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IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances
Infrared thermography (IRT) is a technique used to diagnose Photovoltaic (PV) installations to detect sub-optimal conditions. The increase of PV installations in smart cities has generated the search for technology that improves the use of IRT, which requires irradiance conditions to be greater than...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422287/ https://www.ncbi.nlm.nih.gov/pubmed/37571532 http://dx.doi.org/10.3390/s23156749 |
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author | Cardinale-Villalobos, Leonardo Jimenez-Delgado, Efren García-Ramírez, Yariel Araya-Solano, Luis Solís-García, Luis Antonio Méndez-Porras, Abel Alfaro-Velasco, Jorge |
author_facet | Cardinale-Villalobos, Leonardo Jimenez-Delgado, Efren García-Ramírez, Yariel Araya-Solano, Luis Solís-García, Luis Antonio Méndez-Porras, Abel Alfaro-Velasco, Jorge |
author_sort | Cardinale-Villalobos, Leonardo |
collection | PubMed |
description | Infrared thermography (IRT) is a technique used to diagnose Photovoltaic (PV) installations to detect sub-optimal conditions. The increase of PV installations in smart cities has generated the search for technology that improves the use of IRT, which requires irradiance conditions to be greater than 700 W/ [Formula: see text] , making it impossible to use at times when irradiance goes under that value. This project presents an IoT platform working on artificial intelligence (AI) which automatically detects hot spots in PV modules by analyzing the temperature differentials between modules exposed to irradiances greater than 300 W/ [Formula: see text]. For this purpose, two AI (Deep learning and machine learning) were trained and tested in a real PV installation where hot spots were induced. The system was able to detect hot spots with a sensitivity of 0.995 and an accuracy of 0.923 under dirty, short-circuited, and partially shaded conditions. This project differs from others because it proposes an alternative to facilitate the implementation of diagnostics with IRT and evaluates the real temperatures of PV modules, which represents a potential economic saving for PV installation managers and inspectors. |
format | Online Article Text |
id | pubmed-10422287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104222872023-08-13 IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances Cardinale-Villalobos, Leonardo Jimenez-Delgado, Efren García-Ramírez, Yariel Araya-Solano, Luis Solís-García, Luis Antonio Méndez-Porras, Abel Alfaro-Velasco, Jorge Sensors (Basel) Article Infrared thermography (IRT) is a technique used to diagnose Photovoltaic (PV) installations to detect sub-optimal conditions. The increase of PV installations in smart cities has generated the search for technology that improves the use of IRT, which requires irradiance conditions to be greater than 700 W/ [Formula: see text] , making it impossible to use at times when irradiance goes under that value. This project presents an IoT platform working on artificial intelligence (AI) which automatically detects hot spots in PV modules by analyzing the temperature differentials between modules exposed to irradiances greater than 300 W/ [Formula: see text]. For this purpose, two AI (Deep learning and machine learning) were trained and tested in a real PV installation where hot spots were induced. The system was able to detect hot spots with a sensitivity of 0.995 and an accuracy of 0.923 under dirty, short-circuited, and partially shaded conditions. This project differs from others because it proposes an alternative to facilitate the implementation of diagnostics with IRT and evaluates the real temperatures of PV modules, which represents a potential economic saving for PV installation managers and inspectors. MDPI 2023-07-28 /pmc/articles/PMC10422287/ /pubmed/37571532 http://dx.doi.org/10.3390/s23156749 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cardinale-Villalobos, Leonardo Jimenez-Delgado, Efren García-Ramírez, Yariel Araya-Solano, Luis Solís-García, Luis Antonio Méndez-Porras, Abel Alfaro-Velasco, Jorge IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances |
title | IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances |
title_full | IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances |
title_fullStr | IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances |
title_full_unstemmed | IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances |
title_short | IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances |
title_sort | iot system based on artificial intelligence for hot spot detection in photovoltaic modules for a wide range of irradiances |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422287/ https://www.ncbi.nlm.nih.gov/pubmed/37571532 http://dx.doi.org/10.3390/s23156749 |
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