Infrared imaging of photovoltaic modules: a review of the state of the art and future challenges facing gigawatt photovoltaic power stations. (1st October 2022)

• Record Type: Journal Article
• Authors: Buerhop, Claudia; Bommes, Lukas; Schlipf, Jan; Pickel, Tobias; Fladung, Andreas; Peters, Ian Marius
• Is Part Of: Progress in energy. Volume 4:Number 4(2022)
• Publication Date: 2022-10-01
• Subjects: IR-imaging -- high throughput -- relevance -- image acquisition -- processing and assessment; Renewable energy sources -- Periodicals; Power resources -- Periodicals; 333.79
• DOI: 10.1088/2516-1083/ac890b
• Languages: English
• ISSNs: 2516-1083

Abstract

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.