Quantitative analysis of crystalline silicon wafer PV modules by electroluminescence imaging

2017 ◽  
Author(s):  
Siyu Guo ◽  
Eric Schneller ◽  
Kristopher O. Davis ◽  
Winston V. Schoenfeld
Keyword(s):  
Quantitative Analysis ◽  
Silicon Wafer ◽  
Crystalline Silicon ◽  
Pv Modules

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
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Crystalline Silicon ◽  
Cell Segmentation ◽  
Solar Pv ◽  
Single Crystalline Silicon ◽  
Defect Inspection ◽  
Defect Identification ◽  
Pv Modules ◽  
A Cell

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.

Pulsed Green-Laser Annealing for Single-Crystalline Silicon Film Transferred onto Silicon wafer and Non-alkaline Glass by Hydrogen-Induced Exfoliation

2007 ◽  
Vol 46 (1) ◽  
pp. 21-23 ◽  
Author(s):  
Norihito Kawaguchi ◽  
Ryusuke Kawakami ◽  
Ken-ichiro Nishida ◽  
Naoya Yamamoto ◽  
Miyuki Masaki ◽  
...  
Keyword(s):  
Silicon Wafer ◽  
Laser Annealing ◽  
Crystalline Silicon ◽  
Silicon Film ◽  
Green Laser ◽  
Single Crystalline Silicon ◽  
Single Crystalline

Research on surface nano-texturation and wet-chemical passivation of multi-crystalline silicon wafer

2017 ◽  
Vol 28 (24) ◽  
pp. 18825-18834 ◽  
Author(s):  
Yuxin Zou ◽  
Shaoyuan Li ◽  
Wenhui Ma ◽  
Zhao Ding ◽  
Fengshuo Xi ◽  
...  
Keyword(s):  
Silicon Wafer ◽  
Crystalline Silicon ◽  
Chemical Passivation ◽  
Wet Chemical

scholarly journals Feasibility Study of the Technical and Economic Performance of Grid-Connected PV System for Selected Rooftops in UTHM

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Mohamad Fakrie Mohamad Ali ◽  
◽  
Mohd Noor Abdullah ◽  
Keyword(s):  
Feasibility Study ◽  
Crystalline Silicon ◽  
Electricity Consumption ◽  
Payback Period ◽  
Net Energy ◽  
Pv System ◽  
Pv Module ◽  
Electricity Bill ◽  
Pv Modules ◽  
Energy Metering

This paper presents the feasibility study of the technical and economic performances of grid-connected photovoltaic (PV) system for selected rooftops in Universiti Tun Hussein Onn Malaysia (UTHM). The analysis of the electricity consumption and electricity bill data of UTHM campus show that the monthly electricity usage in UTHM campus is very high and expensive. The main purpose of this project is to reduce the annual electricity consumption and electricity bill of UTHM with Net Energy Metering (NEM) scheme. Therefore, the grid-connected PV system has been proposed at Dewan Sultan Ibrahim (DSI), Tunku Tun Aminah Library (TTAL), Fakulti Kejuruteraan Awam dan Alam Bina (FKAAS) and F2 buildings UTHM by using three types of PV modules which are mono-crystalline silicon (Mono-Si), poly-crystalline silicon (Poly-Si) and Thin-film. These three PV modules were modeled, simulated and calculated using Helioscope software with the capacity of 2,166.40kWp, 2,046.20kWp and 1,845kWp respectively for the total rooftop area of 190,302.9 ft². The economic analysis was conducted on the chosen three installed PV modules using RETScreen software. As a result, the Mono-Si showed the best PV module that can produce 2,332,327.40 kWh of PV energy, 4.4% of CO₂ reduction, 9.3 years of payback period considering 21 years of the contractual period and profit of RM4,932,274.58 for 11.7 years after payback period. Moreover, the proposed installation of 2,166.40kWp (Mono-SI PV module) can reduce the annual electricity bill and CO2 emission of 3.6% (RM421,561.93) and 4.4% (1,851.40 tCO₂) compared to the system without PV system.

Hydrogen Platelet Layer in Silicon Formed from Hydrogen Trapped onto Microbubbles of Gases

2002 ◽  
Vol 719 ◽  
Author(s):  
A.Y. Usenko ◽  
W.N. Carr ◽  
Bo Chen
Keyword(s):  
Silicon Wafer ◽  
Atomic Hydrogen ◽  
Microwave Plasma ◽  
Crystalline Silicon ◽  
Silicon Layer ◽  
Internal Surface ◽  
Layer Plane ◽  
Internal Surfaces ◽  
Buried Layer ◽  
Plasma Hydrogenation

AbstractFeatures of a process of delamination of crystalline silicon layer from silicon wafer along hydrogen platelet layer formed by microwave plasma hydrogenation are described. The process involves first making a buried layer of nuclei for hydrogen platelets. Ion implantation of inert or low-soluble gases is used to form the layer. The nuclei are microbubbles that appear along Rp plane of implanted ions. Results for argon, xenon, and krypton implantation are compared. Wafers thus processed with a dose of 1015cm-2 are then hydrogenated with a microwave plasma. During hydrogenation, an atomic hydrogen diffuses into the silicon wafer and collects onto internal surfaces of the microbubbles. Then the hydrogen increases the internal surface of the microbubbles by growing a platelet type extensions to the microbubbles. The extensions grow preferably along the buried layer plane. A silicon layer above the layer of grown platelets were delaminated through pre-bonding/cut/post-bonding sequence as in the Smart-cut process. The plasma hydrogenation of the trap layer may be used as a step in a process of fabricating of SOI wafers with a very thin top crystalline silicon layer. Also, implant doses needed to form the microbubble trap layer are much lower than doses of direct implantation of hydrogen in the Smart-cut process. Temperature range of 200°C to 400°C during the hydrogenation process allows effectively grow extended hydrogen platelets from the nuclei. Mechanisms of nucleation of platelets as extentions of microbubbles are suggested. Control of hydrogen outdiffusion/platelet growth with thermal trajectory during plasma processing is discussed.

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