A Survey of Photovoltaic Panel Overlay and Fault Detection Methods
Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize e...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/4/837 |
| _version_ | 1797298324245577728 |
|---|---|
| author | Cheng Yang Fuhao Sun Yujie Zou Zhipeng Lv Liang Xue Chao Jiang Shuangyu Liu Bochao Zhao Haoyang Cui |
| author_facet | Cheng Yang Fuhao Sun Yujie Zou Zhipeng Lv Liang Xue Chao Jiang Shuangyu Liu Bochao Zhao Haoyang Cui |
| author_sort | Cheng Yang |
| collection | DOAJ |
| description | Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages. |
| first_indexed | 2024-03-07T22:34:14Z |
| format | Article |
| id | doaj.art-174939f42c1e49a3854bbeef6ca6f140 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-07T22:34:14Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-174939f42c1e49a3854bbeef6ca6f1402024-02-23T15:15:13ZengMDPI AGEnergies1996-10732024-02-0117483710.3390/en17040837A Survey of Photovoltaic Panel Overlay and Fault Detection MethodsCheng Yang0Fuhao Sun1Yujie Zou2Zhipeng Lv3Liang Xue4Chao Jiang5Shuangyu Liu6Bochao Zhao7Haoyang Cui8College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaShanghai Zhabei Power Plant of State Grid Corporation of China, Shanghai 200432, ChinaEnergy Internet Research Institute Co., Ltd., State Grid Corporation of China, Shanghai 200437, ChinaCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaShanghai Guoyun Information Technology Co., Ltd., Shanghai 201210, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaPhotovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.https://www.mdpi.com/1996-1073/17/4/837PV paneloverlay detectionfault detection |
| spellingShingle | Cheng Yang Fuhao Sun Yujie Zou Zhipeng Lv Liang Xue Chao Jiang Shuangyu Liu Bochao Zhao Haoyang Cui A Survey of Photovoltaic Panel Overlay and Fault Detection Methods Energies PV panel overlay detection fault detection |
| title | A Survey of Photovoltaic Panel Overlay and Fault Detection Methods |
| title_full | A Survey of Photovoltaic Panel Overlay and Fault Detection Methods |
| title_fullStr | A Survey of Photovoltaic Panel Overlay and Fault Detection Methods |
| title_full_unstemmed | A Survey of Photovoltaic Panel Overlay and Fault Detection Methods |
| title_short | A Survey of Photovoltaic Panel Overlay and Fault Detection Methods |
| title_sort | survey of photovoltaic panel overlay and fault detection methods |
| topic | PV panel overlay detection fault detection |
| url | https://www.mdpi.com/1996-1073/17/4/837 |
| work_keys_str_mv | AT chengyang asurveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT fuhaosun asurveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT yujiezou asurveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT zhipenglv asurveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT liangxue asurveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT chaojiang asurveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT shuangyuliu asurveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT bochaozhao asurveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT haoyangcui asurveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT chengyang surveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT fuhaosun surveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT yujiezou surveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT zhipenglv surveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT liangxue surveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT chaojiang surveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT shuangyuliu surveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT bochaozhao surveyofphotovoltaicpaneloverlayandfaultdetectionmethods AT haoyangcui surveyofphotovoltaicpaneloverlayandfaultdetectionmethods |