Fusion colour model for photovoltaic (PV) segmentation
The degradation of photovoltaic (PV) module output is influenced by various factors. Among these factors are elevated temperatures of the PV module, shading of certain cells, the presence of conducting or shortened bypass diodes, and the accumulation of soil and degradation in the PV array. However,...
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2024
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| オンライン･アクセス: | http://umpir.ump.edu.my/id/eprint/38582/1/Fusion%20colour%20model%20for%20photovoltaic%20%28PV%29%20segmentation.pdf http://umpir.ump.edu.my/id/eprint/38582/2/Fusion%20colour%20model%20for%20photovoltaic%20%28PV%29%20segmentation_FULL.pdf http://umpir.ump.edu.my/id/eprint/38582/ https://doi.org/10.1007/978-981-97-3851-9_54 |
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my.ump.umpir.385822025-03-19T06:51:22Z http://umpir.ump.edu.my/id/eprint/38582/ Fusion colour model for photovoltaic (PV) segmentation Azura@Nurul Shuhada, Daud Rohana, Abdul Karim Mohd Shawal, Jadin TK Electrical engineering. Electronics Nuclear engineering The degradation of photovoltaic (PV) module output is influenced by various factors. Among these factors are elevated temperatures of the PV module, shading of certain cells, the presence of conducting or shortened bypass diodes, and the accumulation of soil and degradation in the PV array. However, ensuring that PV installations remain a profitable investment relies significantly on conducting effective as well as regular inspections to identify any existing defects. In this context, unmanned aerial vehicles (UAVs) have gained increasing popularity as a means of inspection in a variety of fields, including Large Scale Solar (LSS) installations. These days, hotspot detection is frequently accomplished using infrared thermography (IRT) technology. In large-scale PV facilities, the deployment of UAVs can significantly increase labour efficiency when compared to manual inspection. For the purpose of detecting hotspots, PV module IRT image processing is crucial. The hotspot location cannot be identified without segmenting the PV modules. In this study, we presented a technique for acquiring segmentation by integrating mask images with IRT images. Computer vision and image processing utilizing MATLAB are employed. Thirty PV module experimental results are presented in this research. There are five PV modules with a poor segment out of thirty total PV modules. The color, as well as temperature with respect to the IR image, cannot be simply segmented. The hotspot cell could develop as a result of the PV receiving reflections from the sun. In order to evaluate our quality process, quantitative analysis is employed. The approach works effectively in segmentation as seen by the output mask's average quality of 83.3%. Springer 2024 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38582/1/Fusion%20colour%20model%20for%20photovoltaic%20%28PV%29%20segmentation.pdf pdf en http://umpir.ump.edu.my/id/eprint/38582/2/Fusion%20colour%20model%20for%20photovoltaic%20%28PV%29%20segmentation_FULL.pdf Azura@Nurul Shuhada, Daud and Rohana, Abdul Karim and Mohd Shawal, Jadin (2024) Fusion colour model for photovoltaic (PV) segmentation. In: Proceedings of the 7th International Conference on Electrical, Control and Computer Engineering - Volume 2. . InECCE 2023. Lecture Notes in Electrical Engineering. The 7th International Conference on Electrical, Control and Computer Engineering (InECCE2023) , 22 August 2023 , Royale Chulan Damansara, Petaling Jaya. pp. 635-647., 1213. ISBN 978-981-97-3850-2 (Published) https://doi.org/10.1007/978-981-97-3851-9_54 |
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TK Electrical engineering. Electronics Nuclear engineering Azura@Nurul Shuhada, Daud Rohana, Abdul Karim Mohd Shawal, Jadin Fusion colour model for photovoltaic (PV) segmentation |
| description |
The degradation of photovoltaic (PV) module output is influenced by various factors. Among these factors are elevated temperatures of the PV module, shading of certain cells, the presence of conducting or shortened bypass diodes, and the accumulation of soil and degradation in the PV array. However, ensuring that PV installations remain a profitable investment relies significantly on conducting effective as well as regular inspections to identify any existing defects. In this context, unmanned aerial vehicles (UAVs) have gained increasing popularity as a means of inspection in a variety of fields, including Large Scale Solar (LSS) installations. These days, hotspot detection is frequently accomplished using infrared thermography (IRT) technology. In large-scale PV facilities, the deployment of UAVs can significantly increase labour efficiency when compared to manual inspection. For the purpose of detecting hotspots, PV module IRT image processing is crucial. The hotspot location cannot be identified without segmenting the PV modules. In this study, we presented a technique for acquiring segmentation by integrating mask images with IRT images. Computer vision and image processing utilizing MATLAB are employed. Thirty PV module experimental results are presented in this research. There are five PV modules with a poor segment out of thirty total PV modules. The color, as well as temperature with respect to the IR image, cannot be simply segmented. The hotspot cell could develop as a result of the PV receiving reflections from the sun. In order to evaluate our quality process, quantitative analysis is employed. The approach works effectively in segmentation as seen by the output mask's average quality of 83.3%. |
| format |
Conference or Workshop Item |
| author |
Azura@Nurul Shuhada, Daud Rohana, Abdul Karim Mohd Shawal, Jadin |
| author_facet |
Azura@Nurul Shuhada, Daud Rohana, Abdul Karim Mohd Shawal, Jadin |
| author_sort |
Azura@Nurul Shuhada, Daud |
| title |
Fusion colour model for photovoltaic (PV) segmentation |
| title_short |
Fusion colour model for photovoltaic (PV) segmentation |
| title_full |
Fusion colour model for photovoltaic (PV) segmentation |
| title_fullStr |
Fusion colour model for photovoltaic (PV) segmentation |
| title_full_unstemmed |
Fusion colour model for photovoltaic (PV) segmentation |
| title_sort |
fusion colour model for photovoltaic (pv) segmentation |
| publisher |
Springer |
| publishDate |
2024 |
| url |
http://umpir.ump.edu.my/id/eprint/38582/1/Fusion%20colour%20model%20for%20photovoltaic%20%28PV%29%20segmentation.pdf http://umpir.ump.edu.my/id/eprint/38582/2/Fusion%20colour%20model%20for%20photovoltaic%20%28PV%29%20segmentation_FULL.pdf http://umpir.ump.edu.my/id/eprint/38582/ https://doi.org/10.1007/978-981-97-3851-9_54 |
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1827518356974993408 |
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13.251813 |