scholarly journals Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4292
Author(s):  
Horng-Horng Lin ◽  
Harshad Kumar Dandage ◽  
Keh-Moh Lin ◽  
You-Teh Lin ◽  
Yeou-Jiunn Chen

Solar cells may possess defects during the manufacturing process in photovoltaic (PV) industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing defects are required to be identified. Conventional defect inspection in industries mainly depends on manual defect inspection by highly skilled inspectors, which may still give inconsistent, subjective identification results. In order to automatize the visual defect inspection process, an automatic cell segmentation technique and a convolutional neural network (CNN)-based defect detection system with pseudo-colorization of defects is designed in this paper. High-resolution Electroluminescence (EL) images of single-crystalline silicon (sc-Si) solar PV modules are used in our study for the detection of defects and their quality inspection. Firstly, an automatic cell segmentation methodology is developed to extract cells from an EL image. Secondly, defect detection can be actualized by CNN-based defect detector and can be visualized with pseudo-colors. We used contour tracing to accurately localize the panel region and a probabilistic Hough transform to identify gridlines and busbars on the extracted panel region for cell segmentation. A cell-based defect identification system was developed using state-of-the-art deep learning in CNNs. The detected defects are imposed with pseudo-colors for enhancing defect visualization using K-means clustering. Our automatic cell segmentation methodology can segment cells from an EL image in about 2.71 s. The average segmentation errors along the x-direction and y-direction are only 1.6 pixels and 1.4 pixels, respectively. The defect detection approach on segmented cells achieves 99.8% accuracy. Along with defect detection, the defect regions on a cell are furnished with pseudo-colors to enhance the visualization.

2021 ◽  
pp. 135-241
Author(s):  
Rabindra Satpathy ◽  
Venkateswarlu Pamuru

2020 ◽  
Vol 10 (5) ◽  
pp. 1878 ◽  
Author(s):  
Wenhui Hou ◽  
Dashan Zhang ◽  
Ye Wei ◽  
Jie Guo ◽  
Xiaolong Zhang

The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers.


2014 ◽  
Vol 931-932 ◽  
pp. 1068-1072
Author(s):  
Buntoon Wiengmoon ◽  
Krissanapong Kirtikara ◽  
Chaya Jivacate ◽  
Dhirayut Chenvidhya

This paper presents the investigation on deterioration of PV modules installed in Thailand. The modules under this study were dismantled and collected from KMUTT installation in the Buriram province. Modules were same model of modules that were installed over a thousand of solar water pumping systems and battery charging stations by Green E-san project since end of the 1980's and in earlier of 1990's. There are 35 modules, single crystalline silicon sized 47 Wp, in this study that were exposed in the field with less than 15 years. The experiments consist of visual inspection and measurement of performance at standard test condition (STC 1000 W/m2, 25 °C AM1.5) according to IEC61215:2005 standard. It is found that physical deteriorations and power output degradations. The physical deteriorations in this study can be classified delamination on middle cell, delamination on bus bar, delamination of edge cell and discoloration. For power output degradation, there is no module that was degraded less than 20% of nameplate wattage. There are only 8 of 35 modules (~23%) having power output over 50% of power rating.


2013 ◽  
Vol 58 (2) ◽  
pp. 142-150 ◽  
Author(s):  
A.V. Sachenko ◽  
◽  
V.P. Kostylev ◽  
V.G. Litovchenko ◽  
V.G. Popov ◽  
...  

Author(s):  
Oliver D. Patterson ◽  
Deborah A. Ryan ◽  
Xiaohu Tang ◽  
Shuen Cheng Lei

Abstract In-line E-beam inspection may be used for rapid generation of failure analysis (FA) results for low yielding test structures. This approach provides a number of advantages: 1) It is much earlier than traditional FA, 2) de-processing isn’t required, and 3) a high volume of sites can be processed with the additional support of an in-line FIB. Both physical defect detection and voltage contrast inspection modes are useful for this application. Voltage contrast mode is necessary for isolation of buried defects and is the preferred approach for opens, because it is faster. Physical defect detection mode is generally necessary to locate shorts. The considerations in applying these inspection modes for rapid failure analysis are discussed in the context of two examples: one that lends itself to physical defect inspection and the other, more appropriately addressed with voltage contrast inspection.


2015 ◽  
Vol 8 (1) ◽  
pp. 106-111 ◽  
Author(s):  
Zilong Wang ◽  
Hua Zhang ◽  
Wei Zhao ◽  
Zhigang Zhou ◽  
Mengxun Chen

Research on automatic tracking solar concentrator photovoltaic systems has gained increasing attention in developing the solar PV technology. A paraboloidal concentrator with secondary optic is developed for a three-junction GaInP/GalnAs/Ge solar cell. The concentration ratio of this system is 200 and the photovoltaic cell is cooled by the heat pipe. A detailed analysis on the temperature coefficient influence factors of triple-junction solar cell under different high concentrations (75X, 100X, 125X, 150X, 175X and 200X) has been conducted based on the dish-style concentration photovoltaic system. The results show that under high concentrated light intensity, the temperature coefficient of Voc of triple-junction solar cell is increasing as the concentration ratio increases, from -10.84 mV/°C @ 75X growth to -4.73mV/°C @ 200X. At low concentration, the temperature coefficient of Voc increases rapidly, and then increases slowly as the concentration ratio increases. The temperature dependence of η increased from -0.346%/°C @ 75X growth to - 0.103%/°C @ 200X and the temperature dependence of Pmm and FF increased from -0.125 W/°C, -0.35%/°C @ 75X growth to -0.048W/°C, -0.076%/°C @ 200X respectively. It indicated that the temperature coefficient of three-junction GaInP/GalnAs/Ge solar cell is better than that of crystalline silicon cell array under concentrating light intensity.


Sign in / Sign up

Export Citation Format

Share Document