Solar cell panel crack detection using Particle Swarm Optimization algorithm

A solar cell panel as an efficient power source for the production of electrical energy has long been considered. Any defect on the solar cell panel's surface will be lead to reduced production of power and loss in the yield. In this case, inspection of the solar cell panel is essential to be performed to obtain a product of high quality. Some inspection methods have been developed, but in any event non-contact, non-destructive and efficient testing methods are necessary. This paper proposes an automated inspection system based on an image-processing approach for solar cell panel application in order to detect any cracks which may be appeared on the surface of solar cell panel. The Particle Swarm Optimization (PSO) algorithm as a main constituent of our proposed method is used for edge detection in the solar cell panel. Subsequently, some features like cracks and bus bars will be extracted and we will classify defected products and cracks based on the positions of the bus bars using Fuzzy logic. In this proposed method, an automated inspection system of solar cell panel proposed which has potential to get good results based on Particle Swarm optimization algorithm.

[1]  Du-Ming Tsai,et al.  Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion , 2010, Image Vis. Comput..

[2]  T. Razykov,et al.  Solar photovoltaic electricity: Current status and future prospects , 2011 .

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

[4]  Y. Nosaka,et al.  Solar Cells and Photocatalysts , 2011 .

[5]  Liu Yang,et al.  Solar cell crack inspection by image processing , 2004, Proceedings of 2004 International Conference on the Business of Electronic Product Reliability and Liability (IEEE Cat. No.04EX809).

[6]  S. Ostapenko,et al.  Yield enhancement for solar cell manufacturing using resonance Ultrasonic vibrations inspection , 2009, 2009 34th IEEE Photovoltaic Specialists Conference (PVSC).

[7]  Mengjie Zhang,et al.  A new homogeneity-based approach to edge detection using PSO , 2009, 2009 24th International Conference Image and Vision Computing New Zealand.

[8]  M. Köntges,et al.  The risk of power loss in crystalline silicon based photovoltaic modules due to micro-cracks , 2011 .

[9]  Armin G. Aberle,et al.  Observations on the spectral characteristics of defect luminescence of silicon wafer solar cells , 2010, 2010 35th IEEE Photovoltaic Specialists Conference.

[10]  S. C. Solanki,et al.  Photovoltaic modules and their applications: A review on thermal modelling , 2011 .

[11]  S. Ostapenko,et al.  Resonance Ultrasonic Vibrations for in-line crack detection in silicon wafers and solar cells , 2008, 2008 33rd IEEE Photovoltaic Specialists Conference.

[12]  Ewan D. Dunlop,et al.  Radiometric Pulse and Thermal Imaging Methods for the Detection of Physical Defects in solar Cells and Si Wafers in a Production Environment. , 2004 .

[13]  Qingli Li,et al.  Detection of physical defects in solar cells by hyperspectral imaging technology , 2010 .

[14]  D. P. Hess,et al.  Audible vibration diagnostics of thermo-elastic residual stress in multi-crystalline silicon wafers , 2006 .

[15]  Li Wang,et al.  Automatic Detection of Defects in Solar Modules: Image Processing in Detecting , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[16]  Mark Johnston,et al.  Edge and Corner Extraction Using Particle Swarm Optimisation , 2010, Australasian Conference on Artificial Intelligence.

[17]  Yu-Teng Liang,et al.  Micro crack detection of multi‐crystalline silicon solar wafer using machine vision techniques , 2011 .

[18]  D. P. Hess,et al.  Crack detection in single-crystalline silicon wafers using impact testing , 2008 .

[19]  Patrick M. Pilarski,et al.  A SWARM-BASED SYSTEM FOR OBJECT RECOGNITION , 2005 .