Intelligent Classification of Silicon Photovoltaic Cell Defects Based on Eddy Current Thermography and Convolution Neural Network

Du Bolun; He Yigang; He Yunze; Duan Jiajun; Zhang Yaru

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Intelligent Classification of Silicon Photovoltaic Cell Defects Based on Eddy Current Thermography and Convolution Neural Network

机译：基于涡流热成像和卷积神经网络的硅光伏电池缺陷智能分类

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In this article, defects in the production process of silicon photovoltaic (Si-PV) cells are urgently needed to be detected due to their serious impact on the normal generation of PV system. In view of the shortcomings, such as low-defect efficiency, few detection data, and high detection error rate in the existing industrial production line, the main research purpose of this article is to complete an intelligent classification method for efficient and innovative defect detection for Si-PV cells and modules. The purpose is to improve the detection efficiency of Si-PV cell, to ensure the safety and reliability of Si-PV cell production process, to achieve large number of Si-PV cell defects detection and classification. First, the eddy current thermography system of Si-PV cells is established. Second, principal component analysis, independent component analysis, and nonnegative matrix factorization algorithms are compared for thermography sequences processing. Third, LeNet-5, VGG-16, and GoogleNet models are compared for Si-PV cell defects classification. Finally, the results show that the proposed method have successful application in Si-PV cell defects detection and classification.

机译：在本文中，迫切需要检测到硅光伏（Si-PV）细胞的生产过程中的缺陷，因为它们对PV系统的正常产生的严重影响而被检测到。鉴于缺陷效率，少数检测数据和现有工业生产线的高检测误差率，本文的主要研究目的是为有效和创新的缺陷检测完成智能分类方法Si-PV电池和模块。目的是提高Si-PV细胞的检测效率，以确保Si-PV电池生产过程的安全性和可靠性，实现大量的Si-PV电池缺陷检测和分类。首先，建立Si-PV电池的涡流热成像系统。其次，将主成分分析，独立分析分析和非负矩阵分解算法进行了比较，用于热成像序列处理。第三，LENET-5，VGG-16和Googlenet模型与Si-PV电池缺陷分类进行比较。最后，结果表明，该方法在Si-PV细胞缺陷检测和分类中具有成功应用。

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来源
《IEEE transactions on industrial informatics》 |2020年第10期|6242-6251|共10页
作者
Du Bolun; He Yigang; He Yunze; Duan Jiajun; Zhang Yaru;
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作者单位

Wuhan Univ Coll Elect Engn & Automat Wuhan 430072 Peoples R China;

Wuhan Univ Coll Elect Engn & Automat Wuhan 430072 Peoples R China;

Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China;

Wuhan Univ Coll Elect Engn & Automat Wuhan 430072 Peoples R China;

Wuhan Univ Coll Elect Engn & Automat Wuhan 430072 Peoples R China;

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正文语种 eng
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Convolution neural network (CNN); defect classification; defect feature extraction; eddy current thermography (ECT); nondestructive testing and evaluation; silicon photovoltaic (Si-PV) cell;

机译：卷积神经网络（CNN）;缺陷分类;缺陷特征提取;涡流热成像（ECT）;非破坏性测试和评估;硅光伏（Si-PV）细胞;

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1. Photovoltaic cell defect classification using convolutional neural network and support vector machine [J] . Ashfaq Ahmad, Yi Jin, Changan Zhu, Renewable Power Generation, IET . 2020,第14期

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2. A Hybrid Fuzzy Convolutional Neural Network Based Mechanism for Photovoltaic Cell Defect Detection With Electroluminescence Images [J] . Ge Chunpeng, Liu Zhe, Fang Liming, IEEE Transactions on Parallel and Distributed Systems . 2021,第7期

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3. Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting [J] . Wang Fei, Zhang Zhanyao, Liu Chun, Energy Conversion & Management . 2019,第FEBa期

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Intelligent Classification of Silicon Photovoltaic Cell Defects Based on Eddy Current Thermography and Convolution Neural Network

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