Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization
The installed capacity of solar photovoltaics has increased over the past two decades worldwide, evolving from a few small scale applications to a daily power source. Such growth involves a great impact over operating processes and maintenance practices. The RGB (red, green and blue) and infra-red m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-06-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/7/1688 |
| _version_ | 1818007214616477696 |
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| author | Oswaldo Menéndez Robert Guamán Marcelo Pérez Fernando Auat Cheein |
| author_facet | Oswaldo Menéndez Robert Guamán Marcelo Pérez Fernando Auat Cheein |
| author_sort | Oswaldo Menéndez |
| collection | DOAJ |
| description | The installed capacity of solar photovoltaics has increased over the past two decades worldwide, evolving from a few small scale applications to a daily power source. Such growth involves a great impact over operating processes and maintenance practices. The RGB (red, green and blue) and infra-red monitoring of photovoltaic modules is a non-invasive inspection method which provides information of possible failures, by relating thermal behaviour of the modules to the operational status of solar panels. An adequate thermal measurement module strongly depends on the proper camera angle selection relative to panel’s surface, since reflections and external radiation sources are common causes of misleading results with the unnecessary maintenance work. In this work, we test a portable ground-based system capable of detecting and classifying hot-spots related to photovoltaic module failures. The system characterizes in 3D thermal information from the panels structure to detect and classify hot-spots. Unlike traditional systems, our proposal detects false hot-spots associated with people or device reflections, and from external radiation sources. Experimental results show that the proposed diagnostic approach can provide of an adequate thermal monitoring of photovoltaic modules and improve existing methods in 12% of effectiveness, with the corresponding financial impact. |
| first_indexed | 2024-04-14T05:12:44Z |
| format | Article |
| id | doaj.art-922555d90100413bb571aa37a8c9ef92 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T05:12:44Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-922555d90100413bb571aa37a8c9ef922022-12-22T02:10:29ZengMDPI AGEnergies1996-10732018-06-01117168810.3390/en11071688en11071688Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact MinimizationOswaldo Menéndez0Robert Guamán1Marcelo Pérez2Fernando Auat Cheein3Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, ChileDepartment of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, ChileDepartment of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, ChileDepartment of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, ChileThe installed capacity of solar photovoltaics has increased over the past two decades worldwide, evolving from a few small scale applications to a daily power source. Such growth involves a great impact over operating processes and maintenance practices. The RGB (red, green and blue) and infra-red monitoring of photovoltaic modules is a non-invasive inspection method which provides information of possible failures, by relating thermal behaviour of the modules to the operational status of solar panels. An adequate thermal measurement module strongly depends on the proper camera angle selection relative to panel’s surface, since reflections and external radiation sources are common causes of misleading results with the unnecessary maintenance work. In this work, we test a portable ground-based system capable of detecting and classifying hot-spots related to photovoltaic module failures. The system characterizes in 3D thermal information from the panels structure to detect and classify hot-spots. Unlike traditional systems, our proposal detects false hot-spots associated with people or device reflections, and from external radiation sources. Experimental results show that the proposed diagnostic approach can provide of an adequate thermal monitoring of photovoltaic modules and improve existing methods in 12% of effectiveness, with the corresponding financial impact.http://www.mdpi.com/1996-1073/11/7/1688infrared imagingsolar panelshot-spot detectionimage processinginspection |
| spellingShingle | Oswaldo Menéndez Robert Guamán Marcelo Pérez Fernando Auat Cheein Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization Energies infrared imaging solar panels hot-spot detection image processing inspection |
| title | Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization |
| title_full | Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization |
| title_fullStr | Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization |
| title_full_unstemmed | Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization |
| title_short | Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization |
| title_sort | photovoltaic modules diagnosis using artificial vision techniques for artifact minimization |
| topic | infrared imaging solar panels hot-spot detection image processing inspection |
| url | http://www.mdpi.com/1996-1073/11/7/1688 |
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