Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detectionMendeley Data
Solar energy has become the fastest growing renewable and alternative source of energy. However, there is little or no open-source datasets to advance research knowledge in photovoltaic related systems. The work presented in this article is a step towards deriving Photo-Voltaic Module Dataset (PVMD)...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924011466 |
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| author | Rotimi-Williams Bello Pius A. Owolawi Etienne A. van Wyk Chunling Du |
| author_facet | Rotimi-Williams Bello Pius A. Owolawi Etienne A. van Wyk Chunling Du |
| author_sort | Rotimi-Williams Bello |
| collection | DOAJ |
| description | Solar energy has become the fastest growing renewable and alternative source of energy. However, there is little or no open-source datasets to advance research knowledge in photovoltaic related systems. The work presented in this article is a step towards deriving Photo-Voltaic Module Dataset (PVMD) of thermal images and ensuring they are publicly available. The work provides a PVMD dataset comprising a total of 1000 self-acquired and augmented images. The dataset includes both permanent and temporal anomalies, namely Hotspots, Cracks, and Shadings. The dataset was collected on September 5, 2024 at the Soshanguve South Campus, Tshwane University of Technology, South Africa using DJI Mavic 3 Thermal's high-resolution thermal and visual imaging capabilities. DJI Mavic 3 Thermal coupled with its advanced flight features makes it an excellent tool for precise and efficient inspections of PV systems. The laboratory experiment performed on the dataset lasted one week. The work aims to provide supervised dataset good enough to support research method in providing a comprehensive and efficient approach to monitoring and maintaining large PV systems. Extensive analysis of the thermal data reveals the anomalies as indicative of faults in the solar cells of PV module, thereby opening up advancement in solar energy research. Because the data comes from a single-day collection and one week laboratory experiment, it makes the data more suitable for testing algorithms designed for fault detection. The dataset is publicly and freely available to the scientific community at 10.17632/5ssmfpgrpc.1 |
| format | Article |
| id | doaj-art-2f480e2d101f40279f509e91ecbb8164 |
| institution | DOAJ |
| issn | 2352-3409 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-2f480e2d101f40279f509e91ecbb81642025-08-20T02:50:00ZengElsevierData in Brief2352-34092024-12-015711118410.1016/j.dib.2024.111184Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detectionMendeley DataRotimi-Williams Bello0Pius A. Owolawi1Etienne A. van Wyk2Chunling Du3Corresponding author.; Department of Computer Systems Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, South AfricaDepartment of Computer Systems Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, South AfricaDepartment of Computer Systems Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, South AfricaDepartment of Computer Systems Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, South AfricaSolar energy has become the fastest growing renewable and alternative source of energy. However, there is little or no open-source datasets to advance research knowledge in photovoltaic related systems. The work presented in this article is a step towards deriving Photo-Voltaic Module Dataset (PVMD) of thermal images and ensuring they are publicly available. The work provides a PVMD dataset comprising a total of 1000 self-acquired and augmented images. The dataset includes both permanent and temporal anomalies, namely Hotspots, Cracks, and Shadings. The dataset was collected on September 5, 2024 at the Soshanguve South Campus, Tshwane University of Technology, South Africa using DJI Mavic 3 Thermal's high-resolution thermal and visual imaging capabilities. DJI Mavic 3 Thermal coupled with its advanced flight features makes it an excellent tool for precise and efficient inspections of PV systems. The laboratory experiment performed on the dataset lasted one week. The work aims to provide supervised dataset good enough to support research method in providing a comprehensive and efficient approach to monitoring and maintaining large PV systems. Extensive analysis of the thermal data reveals the anomalies as indicative of faults in the solar cells of PV module, thereby opening up advancement in solar energy research. Because the data comes from a single-day collection and one week laboratory experiment, it makes the data more suitable for testing algorithms designed for fault detection. The dataset is publicly and freely available to the scientific community at 10.17632/5ssmfpgrpc.1http://www.sciencedirect.com/science/article/pii/S2352340924011466AnomaliesCracksHotspotsShadingsSolar cells |
| spellingShingle | Rotimi-Williams Bello Pius A. Owolawi Etienne A. van Wyk Chunling Du Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detectionMendeley Data Data in Brief Anomalies Cracks Hotspots Shadings Solar cells |
| title | Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detectionMendeley Data |
| title_full | Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detectionMendeley Data |
| title_fullStr | Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detectionMendeley Data |
| title_full_unstemmed | Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detectionMendeley Data |
| title_short | Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detectionMendeley Data |
| title_sort | photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detectionmendeley data |
| topic | Anomalies Cracks Hotspots Shadings Solar cells |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924011466 |
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