Defect Detection in c-Si Photovoltaic Modules via Transient Thermography and Deconvolution Optimization  

作　　者：Zekai Shen Hanqi Dai Hongwei Mei Yanxin Tu Liming Wang 

机构地区：[1]State Grid Hangzhou Power Supply Company,State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou 310000,China [2]Huairou Power Supply Branch,State Grid Beijing Electric Power Co.,Ltd.,Beijing 101400,China [3]Tsinghua Shenzhen International Graduate School,Tsinghua University,Shenzhen 518055,China

出　　处：《Chinese Journal of Electrical Engineering》2024年第1期3-11,共9页中国电气工程学报（英文）

基　　金：Supported in part by the National Natural Science Foundation of China under Grant 51977117.

摘　　要：Defects may occur in photovoltaic(PV)modules during production and long-term use,thereby threatening the safe operation of PV power stations.Transient thermography is a promising defect detection technology;however,its detection is limited by transverse thermal diffusion.This phenomenon is particularly noteworthy in the panel glasses of PV modules.A dynamic thermography testing method via transient thermography and Wiener filtering deconvolution optimization is proposed.Based on the time-varying characteristics of the point spread function,the selection rules of the first-order difference image for deconvolution are given.Samples with a broken grid and artificial cracks were tested to validate the performance of the optimization method.Compared with the feature images generated by traditional methods,the proposed method significantly improved the visual quality.Quantitative defect size detection can be realized by combining the deconvolution optimization method with adaptive threshold segmentation.For the same batch of PV products,the detection error could be controlled to within 10%.

关 键 词：Photovoltaic module transient thermography point spread function deconvolution optimization quantitative detection 

分 类 号：O53[理学—等离子体物理]