C. Buerhop, Lukas Bommes, Jan Schlipf, Tobias Pickel, Andreas Fladung, I. M. Peters
{"title":"光伏组件的红外成像:回顾吉瓦光伏电站的技术现状和未来面临的挑战","authors":"C. Buerhop, Lukas Bommes, Jan Schlipf, Tobias Pickel, Andreas Fladung, I. M. Peters","doi":"10.1088/2516-1083/ac890b","DOIUrl":null,"url":null,"abstract":"Thermography is a frequently used and appreciated method to detect underperforming Photovoltaic modules in solar power stations. With the review, we give insights on two aspects: (a) are the developed measurement strategies highly efficient (about 1 module s−1) to derive timely answers from the images for operators of multi-Mega Warr peak power stations, and (b) do Photovoltaic stakeholders get answers on the relevance of thermal anomalies for further decisions. Following these questions, the influence of measurement conditions, image and data collection, image evaluation as well as image assessment are discussed. From the literature it is clear that automated image acquisition with manned and unmanned aircrafts allow to capture more than 1 module s−1. This makes it possible to achieve almost identical measurement conditions for the modules; however, it is documented to what extent the increase in speed is achieved at the expense of image resolution. Many image processing tools based on machine learning (ML) have been developed and show the potential for analysis of infrared (IR) images and defect classification. There are different approaches to evaluating IR anomalies in terms of impact on performance, yield or degradation, of individual modules or modules in a string configuration. It is clear that the problem is very complex and multi-layered. On the one hand, information on the electrical interconnection is necessary, and on the other hand, there is a lack of sufficient and suitable data sets to adapt existing computer vision tools to Photovolatics. This is where we see the greatest need for action and further development to increase the expressiveness of IR images for PV stakeholder. We conclude with recommendations to improve the outcome of IR-images and encourage the generation of suitable public data sets of IR-footage for the development of ML tools.","PeriodicalId":410,"journal":{"name":"Progress in Energy and Combustion Science","volume":"78 1","pages":""},"PeriodicalIF":32.0000,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Infrared imaging of photovoltaic modules: a review of the state of the art and future challenges facing gigawatt photovoltaic power stations\",\"authors\":\"C. Buerhop, Lukas Bommes, Jan Schlipf, Tobias Pickel, Andreas Fladung, I. M. Peters\",\"doi\":\"10.1088/2516-1083/ac890b\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Thermography is a frequently used and appreciated method to detect underperforming Photovoltaic modules in solar power stations. 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Many image processing tools based on machine learning (ML) have been developed and show the potential for analysis of infrared (IR) images and defect classification. There are different approaches to evaluating IR anomalies in terms of impact on performance, yield or degradation, of individual modules or modules in a string configuration. It is clear that the problem is very complex and multi-layered. On the one hand, information on the electrical interconnection is necessary, and on the other hand, there is a lack of sufficient and suitable data sets to adapt existing computer vision tools to Photovolatics. This is where we see the greatest need for action and further development to increase the expressiveness of IR images for PV stakeholder. 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Infrared imaging of photovoltaic modules: a review of the state of the art and future challenges facing gigawatt photovoltaic power stations
Thermography is a frequently used and appreciated method to detect underperforming Photovoltaic modules in solar power stations. With the review, we give insights on two aspects: (a) are the developed measurement strategies highly efficient (about 1 module s−1) to derive timely answers from the images for operators of multi-Mega Warr peak power stations, and (b) do Photovoltaic stakeholders get answers on the relevance of thermal anomalies for further decisions. Following these questions, the influence of measurement conditions, image and data collection, image evaluation as well as image assessment are discussed. From the literature it is clear that automated image acquisition with manned and unmanned aircrafts allow to capture more than 1 module s−1. This makes it possible to achieve almost identical measurement conditions for the modules; however, it is documented to what extent the increase in speed is achieved at the expense of image resolution. Many image processing tools based on machine learning (ML) have been developed and show the potential for analysis of infrared (IR) images and defect classification. There are different approaches to evaluating IR anomalies in terms of impact on performance, yield or degradation, of individual modules or modules in a string configuration. It is clear that the problem is very complex and multi-layered. On the one hand, information on the electrical interconnection is necessary, and on the other hand, there is a lack of sufficient and suitable data sets to adapt existing computer vision tools to Photovolatics. This is where we see the greatest need for action and further development to increase the expressiveness of IR images for PV stakeholder. We conclude with recommendations to improve the outcome of IR-images and encourage the generation of suitable public data sets of IR-footage for the development of ML tools.
期刊介绍:
Progress in Energy and Combustion Science (PECS) publishes review articles covering all aspects of energy and combustion science. These articles offer a comprehensive, in-depth overview, evaluation, and discussion of specific topics. Given the importance of climate change and energy conservation, efficient combustion of fossil fuels and the development of sustainable energy systems are emphasized. Environmental protection requires limiting pollutants, including greenhouse gases, emitted from combustion and other energy-intensive systems. Additionally, combustion plays a vital role in process technology and materials science.
PECS features articles authored by internationally recognized experts in combustion, flames, fuel science and technology, and sustainable energy solutions. Each volume includes specially commissioned review articles providing orderly and concise surveys and scientific discussions on various aspects of combustion and energy. While not overly lengthy, these articles allow authors to thoroughly and comprehensively explore their subjects. They serve as valuable resources for researchers seeking knowledge beyond their own fields and for students and engineers in government and industrial research seeking comprehensive reviews and practical solutions.