Automatic Detection of Defective Solar Modules by Thermovision

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU120850" target="_blank" >RIV/00216305:26220/16:PU120850 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Detection of Defective Solar Modules by Thermovision

Popis výsledku v původním jazyce

This paper deals with the possibility of automated detection of defective solar modules by thermovision camera mounted on a dron or on the car roof. During thermovision imaging analysis of large photovoltaic power plants there are captured large numbers of images. These images are analyzed by human operators. Given the vast amount of images, which are sometimes very similar, and given of the specificity of some defects, the work of operators can be replaced by the automation recognizing of anomalies software. Two methods for the automatic detection of defects in thermal imaging pictures were developed and validated. First one method is based on the geometric information of tested modules and on the assumption that defective cell has an increased temperature along its whole surface and therefore will appear as a regular geometric shape which is recognizable by geometric comparisons. The second method does recognition by usage of trained artificial neural network.

Název v anglickém jazyce

Automatic Detection of Defective Solar Modules by Thermovision

Popis výsledku anglicky

This paper deals with the possibility of automated detection of defective solar modules by thermovision camera mounted on a dron or on the car roof. During thermovision imaging analysis of large photovoltaic power plants there are captured large numbers of images. These images are analyzed by human operators. Given the vast amount of images, which are sometimes very similar, and given of the specificity of some defects, the work of operators can be replaced by the automation recognizing of anomalies software. Two methods for the automatic detection of defects in thermal imaging pictures were developed and validated. First one method is based on the geometric information of tested modules and on the assumption that defective cell has an increased temperature along its whole surface and therefore will appear as a regular geometric shape which is recognizable by geometric comparisons. The second method does recognition by usage of trained artificial neural network.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/LO1210" target="_blank" >LO1210: Energie v podmínkách udržitelného rozvoje (EN-PUR)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů