An Effective Evaluation on Fault Detection in Solar Panels

The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy source...

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Autores principales:	Joshuva Arockia Dhanraj, Ali Mostafaeipour, Karthikeyan Velmurugan, Kuaanan Techato, Prem Kumar Chaurasiya, Jenoris Muthiya Solomon, Anitha Gopalan, Khamphe Phoungthong
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fault detection machine learning solar panel power efficiency Technology T
Acceso en línea:	https://doaj.org/article/b5e1e00b57084a7abc8132827b197089
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spelling	oai:doaj.org-article:b5e1e00b57084a7abc8132827b1970892021-11-25T17:28:38ZAn Effective Evaluation on Fault Detection in Solar Panels10.3390/en142277701996-1073https://doaj.org/article/b5e1e00b57084a7abc8132827b1970892021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7770https://doaj.org/toc/1996-1073The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.Joshuva Arockia DhanrajAli MostafaeipourKarthikeyan VelmuruganKuaanan TechatoPrem Kumar ChaurasiyaJenoris Muthiya SolomonAnitha GopalanKhamphe PhoungthongMDPI AGarticlefault detectionmachine learningsolar panelpower efficiencyTechnologyTENEnergies, Vol 14, Iss 7770, p 7770 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fault detection machine learning solar panel power efficiency Technology T
spellingShingle	fault detection machine learning solar panel power efficiency Technology T Joshuva Arockia Dhanraj Ali Mostafaeipour Karthikeyan Velmurugan Kuaanan Techato Prem Kumar Chaurasiya Jenoris Muthiya Solomon Anitha Gopalan Khamphe Phoungthong An Effective Evaluation on Fault Detection in Solar Panels
description	The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.
format	article
author	Joshuva Arockia Dhanraj Ali Mostafaeipour Karthikeyan Velmurugan Kuaanan Techato Prem Kumar Chaurasiya Jenoris Muthiya Solomon Anitha Gopalan Khamphe Phoungthong
author_facet	Joshuva Arockia Dhanraj Ali Mostafaeipour Karthikeyan Velmurugan Kuaanan Techato Prem Kumar Chaurasiya Jenoris Muthiya Solomon Anitha Gopalan Khamphe Phoungthong
author_sort	Joshuva Arockia Dhanraj
title	An Effective Evaluation on Fault Detection in Solar Panels
title_short	An Effective Evaluation on Fault Detection in Solar Panels
title_full	An Effective Evaluation on Fault Detection in Solar Panels
title_fullStr	An Effective Evaluation on Fault Detection in Solar Panels
title_full_unstemmed	An Effective Evaluation on Fault Detection in Solar Panels
title_sort	effective evaluation on fault detection in solar panels
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b5e1e00b57084a7abc8132827b197089
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An Effective Evaluation on Fault Detection in Solar Panels

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