Data-Driven Digital Inspection of Photovoltaic Panels Using a Portable Hybrid Model Combining Meteorological Data and Image Processing

IF 2.5 3区工程技术 Q3 ENERGY & FUELS

IEEE Journal of Photovoltaics Pub Date : 2024-09-02 DOI:10.1109/JPHOTOV.2024.3437736

Ayoub Oufadel;Alae Azouzoute;Hicham Ghennioui;Chaimae Soubai;Ibrahim Taabane

{"title":"Data-Driven Digital Inspection of Photovoltaic Panels Using a Portable Hybrid Model Combining Meteorological Data and Image Processing","authors":"Ayoub Oufadel;Alae Azouzoute;Hicham Ghennioui;Chaimae Soubai;Ibrahim Taabane","doi":"10.1109/JPHOTOV.2024.3437736","DOIUrl":null,"url":null,"abstract":"This article proposes a novel approach to photovoltaic panel inspection through the integration of image classification and meteorological data analysis. Utilizing two convolutional neural network models with distinct architectures for classifying thermal and red, green, blue (RGB) images of photovoltaic installations, in addition to an support vector machines model for meteorological data classification, the results from these models are concatenated, allowing the fusion of visual and meteorological information for comprehensive defect detection. Data collection from photovoltaic panels is achieved using a portable device, followed by the application of advanced image processing techniques to identify faults rapidly and accurately with up to 96% accuracy. The inspection results are presented in a user-friendly format, facilitating straightforward interpretation and analysis. This new approach has the potential to significantly enhance the efficiency and durability of solar energy systems, enabling timely maintenance and repair for photovoltaic panel issues.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 6","pages":"937-950"},"PeriodicalIF":2.5000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Photovoltaics","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10663424/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}

引用次数: 0

Abstract

This article proposes a novel approach to photovoltaic panel inspection through the integration of image classification and meteorological data analysis. Utilizing two convolutional neural network models with distinct architectures for classifying thermal and red, green, blue (RGB) images of photovoltaic installations, in addition to an support vector machines model for meteorological data classification, the results from these models are concatenated, allowing the fusion of visual and meteorological information for comprehensive defect detection. Data collection from photovoltaic panels is achieved using a portable device, followed by the application of advanced image processing techniques to identify faults rapidly and accurately with up to 96% accuracy. The inspection results are presented in a user-friendly format, facilitating straightforward interpretation and analysis. This new approach has the potential to significantly enhance the efficiency and durability of solar energy systems, enabling timely maintenance and repair for photovoltaic panel issues.

查看原文本刊更多论文

使用结合气象数据和图像处理的便携式混合模型，对光伏电池板进行数据驱动的数字化检测

本文提出了一种整合图像分类和气象数据分析的光伏面板检测新方法。利用两个具有不同架构的卷积神经网络模型对光伏设备的热图像和红、绿、蓝（RGB）图像进行分类，并利用支持向量机模型对气象数据进行分类。使用便携式设备从光伏电池板采集数据，然后应用先进的图像处理技术快速准确地识别故障，准确率高达 96%。检测结果以用户友好的格式呈现，便于直接解释和分析。这种新方法有可能大大提高太阳能系统的效率和耐用性，及时维护和修理光伏电池板。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Journal of Photovoltaics ENERGY & FUELS-MATERIALS SCIENCE, MULTIDISCIPLINARY

CiteScore

7.00

自引率

10.00%

发文量

206

期刊介绍： The IEEE Journal of Photovoltaics is a peer-reviewed, archival publication reporting original and significant research results that advance the field of photovoltaics (PV). The PV field is diverse in its science base ranging from semiconductor and PV device physics to optics and the materials sciences. The journal publishes articles that connect this science base to PV science and technology. The intent is to publish original research results that are of primary interest to the photovoltaic specialist. The scope of the IEEE J. Photovoltaics incorporates: fundamentals and new concepts of PV conversion, including those based on nanostructured materials, low-dimensional physics, multiple charge generation, up/down converters, thermophotovoltaics, hot-carrier effects, plasmonics, metamorphic materials, luminescent concentrators, and rectennas; Si-based PV, including new cell designs, crystalline and non-crystalline Si, passivation, characterization and Si crystal growth; polycrystalline, amorphous and crystalline thin-film solar cell materials, including PV structures and solar cells based on II-VI, chalcopyrite, Si and other thin film absorbers; III-V PV materials, heterostructures, multijunction devices and concentrator PV; optics for light trapping, reflection control and concentration; organic PV including polymer, hybrid and dye sensitized solar cells; space PV including cell materials and PV devices, defects and reliability, environmental effects and protective materials; PV modeling and characterization methods; and other aspects of PV, including modules, power conditioning, inverters, balance-of-systems components, monitoring, analyses and simulations, and supporting PV module standards and measurements. Tutorial and review papers on these subjects are also published and occasionally special issues are published to treat particular areas in more depth and breadth.