Infrared Computer Vision for Utility-Scale Photovoltaic Array Inspection
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arXiv
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| Format: | Preprint |
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2024
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| author | Ramirez, David F. Pujara, Deep Tepedelenlioglu, Cihan Srinivasan, Devarajan Spanias, Andreas |
| author_facet | Ramirez, David F. Pujara, Deep Tepedelenlioglu, Cihan Srinivasan, Devarajan Spanias, Andreas |
| contents | Utility-scale solar arrays require specialized inspection methods for detecting faulty panels. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues, temperature, environment, age, and other damage can take many forms but often symptomatically exhibit temperature differences. Included is a mini survey to review these common faults and PV array fault detection approaches. Among these, infrared thermography cameras are a powerful tool for improving solar panel inspection in the field. These can be combined with other technologies, including image processing and machine learning. This position paper examines several computer vision algorithms that automate thermal anomaly detection in infrared imagery. We demonstrate our infrared thermography data collection approach, the PV thermal imagery benchmark dataset, and the measured performance of image processing transformations, including the Hough Transform for PV segmentation. The results of this implementation are presented with a discussion of future work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00544 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Infrared Computer Vision for Utility-Scale Photovoltaic Array Inspection Ramirez, David F. Pujara, Deep Tepedelenlioglu, Cihan Srinivasan, Devarajan Spanias, Andreas Image and Video Processing Utility-scale solar arrays require specialized inspection methods for detecting faulty panels. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues, temperature, environment, age, and other damage can take many forms but often symptomatically exhibit temperature differences. Included is a mini survey to review these common faults and PV array fault detection approaches. Among these, infrared thermography cameras are a powerful tool for improving solar panel inspection in the field. These can be combined with other technologies, including image processing and machine learning. This position paper examines several computer vision algorithms that automate thermal anomaly detection in infrared imagery. We demonstrate our infrared thermography data collection approach, the PV thermal imagery benchmark dataset, and the measured performance of image processing transformations, including the Hough Transform for PV segmentation. The results of this implementation are presented with a discussion of future work. |
| title | Infrared Computer Vision for Utility-Scale Photovoltaic Array Inspection |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2407.00544 |