Method for the Automated Inspection of the Surfaces of Photovoltaic Modules

Pavel Kuznetsov, Dmitry Kotelnikov (), Leonid Yuferev, Vladimir Panchenko, Vadim Bolshev (), Marek Jasiński and Aymen Flah
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Pavel Kuznetsov: Institute of Nuclear Energy and Industry, Sevastopol State University, 299053 Sevastopol, Russia
Dmitry Kotelnikov: Institute of Nuclear Energy and Industry, Sevastopol State University, 299053 Sevastopol, Russia
Leonid Yuferev: Institute of Nuclear Energy and Industry, Sevastopol State University, 299053 Sevastopol, Russia
Vladimir Panchenko: Department of Theoretical and Applied Mechanics, Russian University of Transport, 127994 Moscow, Russia
Vadim Bolshev: Laboratory of Power and Heat Supply, Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia
Marek Jasiński: WWSIS “Horyzont”, 54-239 Wrocław, Poland
Aymen Flah: National Engineering School of Gabès, Processes, Energy, Environment and Electrical Systems, University of Gabès, LR18ES34, Gabes 6072, Tunisia

Sustainability, 2022, vol. 14, issue 19, 1-16

Abstract: One of the most important conditions for the efficient operation of solar power plants with a large installed capacity is to ensure the systematic monitoring of the surface condition of the photovoltaic modules. This procedure is aimed at the timely detection of external damage to the modules, as well as their partial shading. The implementation of these measures solely through visual inspection by the maintenance personnel of the power plant requires significant labor intensity due to the large areas of the generation fields and the operating conditions. Authors propose an approach aimed at increasing the energy efficiency of high-power solar power plants by automating the inspection procedures of the surfaces of photovoltaic modules. The solution is based on the use of an unmanned aerial vehicle with a payload capable of video and geospatial data recording. To perform the procedures for detecting problem modules, it is proposed to use “object-detection” technology, which uses neural network classification methods characterized by high adaptability to various image parameters. The results of testing the technology showed that the use of a neural network based on the R-CNN architecture with the learning algorithm—Inception v2 (COCO)—allows detecting problematic photovoltaic modules with an accuracy of more than 95% on a clear day.

Keywords: monitoring; diagnostics; solar power plants; photovoltaic modules; unmanned aerial vehicles; neural networks; machine vision (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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