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
SCOPUSTM
Citations
151
checked on Nov 9, 2023
WEB OF SCIENCETM
Citations
122
Last Week
0
0
Last month
1
1
checked on Oct 29, 2023
Page view(s)
427
Last Week
13
13
Last month
2
2
checked on Nov 23, 2024
Google ScholarTM
Check
Altmetric
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.