SCOPUSTM   
Citations

151
checked on Nov 9, 2023

WEB OF SCIENCETM
Citations

122
Last Week
0
Last month
1
checked on Oct 29, 2023

Page view(s)

427
Last Week
13
Last month
2
checked on Nov 23, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.