Machine Learning in Solar Plants Inspection Automation

Jacek Starzyński (), Paweł Zawadzki and Dariusz Harańczyk
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Jacek Starzyński: Sense Software, Obrzeżna 1F/6U9, 02-691 Warszawa, Poland
Paweł Zawadzki: Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warszawa, Poland
Dariusz Harańczyk: SolarSoft, Sikorek 5, 43-300 Bielsko-Biała, Poland

Energies, 2022, vol. 15, issue 16, 1-21

Abstract: The emergence of large photovoltaic farms poses a new challenge for quick and economic diagnostics of such installations. This article presents this issue starting from a quantitative analysis of the impact of panel defects, faulty installation, and lack of farm maintenance on electricity production. We propose a low-cost and efficient method for photovoltaic (PV) plant quality surveillance that combines technologies such as an unmanned aerial vehicle (UAV), thermal imaging, and machine learning so that systematic inspection of a PV farm can be performed frequently. Most emphasis is placed on using deep neural networks to analyze thermographic images. We show how the use of the YOLO network makes it possible to develop a tool that performs the analysis of the image material already during the flyby.

Keywords: renewable sources; solar energy; photovoltaic modules inspection; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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