Abstract
AbstractSolar cells represent one of the most important sources of clean energy in modern societies. Solar cell manufacturing is a delicate process that often introduces defects that reduce cell efficiency or compromise durability. Current inspection systems detect and discard faulty cells, wasting a significant percentage of resources. We introduce Cell Doctor, a new inspection system that uses state of the art techniques to locate and classify defects in solar cells and performs a diagnostic and treatment process to isolate or eliminate the defects. Cell Doctor uses a fully automatic process that can be included in a manufacturing line. Incoming solar cells are first moved with a robotic arm to an Electroluminescence diagnostic station, where they are imaged and analysed with a set of Gabor filters, a Principal Component Analysis technique, a Random Forest classifier and different image processing techniques to detect possible defects in the surface of the cell. After the diagnosis, a laser station performs an isolation or cutting process depending on the detected defects. In a final stage, the solar cells are characterised in terms of their I–V Curve and I–V Parameters, in a Solar Simulator station. We validated and tested Cell Doctor with a labelled dataset of images of monocrystalline silicon cells, obtaining an accuracy and recall above 90% for Cracks, Area Defects and Finger interruptions; and precision values of 77% for Finger Interruptions and above 90% for Cracks and Area Defects. Which allows Cell Doctor to diagnose and repair solar cells in an industrial environment in a fully automatic way.
Funder
H2020 LEIT Advanced Manufacturing and Processing
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Reference22 articles.
1. Anwar, S. A., & Abdullah, M. Z. (2014). Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique. EURASIP Journal on Image and Video Processing, 2014(1), 15.
2. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
3. Breitenstein, O., Rakotoniaina, J. P., Al Rifai, M. H., & Werner, M. (2004). Shunt types in crystalline silicon solar cells. Progress in Photovoltaics: Research and Applications, 12(7), 529–538.
4. Chen, H., Pang, Y., Hu, Q., & Liu, K. (2018). Solar cell surface defect inspection based on multispectral convolutional neural network. Journal of Intelligent Manufacturing, 23, 1–16.
5. Chiou, Y.-C., Liu, J.-Z., & Liang, Y.-T. (2011). Micro crack detection of multi-crystalline silicon solar wafer using machine vision techniques. Sensor Review, 31(2), 154–165.
Cited by
11 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