• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

通过电致发光检测计算光伏组件受损表面及其与功率损耗的关系以进行纠正性维护的方法

Methodology for Calculating the Damaged Surface and Its Relationship with Power Loss in Photovoltaic Modules by Electroluminescence Inspection for Corrective Maintenance.

作者信息

Saborido-Barba Nieves, García-López Carmen, Clavijo-Blanco José Antonio, Jiménez-Castañeda Rafael, Álvarez-Tey Germán

机构信息

Departamento de Ingeniería Eléctrica, Universidad de Cádiz, Avenida de la Universidad de Cádiz 10, 11519 Puerto Real, Cádiz, Spain.

出版信息

Sensors (Basel). 2024 Feb 24;24(5):1479. doi: 10.3390/s24051479.

DOI:10.3390/s24051479
PMID:38475019
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10933771/
Abstract

Photovoltaic panels are exposed to various external factors that can cause damage, with the formation of cracks in the photovoltaic cells being one of the most recurrent issues affecting their production capacity. Electroluminescence (EL) tests are employed to detect these cracks. In this study, a methodology developed according to the IEC TS 60904-13 standard is presented, allowing for the calculation of the percentage of type C cracks in a PV panel and subsequently estimating the associated power loss. To validate the methodology, it was applied to a polycrystalline silicon module subjected to incremental damage through multiple impacts on its rear surface. After each impact, electroluminescence images and I-V curves were obtained and used to verify power loss estimates. More accurate estimates were achieved by assessing cracks at the PV cell level rather than by substring or considering the entire module. In this context, cell-level analysis becomes indispensable, as the most damaged cell significantly influences the performance of the photovoltaic model. Subsequently, the developed methodology was applied to evaluate the conditions of four photovoltaic panels that had been in operation, exemplifying its application in maintenance tasks. The results assisted in decision making regarding whether to replace or continue using the panels.

摘要

光伏板会受到各种可能导致损坏的外部因素影响,光伏电池中出现裂缝是影响其生产能力的最常见问题之一。电致发光(EL)测试用于检测这些裂缝。在本研究中,提出了一种根据IEC TS 60904-13标准开发的方法,该方法可计算光伏板中C类裂缝的百分比,并随后估算相关的功率损失。为验证该方法,将其应用于一个多晶硅模块,该模块通过对其背面进行多次撞击而遭受渐进式损坏。每次撞击后,获取电致发光图像和I-V曲线,并用于验证功率损失估计。通过在光伏电池层面评估裂缝,而不是通过子串或考虑整个模块,可获得更准确的估计。在这种情况下,电池层面的分析变得不可或缺,因为受损最严重的电池会显著影响光伏模型的性能。随后,将所开发的方法应用于评估四块已投入运行的光伏板的状况,例证了其在维护任务中的应用。结果有助于就是否更换或继续使用这些光伏板做出决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/344f716375d5/sensors-24-01479-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/ee4b68276bc9/sensors-24-01479-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/36279469b852/sensors-24-01479-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/5bb6e1b3e02a/sensors-24-01479-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/637f25e4bd3d/sensors-24-01479-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/82eea370ea6a/sensors-24-01479-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/ea92ed848dee/sensors-24-01479-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/cf75c111220b/sensors-24-01479-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/4e800c178cfa/sensors-24-01479-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/9e51fac5fc93/sensors-24-01479-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/2e76ec9d8ce8/sensors-24-01479-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/abb9fc9e87d1/sensors-24-01479-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/4a5555dba871/sensors-24-01479-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/3afec5e05009/sensors-24-01479-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/0e395adfd826/sensors-24-01479-g014a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/0818afaf2ded/sensors-24-01479-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/344f716375d5/sensors-24-01479-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/ee4b68276bc9/sensors-24-01479-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/36279469b852/sensors-24-01479-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/5bb6e1b3e02a/sensors-24-01479-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/637f25e4bd3d/sensors-24-01479-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/82eea370ea6a/sensors-24-01479-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/ea92ed848dee/sensors-24-01479-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/cf75c111220b/sensors-24-01479-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/4e800c178cfa/sensors-24-01479-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/9e51fac5fc93/sensors-24-01479-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/2e76ec9d8ce8/sensors-24-01479-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/abb9fc9e87d1/sensors-24-01479-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/4a5555dba871/sensors-24-01479-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/3afec5e05009/sensors-24-01479-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/0e395adfd826/sensors-24-01479-g014a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/0818afaf2ded/sensors-24-01479-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffb/10933771/344f716375d5/sensors-24-01479-g016.jpg

相似文献

1
Methodology for Calculating the Damaged Surface and Its Relationship with Power Loss in Photovoltaic Modules by Electroluminescence Inspection for Corrective Maintenance.通过电致发光检测计算光伏组件受损表面及其与功率损耗的关系以进行纠正性维护的方法
Sensors (Basel). 2024 Feb 24;24(5):1479. doi: 10.3390/s24051479.
2
Spectrum Analysis Enabled Periodic Feature Reconstruction Based Automatic Defect Detection System for Electroluminescence Images of Photovoltaic Modules.基于频谱分析的周期性特征重构光伏组件电致发光图像自动缺陷检测系统
Micromachines (Basel). 2022 Feb 19;13(2):332. doi: 10.3390/mi13020332.
3
Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization.采用深度学习的伪彩色化对单晶硅光伏组件的电致发光图像进行高效的细胞分割和基于电池的缺陷识别。
Sensors (Basel). 2021 Jun 23;21(13):4292. doi: 10.3390/s21134292.
4
Examining the interplay of dust and defects: A comprehensive experimental analysis on the performance of photovoltaic modules.探究灰尘与缺陷的相互作用:对光伏组件性能的全面实验分析
Heliyon. 2024 Aug 23;10(17):e36796. doi: 10.1016/j.heliyon.2024.e36796. eCollection 2024 Sep 15.
5
Electroluminescence image analysis of a photovoltaic module under accelerated lifecycle testing.光伏组件在加速寿命周期测试下的电致发光图像分析
Appl Opt. 2020 Aug 1;59(22):G225-G233. doi: 10.1364/AO.391957.
6
Mechanical integrity of photovoltaic panels under hailstorms: Mono vs. poly-crystalline comparison.冰雹天气下光伏板的机械完整性:单晶与多晶比较
Heliyon. 2024 Feb 7;10(4):e25865. doi: 10.1016/j.heliyon.2024.e25865. eCollection 2024 Feb 29.
7
Comprehensive review of environmental factors influencing the performance of photovoltaic panels: Concern over emissions at various phases throughout the lifecycle.全面综述影响光伏电池板性能的环境因素:关注生命周期各阶段的排放问题。
Environ Pollut. 2023 Jun 1;326:121474. doi: 10.1016/j.envpol.2023.121474. Epub 2023 Mar 23.
8
Reliability Study of c-Si PV Module Mounted on a Concrete Slab by Thermal Cycling Using Electroluminescence Scanning: Application in Future Solar Roadways.通过电致发光扫描热循环对安装在混凝土板上的晶体硅光伏组件进行可靠性研究:在未来太阳能道路中的应用
Materials (Basel). 2020 Jan 19;13(2):470. doi: 10.3390/ma13020470.
9
Passive Electroluminescence and Photoluminescence Imaging Acquisition of Photovoltaic Modules.光伏组件的被动电致发光和光致发光成像采集
Sensors (Basel). 2024 Feb 28;24(5):1539. doi: 10.3390/s24051539.
10
Fatigue degradation and electric recovery in Silicon solar cells embedded in photovoltaic modules.嵌入光伏组件中的硅太阳能电池的疲劳退化与电恢复
Sci Rep. 2014 Mar 28;4:4506. doi: 10.1038/srep04506.

本文引用的文献

1
A Machine-Learning-Based Robust Classification Method for PV Panel Faults.基于机器学习的光伏板故障稳健分类方法。
Sensors (Basel). 2022 Nov 4;22(21):8515. doi: 10.3390/s22218515.
2
Spectrum Analysis Enabled Periodic Feature Reconstruction Based Automatic Defect Detection System for Electroluminescence Images of Photovoltaic Modules.基于频谱分析的周期性特征重构光伏组件电致发光图像自动缺陷检测系统
Micromachines (Basel). 2022 Feb 19;13(2):332. doi: 10.3390/mi13020332.
3
Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization.
采用深度学习的伪彩色化对单晶硅光伏组件的电致发光图像进行高效的细胞分割和基于电池的缺陷识别。
Sensors (Basel). 2021 Jun 23;21(13):4292. doi: 10.3390/s21134292.