Author:
Kurumundayil Leslie,Ramspeck Klaus,Rein Stefan,Demant Matthias
Abstract
The future of the energy transition will lead to a terrawatt-scale photovoltaic market, which can be served cost-effectively primarily by means of high-throughput production of solar cells. In addition to high-throughput production, characterization must be adapted to highest cycle times. Therefore, we present an innovative approach to detect image defects in solar cells using on-the-fly electroluminescence measurements. When a solar cell passes a standard current–voltage (I–V) unit, the cell is stopped, contacted, measured, released, and afterwards again accelerated. In contrast to this, contacting and measuring the sample on-the-fly saves a lot of time. Yet, the resulting images are blurred due to high-speed motion. For the development of such an on-the-fly contact measurement tool, a deblurring method is developed in this work. Our deep-learning-based deblurring model enables to present a clean EL image of the solar cell to the human operator and allows for a proper defect detection, reaching a correlation coefficient of 0.84.
Subject
Electrical and Electronic Engineering,Condensed Matter Physics,Renewable Energy, Sustainability and the Environment,Electronic, Optical and Magnetic Materials
Reference26 articles.
1. Kunze P., Greulich J., Rein S., Ramspeck K., Hemsendorf M., Vetter A., Demant M., Efficient deployment of deep neural networks for quality inspection of solar cells using smart labeling, in 37th European PV Solar Energy Conference and Exhibition (2020), Vol. 7, p. 11
2. Alt M., Fischer S., Schenk S., Zimmermann S., Ramspeck K., Meixner M., Electroluminescence imaging and automatic cell classification in mass production of silicon solar cells, in 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC and 34th EU PVSEC) (2018), pp. 3298–3304
3. Learning an Empirical Digital Twin from Measurement Images for a Comprehensive Quality Inspection of Solar Cells
4. Learning Quality Rating of As-Cut mc-Si Wafers via Convolutional Regression Networks
5. Automatic classification of defective photovoltaic module cells in electroluminescence images
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献