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Dataset for recognition of snail trails and hot spot failures in monocrystalline Si solar panels

This article presents a dataset for thermal characterization of photovoltaic systems to identify snail trails and hot spot failures. This dataset has 277 thermographic aerial images that were acquired by a Zenmuse XT IR camera (7–13 [Formula: see text] wavelength) from a DJI Matrice 100 (1)drone (qu...

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Autores principales: Alfaro-Mejía, Estefanía, Loaiza-Correa, Humberto, Franco-Mejía, Edinson, Restrepo-Girón, Andrés David, Nope-Rodríguez, Sandra Esperanza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6811936/
https://www.ncbi.nlm.nih.gov/pubmed/31667220
http://dx.doi.org/10.1016/j.dib.2019.104441
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author Alfaro-Mejía, Estefanía
Loaiza-Correa, Humberto
Franco-Mejía, Edinson
Restrepo-Girón, Andrés David
Nope-Rodríguez, Sandra Esperanza
author_facet Alfaro-Mejía, Estefanía
Loaiza-Correa, Humberto
Franco-Mejía, Edinson
Restrepo-Girón, Andrés David
Nope-Rodríguez, Sandra Esperanza
author_sort Alfaro-Mejía, Estefanía
collection PubMed
description This article presents a dataset for thermal characterization of photovoltaic systems to identify snail trails and hot spot failures. This dataset has 277 thermographic aerial images that were acquired by a Zenmuse XT IR camera (7–13 [Formula: see text] wavelength) from a DJI Matrice 100 (1)drone (quadcopter). Additionally, our dataset includes the next environmental measurements: temperature, wind speed, and irradiance. The experimental set up consisted in a photovoltaic array of 4 serial monocrystalline Si panels (string) and an electronic equipment emulating a real load. The conditions for images acquisition were stablished in a flight protocol in which we defined altitude, attitude, and weather conditions.
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spelling pubmed-68119362019-10-30 Dataset for recognition of snail trails and hot spot failures in monocrystalline Si solar panels Alfaro-Mejía, Estefanía Loaiza-Correa, Humberto Franco-Mejía, Edinson Restrepo-Girón, Andrés David Nope-Rodríguez, Sandra Esperanza Data Brief Computer Science This article presents a dataset for thermal characterization of photovoltaic systems to identify snail trails and hot spot failures. This dataset has 277 thermographic aerial images that were acquired by a Zenmuse XT IR camera (7–13 [Formula: see text] wavelength) from a DJI Matrice 100 (1)drone (quadcopter). Additionally, our dataset includes the next environmental measurements: temperature, wind speed, and irradiance. The experimental set up consisted in a photovoltaic array of 4 serial monocrystalline Si panels (string) and an electronic equipment emulating a real load. The conditions for images acquisition were stablished in a flight protocol in which we defined altitude, attitude, and weather conditions. Elsevier 2019-08-28 /pmc/articles/PMC6811936/ /pubmed/31667220 http://dx.doi.org/10.1016/j.dib.2019.104441 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Computer Science
Alfaro-Mejía, Estefanía
Loaiza-Correa, Humberto
Franco-Mejía, Edinson
Restrepo-Girón, Andrés David
Nope-Rodríguez, Sandra Esperanza
Dataset for recognition of snail trails and hot spot failures in monocrystalline Si solar panels
title Dataset for recognition of snail trails and hot spot failures in monocrystalline Si solar panels
title_full Dataset for recognition of snail trails and hot spot failures in monocrystalline Si solar panels
title_fullStr Dataset for recognition of snail trails and hot spot failures in monocrystalline Si solar panels
title_full_unstemmed Dataset for recognition of snail trails and hot spot failures in monocrystalline Si solar panels
title_short Dataset for recognition of snail trails and hot spot failures in monocrystalline Si solar panels
title_sort dataset for recognition of snail trails and hot spot failures in monocrystalline si solar panels
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6811936/
https://www.ncbi.nlm.nih.gov/pubmed/31667220
http://dx.doi.org/10.1016/j.dib.2019.104441
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