Decoding pixels: A modular software prototype for cognitive image-based diagnostics of PV plants
Although aerial infrared (aIRT) imagery-based solutions for diagnostics of PV plants demonstrate impressive time-efficiency and spatial resolution, they also suffer from considerable drawbacks: limited automation (hence, expert dependence) and insufficient quantitative insights. In this paper, we in...
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EDP Sciences
2025-01-01
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| Series: | EPJ Photovoltaics |
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| Online Access: | https://www.epj-pv.org/articles/epjpv/full_html/2025/01/pv20240066/pv20240066.html |
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| author | Tsanakas John Ioannis A. Marechal Philippe |
| author_facet | Tsanakas John Ioannis A. Marechal Philippe |
| author_sort | Tsanakas John Ioannis A. |
| collection | DOAJ |
| description | Although aerial infrared (aIRT) imagery-based solutions for diagnostics of PV plants demonstrate impressive time-efficiency and spatial resolution, they also suffer from considerable drawbacks: limited automation (hence, expert dependence) and insufficient quantitative insights. In this paper, we introduce a software prototype, evolved from an innovative diagnostics framework researched and developed by CEA-INES over the last years, which integrates aIRT imagery with deep learning-based algorithms and physical/electrical modeling. With such an approach, unlike conventional ones, we worked on reaching both qualitative fault detection and quantitative (power loss) insights, with a focus on various spatial granularity levels within PV systems. Leveraging advanced deep learning techniques, first results show that we can achieve automated PV module detection and fault identification/classification, with associated power loss analysis at PV system, string/inverter, or module level. Further real-life validation efforts are underway, in utility-scale PV plants. Future developments aim to enhance further enhance our PV diagnostic framework, through data fusion with SCADA outputs and integration with maintenance and end-of-life (EoL) management tools. |
| format | Article |
| id | doaj-art-ef3371d4f21344b2bd919175cc1571f4 |
| institution | DOAJ |
| issn | 2105-0716 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Photovoltaics |
| spelling | doaj-art-ef3371d4f21344b2bd919175cc1571f42025-08-20T03:08:47ZengEDP SciencesEPJ Photovoltaics2105-07162025-01-01162410.1051/epjpv/2025013pv20240066Decoding pixels: A modular software prototype for cognitive image-based diagnostics of PV plantsTsanakas John Ioannis A.0https://orcid.org/0000-0002-8230-4635Marechal Philippe1CEA, Liten, Univ. Grenoble Alpes Campus INESCEA, Liten, Univ. Grenoble Alpes Campus INESAlthough aerial infrared (aIRT) imagery-based solutions for diagnostics of PV plants demonstrate impressive time-efficiency and spatial resolution, they also suffer from considerable drawbacks: limited automation (hence, expert dependence) and insufficient quantitative insights. In this paper, we introduce a software prototype, evolved from an innovative diagnostics framework researched and developed by CEA-INES over the last years, which integrates aIRT imagery with deep learning-based algorithms and physical/electrical modeling. With such an approach, unlike conventional ones, we worked on reaching both qualitative fault detection and quantitative (power loss) insights, with a focus on various spatial granularity levels within PV systems. Leveraging advanced deep learning techniques, first results show that we can achieve automated PV module detection and fault identification/classification, with associated power loss analysis at PV system, string/inverter, or module level. Further real-life validation efforts are underway, in utility-scale PV plants. Future developments aim to enhance further enhance our PV diagnostic framework, through data fusion with SCADA outputs and integration with maintenance and end-of-life (EoL) management tools.https://www.epj-pv.org/articles/epjpv/full_html/2025/01/pv20240066/pv20240066.htmlpv systemsfault diagnosticsdeep learningthermal imagery |
| spellingShingle | Tsanakas John Ioannis A. Marechal Philippe Decoding pixels: A modular software prototype for cognitive image-based diagnostics of PV plants EPJ Photovoltaics pv systems fault diagnostics deep learning thermal imagery |
| title | Decoding pixels: A modular software prototype for cognitive image-based diagnostics of PV plants |
| title_full | Decoding pixels: A modular software prototype for cognitive image-based diagnostics of PV plants |
| title_fullStr | Decoding pixels: A modular software prototype for cognitive image-based diagnostics of PV plants |
| title_full_unstemmed | Decoding pixels: A modular software prototype for cognitive image-based diagnostics of PV plants |
| title_short | Decoding pixels: A modular software prototype for cognitive image-based diagnostics of PV plants |
| title_sort | decoding pixels a modular software prototype for cognitive image based diagnostics of pv plants |
| topic | pv systems fault diagnostics deep learning thermal imagery |
| url | https://www.epj-pv.org/articles/epjpv/full_html/2025/01/pv20240066/pv20240066.html |
| work_keys_str_mv | AT tsanakasjohnioannisa decodingpixelsamodularsoftwareprototypeforcognitiveimagebaseddiagnosticsofpvplants AT marechalphilippe decodingpixelsamodularsoftwareprototypeforcognitiveimagebaseddiagnosticsofpvplants |