Fusion of Thermal and Visible Acquisitions for Evaluating Production-borne Scratches and Shunts in Photo-Voltaic PV Cells

This manuscript discusses the development of a fusion-based method of visible and thermal acquisitions (Long Wave and Mid-Wave Infrared LWIR, MWIR) to evaluate scratches and shunts in poly-crystalline, Photo-Voltaic PV solar cells. The proposed technique enables non-destructive and real-time inspection of PV cells production-borne defects; using forward bias and reverse bias Electro-Luminescence EL modes. The thermal imagery of the cells is designed to expose the extent of scratches in both the LWIR and MWIR spectra and whether it acts as an emitter layer bringing the p-n junction to the cell surface; while the visible images (from a CCD detector) evaluate the scratch effect on the surface coating layer and hence its reflectivity. On the other hand, the shunts are detected as hot spots and classified as Ohmic and Non-Ohmic using the reverse and forward bias images of the MWIR imager. The acquired images coming from the three channels (LWIR, MWIR, Visible) are combined using two fusion algorithms suited for real-time applications (>30 Hz), specifically; Wavelet based fusion, and Principle Component Analysis PCA based fusion. The fused results are further judged based on several reported criteria mainly; cross entropy, mutual information, and the Signal to Noise Ratio SNR, highlighting the need for a unified fusion evaluation approach. The fusion of visible and thermal inputs provides accurate assessment of the PV cells scratches and shunts in contact-less and real-time fashion. The proposed fused imagery approach combines the information from visual inspection and thermal testing using spatially resolved scanning of the PV cell in real-time.

[1]  Xavier Maldague,et al.  Micro-cracks detection in Photo-Voltaic Cells by Infrared Thermography , 2004 .

[2]  Yi Zhou,et al.  Pixel-Level Fusion for Infrared and Visible Acquisitions , 2009 .

[3]  Pawan Sinha,et al.  Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About , 2006, Proceedings of the IEEE.

[4]  Jim Colvin.  Comparative Failure Analysis of Photovoltaic Devices , 2009 .

[5]  Wilhelm Warta,et al.  Diffusion lengths of silicon solar cells from luminescence images , 2007 .

[6]  Yi Zhou,et al.  Principal Component Analysis-Based Image Fusion Routine with Application to Automotive Stamping Split Detection , 2011 .

[7]  S. Vergura,et al.  Typical defects of PV-cells , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[8]  Nik Rajic,et al.  Principal component thermography for flaw contrast enhancement and flaw depth characterisation in composite structures , 2002 .

[9]  Karsten Bothe,et al.  Advances in Contactless Silicon Defect and Impurity Diagnostics Based on Lifetime Spectroscopy and Infrared Imaging , 2007 .

[10]  M. Werner,et al.  Shunt types in crystalline silicon solar cells , 2004 .

[11]  M. Schubert,et al.  Photoluminescence imaging of silicon wafers , 2006 .

[12]  Gang Hong,et al.  High resolution image fusion based on wavelet and IHS transformations , 2003 .

[13]  Ieee Geoscience,et al.  2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, URBAN 2003 : Berlin, 22-23 May 2003, Technical University of Berlin. , 2003 .

[14]  Wilhelm Warta,et al.  Luminescence imaging for the detection of shunts on silicon solar cells , 2008 .

[15]  Cedric Nishan Canagarajah,et al.  Image fusion using a new framework for complex wavelet transforms , 2005, IEEE International Conference on Image Processing 2005.

[16]  Takashi Sekiguchi,et al.  Evaluation of defects generation in crystalline silicon ingot grown by cast technique with seed crystal for solar cells. , 2012, Journal of applied physics.

[17]  B. S. Manjunath,et al.  Multi-sensor image fusion using the wavelet transform , 1994, Proceedings of 1st International Conference on Image Processing.

[18]  O. Breitenstein,et al.  Lock-in Thermography: A Versatile Tool for Failure Analysis of Solar Cells , 2009, EDFA Technical Articles.

[19]  Yi Zhou,et al.  Feature-level and pixel-level fusion routines when coupled to infrared night-vision tracking scheme , 2010 .

[20]  Gonzalo Pajares Martinsanz,et al.  A wavelet-based image fusion tutorial , 2004 .

[21]  N. Rajic.  Principal Component Thermography , 2002 .

[22]  B. Erik Ydstie,et al.  Silicon solar cell production , 2011, Comput. Chem. Eng..

[23]  Joel Lanir,et al.  Comparing Multispectral Image Fusion Methods for a Target Detection Task , 2006, 2006 9th International Conference on Information Fusion.

[24]  Wilhelm Warta,et al.  PROGRESS WITH LUMINESCENCE IMAGING FOR THE CHARACTERISATION OF SILICON WAFERS AND SOLAR CELLS , 2007 .

[25]  T. Fuyuki,et al.  Photographic surveying of minority carrier diffusion length in polycrystalline silicon solar cells by electroluminescence , 2005 .

[26]  T. Saga.  Advances in crystalline silicon solar cell technology for industrial mass production , 2010 .

[27]  R. Parvataneni.  PRINCIPAL COMPONENT THERMOGRAPHY FOR STEADY THERMAL PERTURBATION SCENARIOS , 2009 .