Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles

Photovoltaic solar energy is increasing its capacity in the global electric market due to its lower operating costs and higher efficiency, together with the support of the governments. Photovoltaic solar panels require high initial investments, and it is necessary to use advanced and efficient metho...

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Autores: Segovia Ramírez, Isaac, Das, Bikramaditya, García Márquez, Fausto Pedro
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/36797
Acceso en línea:https://doi.org/10.1002/pip.3479
https://hdl.handle.net/10578/36797
Access Level:acceso abierto
Palabra clave:Condition monitoring system
Fault detection and diagnosis
Pattern recognition
Photovoltaic
Radiometric sensor
Unmanned aerial vehicle
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spelling Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehiclesSegovia Ramírez, IsaacDas, BikramadityaGarcía Márquez, Fausto PedroCondition monitoring systemFault detection and diagnosisPattern recognitionPhotovoltaicRadiometric sensorUnmanned aerial vehiclePhotovoltaic solar energy is increasing its capacity in the global electric market due to its lower operating costs and higher efficiency, together with the support of the governments. Photovoltaic solar panels require high initial investments, and it is necessary to use advanced and efficient methods that lead to the maintainability and reliability of these systems, extending their life cycle and productivity. New condition monitoring systems are being applied to reduce the cost of inspections and ensure efficient data collection. The main contribution of this paper is a new efficient and low-cost condition monitoring system based on radiometric sensors. The thermal patterns of the main photovoltaic faults (hot spot, fault cell, open circuit, bypass diode, and polarization) are studied in real photovoltaic panels. Different scenarios are considered, analyzing online the main patterns of the faults by Internet of Things. The accuracies of the results are statistically analyzed and compared with fault-free scenarios. The validation of the approach is done by thermographic analysis of the experiments and employing a different radiometric sensor. The detection of faults with statistical analysis is achieved in 100% of the cases, and the identification of the specific fault in 96% of the scenarios. An artificial neural network and classification learner algorithms are employed to validate the results, obtaining similar results for fault identification, 95% and 94%, respectively, and 100% for fault detection.Wiley202420242021info:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.1002/pip.3479https://hdl.handle.net/10578/36797reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésRTC2019-007364-3 (Ministerio de Economía y Competitividad)info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/oai:ruidera.uclm.es:10578/367972026-05-27T07:36:41Z
dc.title.none.fl_str_mv Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles
title Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles
spellingShingle Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles
Segovia Ramírez, Isaac
Condition monitoring system
Fault detection and diagnosis
Pattern recognition
Photovoltaic
Radiometric sensor
Unmanned aerial vehicle
title_short Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles
title_full Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles
title_fullStr Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles
title_full_unstemmed Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles
title_sort Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles
dc.creator.none.fl_str_mv Segovia Ramírez, Isaac
Das, Bikramaditya
García Márquez, Fausto Pedro
author Segovia Ramírez, Isaac
author_facet Segovia Ramírez, Isaac
Das, Bikramaditya
García Márquez, Fausto Pedro
author_role author
author2 Das, Bikramaditya
García Márquez, Fausto Pedro
author2_role author
author
dc.subject.none.fl_str_mv Condition monitoring system
Fault detection and diagnosis
Pattern recognition
Photovoltaic
Radiometric sensor
Unmanned aerial vehicle
topic Condition monitoring system
Fault detection and diagnosis
Pattern recognition
Photovoltaic
Radiometric sensor
Unmanned aerial vehicle
description Photovoltaic solar energy is increasing its capacity in the global electric market due to its lower operating costs and higher efficiency, together with the support of the governments. Photovoltaic solar panels require high initial investments, and it is necessary to use advanced and efficient methods that lead to the maintainability and reliability of these systems, extending their life cycle and productivity. New condition monitoring systems are being applied to reduce the cost of inspections and ensure efficient data collection. The main contribution of this paper is a new efficient and low-cost condition monitoring system based on radiometric sensors. The thermal patterns of the main photovoltaic faults (hot spot, fault cell, open circuit, bypass diode, and polarization) are studied in real photovoltaic panels. Different scenarios are considered, analyzing online the main patterns of the faults by Internet of Things. The accuracies of the results are statistically analyzed and compared with fault-free scenarios. The validation of the approach is done by thermographic analysis of the experiments and employing a different radiometric sensor. The detection of faults with statistical analysis is achieved in 100% of the cases, and the identification of the specific fault in 96% of the scenarios. An artificial neural network and classification learner algorithms are employed to validate the results, obtaining similar results for fault identification, 95% and 94%, respectively, and 100% for fault detection.
publishDate 2021
dc.date.none.fl_str_mv 2021
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1002/pip.3479
https://hdl.handle.net/10578/36797
url https://doi.org/10.1002/pip.3479
https://hdl.handle.net/10578/36797
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv RTC2019-007364-3 (Ministerio de Economía y Competitividad)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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