CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction
Producción Científica
| Autores: | , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Valladolid |
| Repositorio: | UVaDOC. Repositorio Documental de la Universidad de Valladolid |
| OAI Identifier: | oai:dnet:uvadoc______::676fbc878dfaf5c923c4946279296735 |
| Acceso en línea: | https://doi.org/10.3934/mbe.2026046 https://uvadoc.uva.es/handle/10324/83917 |
| Access Level: | acceso abierto |
| Palabra clave: | Photovoltaic Electroluminescence Computer Vision IV-Curve 3306 Ingeniería y Tecnología Eléctricas |
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CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power predictionMateo Romero, Héctor FelipeMorales Aragones, José IgnacioHernández Callejo, LuisGonzález Rebollo, Miguel ÁngelCardeñoso Payo, ValentínAlonso Gómez, VíctorCarbonó de la Rosa, Mario EduardoGarcía Mateos, GinésPhotovoltaicElectroluminescenceComputer VisionIV-Curve3306 Ingeniería y Tecnología EléctricasProducción CientíficaThe estimation of series resistance in photovoltaic (PV) cells is a crucial parameter that significantly influences their efficiency and overall performance. This study proposes a novel methodology to predict the slope of the current–voltage (Ⅰ–Ⅴ) curve of a PV cell in the first quadrant, where this slope (the electrical conductance) is directly associated with the series resistance of the cell. By leveraging artificial intelligence techniques, a convolutional neural network model has been developed to estimate this slope from electroluminescence (EL) images of the cells. The model was trained on a dataset consisting of EL images of PV cells with artificially induced defects, together with the corresponding slope values derived from the cells' Ⅰ–Ⅴ curves. Furthermore, this work presents a second model that combines the slope information and EL images to improve the prediction of the maximum power point (MPP) of a PV cell, surpassing previous approaches that rely solely on EL imagery. Both models demonstrated low error rates across multiple evaluation metrics, evidencing their accuracy and robustness. Additionally, comparative analysis with other machine learning methods highlights the competitive performance of the proposed approaches. These contributions provide promising tools for enhancing the assessment and diagnosis of PV cell efficiency and reliability, potentially leading to improved performance and increased longevity of photovoltaic systems.Universidad de Valladolid through the 2020 predoctoral contracts, co-funded by Santander BankSpanish Ministry of Science, Innovation, and Universities within the framework of the "Plan Estatal de Investigación Científica, Técnica y de Innovación" (project ID: PID2023-148369OB-C43)Spanish Ministry of Science and Innovation under project PID2020-113533RB-C33Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia (project 22130/PI/22)Universidad de Valladolid also supported this work through the ERASMUS+ KA-107 programMovilidad de Doctorandos y Doctorandas UVA 2024 program at the University of ValladolidAIMS Press2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3934/mbe.2026046https://uvadoc.uva.es/handle/10324/83917reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://www.aimspress.com/article/doi/10.3934/mbe.2026046info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:dnet:uvadoc______::676fbc878dfaf5c923c49462792967352026-06-13T12:44:47Z |
| dc.title.none.fl_str_mv |
CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction |
| title |
CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction |
| spellingShingle |
CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction Mateo Romero, Héctor Felipe Photovoltaic Electroluminescence Computer Vision IV-Curve 3306 Ingeniería y Tecnología Eléctricas |
| title_short |
CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction |
| title_full |
CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction |
| title_fullStr |
CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction |
| title_full_unstemmed |
CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction |
| title_sort |
CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction |
| dc.creator.none.fl_str_mv |
Mateo Romero, Héctor Felipe Morales Aragones, José Ignacio Hernández Callejo, Luis González Rebollo, Miguel Ángel Cardeñoso Payo, Valentín Alonso Gómez, Víctor Carbonó de la Rosa, Mario Eduardo García Mateos, Ginés |
| author |
Mateo Romero, Héctor Felipe |
| author_facet |
Mateo Romero, Héctor Felipe Morales Aragones, José Ignacio Hernández Callejo, Luis González Rebollo, Miguel Ángel Cardeñoso Payo, Valentín Alonso Gómez, Víctor Carbonó de la Rosa, Mario Eduardo García Mateos, Ginés |
| author_role |
author |
| author2 |
Morales Aragones, José Ignacio Hernández Callejo, Luis González Rebollo, Miguel Ángel Cardeñoso Payo, Valentín Alonso Gómez, Víctor Carbonó de la Rosa, Mario Eduardo García Mateos, Ginés |
| author2_role |
author author author author author author author |
| dc.subject.none.fl_str_mv |
Photovoltaic Electroluminescence Computer Vision IV-Curve 3306 Ingeniería y Tecnología Eléctricas |
| topic |
Photovoltaic Electroluminescence Computer Vision IV-Curve 3306 Ingeniería y Tecnología Eléctricas |
| description |
Producción Científica |
| publishDate |
2026 |
| dc.date.none.fl_str_mv |
2026 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://doi.org/10.3934/mbe.2026046 https://uvadoc.uva.es/handle/10324/83917 |
| url |
https://doi.org/10.3934/mbe.2026046 https://uvadoc.uva.es/handle/10324/83917 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
https://www.aimspress.com/article/doi/10.3934/mbe.2026046 |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ |
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application/pdf |
| dc.publisher.none.fl_str_mv |
AIMS Press |
| publisher.none.fl_str_mv |
AIMS Press |
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reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid instname:Universidad de Valladolid |
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Universidad de Valladolid |
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UVaDOC. Repositorio Documental de la Universidad de Valladolid |
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UVaDOC. Repositorio Documental de la Universidad de Valladolid |
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15,811543 |