Solar Panels Defect Detection Based on Radial Basis Function Neural Network

SHEN Ling-yun; ZHU Ming; CHEN Xiao-yun

doi:10.3788/fgxb20153601.0099

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Solar Panels Defect Detection Based on Radial Basis Function Neural Network

更新时间：2020-08-12

- Solar Panels Defect Detection Based on Radial Basis Function Neural Network
- Chinese Journal of Luminescence Vol. 36, Issue 1, Pages: 99-105(2015)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,100049
  3. 长春理工大学电子信息工程学院,吉林长春,中国,130022
- 作者简介：
- 基金信息：
- DOI：10.3788/fgxb20153601.0099
  CLC： TP394.1;TH691.9
- Received：22 August 2014，
  
  Revised：19 October 2014，
  
  Published Online：18 November 2014，
  
  Published：03 January 2015
- 稿件说明：
移动端阅览
沈凌云, 朱明, 陈小云. 基于径向基神经网络的太阳能电池缺陷检测[J]. 发光学报, 2015,36(1): 99-105

SHEN Ling-yun, ZHU Ming, CHEN Xiao-yun. Solar Panels Defect Detection Based on Radial Basis Function Neural Network[J]. Chinese Journal of Luminescence, 2015,36(1): 99-105
沈凌云, 朱明, 陈小云. 基于径向基神经网络的太阳能电池缺陷检测[J]. 发光学报, 2015,36(1): 99-105 DOI： 10.3788/fgxb20153601.0099.

SHEN Ling-yun, ZHU Ming, CHEN Xiao-yun. Solar Panels Defect Detection Based on Radial Basis Function Neural Network[J]. Chinese Journal of Luminescence, 2015,36(1): 99-105 DOI： 10.3788/fgxb20153601.0099.

摘要

为了检测太阳能电池的缺陷

建立了太阳能电池板的电致发光(EL)图像与其缺陷类型间的神经网络预测模型

可以对太阳能电池板不同类型缺陷进行自适应检测。首先

采用主成分分量分析(PCA)算法对电致发光(EL)图像训练样本集降维;然后

将降维后得到的数据输入神经网络预测模型进行学习

对模型的参数进行优化选取;最后

将训练好的网络对测试样本集进行仿真。仿真结果表明:在采用相同的训练样本集和测试样本集条件下

与反向传播神经网络(BPNN)相比

径向基神经网络(RBFNN)具有全局最优特性

结构简单

最高识别率达96.25%

计算时间较短

能满足在线检测的要求。

Abstract

In order to detect the defect on solar panels and improve the conversion efficiency

two neural network models were established between solar panels electroluminescence (EL) images and defect types

which can detect different types of defects on solar panels adaptively. Firstly

the dimensions of EL images training samples set were reduced by using principal component analysis (PCA). Then

EL images training samples set after dimension reduction was put into the neural networks for training. Finally

the testing samples set was simulated by the trained network through choosing the best parameters. Compared with BPNN

RBFNN has the advantages of global optimization characteristics and simple structure

which leads to the highest accuracy rate of 96.25% and shorter computational time. The experiment results show that RBFNN can meet the requirements of online detection.

关键词

Keywords

references

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