Binary Classification of Defective Solar PV Modules Using Thermography

Kamran Ali Khan Niazi, Wajahat Akhtar, Hassan Abbas Khan, Sarmad Sohaib, Ahmad Kamal Nasir

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17 Citationer (Scopus)
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Abstract

Photovoltaic (PV) modules are subject to various internal or external stresses due to their operation in solar PV based power systems. Therefore, monitoring and maintenance are critical issues to ensure reliability of PV modules which in turn would affect the reliability of any PV system. In this paper, we categorize operational solar panels into two categories (Defective and Non-Defective panels) using a machine learning technique i.e. texture features through thermography assessment. Further, the panels are also categorized for diagnostic perspective using nBayes classifier. Results from an investigation for a 42.24 kWp PV system showed a mean recognition rate of 98.4% for a set of 260 test samples.
OriginalsprogEngelsk
TitelProceedings of the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)
Antal sider5
Publikationsdatojun. 2018
Sider0753-0757
ISBN (Trykt)978-1-5386-8530-3
ISBN (Elektronisk)978-1-5386-8529-7
StatusUdgivet - jun. 2018
Begivenhed7th World Conference on Photovoltaic Energy Conversion - Hilton Waikoloa Village, Waikoloa, USA
Varighed: 10 jun. 201815 jun. 2018
http://www.wcpec7.org/WCPEC-7/

Konference

Konference7th World Conference on Photovoltaic Energy Conversion
LokationHilton Waikoloa Village
Land/OmrådeUSA
ByWaikoloa
Periode10/06/201815/06/2018
Internetadresse

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