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.
Originalsprog | Engelsk |
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Titel | Proceedings 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 sider | 5 |
Publikationsdato | jun. 2018 |
Sider | 0753-0757 |
ISBN (Trykt) | 978-1-5386-8530-3 |
ISBN (Elektronisk) | 978-1-5386-8529-7 |
Status | Udgivet - jun. 2018 |
Begivenhed | 7th World Conference on Photovoltaic Energy Conversion - Hilton Waikoloa Village, Waikoloa, USA Varighed: 10 jun. 2018 → 15 jun. 2018 http://www.wcpec7.org/WCPEC-7/ |
Konference
Konference | 7th World Conference on Photovoltaic Energy Conversion |
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Lokation | Hilton Waikoloa Village |
Land/Område | USA |
By | Waikoloa |
Periode | 10/06/2018 → 15/06/2018 |
Internetadresse |