Automatic detection of micro-crack in solar wafers and cells: a review

Israil, M.; Anwar, Said Amirul; Abdullah, Mohd Zaid

doi:10.1177/0142331212457583

“…In recent years, automatic detection of microcracks has been explored in some literatures [6][7][8][9]. These researches usually detect defects by using gray level and shape differences.…”

Section: Introductionmentioning

confidence: 99%

Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells

Tseng

¹

,

Liu

²

,

Chou

³

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This study provides an automatic method for detecting finger interruptions in electroluminescence (EL) images of multicrystalline solar cells. The proposed method is a supervised classification method. We obtain regions of interest (ROI) by separating the EL image to several regions. The fingers within each ROI are candidates for defect detection. We horizontally scan each ROI region and extract features from each finger pixel. In the training stage, we record a set of features which are extracted from interrupted fingers and noninterrupted fingers. These features are represented as points in a spectral embedding space produced by spectral clustering method. These points will be classified into two clusters: interrupted fingers and noninterrupted fingers. In the classification stage, we firstly detect the position of fingers in an EL image and obtain features from each finger. The set of features in each finger combined with known features in the training stage will be represented as points in the spectral embedding space and then will be classified to the cluster with nearer cluster centroid of known features. Experimental results show that the proposed method can effectively detect finger interruptions on a set of EL images of various solar cells.

show abstract

“…Texture analysis is commonly used as an intermediate process [10]. For performing image classification and interpretation, An image classification process based on texture analysis generally requires feature extraction which can consist of two types of methods: statistical methods and structural methods.…”

Section: Texture Of Imagementioning

confidence: 99%

Statistical Characteristics For Identification Defect of Solar Panel with Naive Bayes

Wiliani¹,

Rahman²,

Sani³

2019

Proceedings of the Proceedings of the 1st Seminar and Workshop on Research Design, for Education, Social Science, Arts, and Hum

1

0

View full text Add to dashboard Cite

Energy that comes from the sun is energy without limits and never runs out. This energy is alternative energy that can be converted into electrical energy, namely by using solar cells. But people who live in remote areas will have difficulty getting electricity. Solar panels are an alternative power source. Solar panels are an alternative way to produce electricity. The production of good solar panels is an important thing that must be done to produce the desired electrical energy. The uncontrolled production process causes various types of defects that appear in solar panels. This study applies the Bayes theorem to classify data by estimating the probability that tuple X is in a class. Using thirty samples consisting of fifteen images of undamaged solar panels and fifteen images. The level of accuracy of image processing for identification of flawed solar panel textures by the Naive Bayesian Classifier method or Simple Bayesian Classifier is around eighty three percent. The results of this study are expected to be used as a reference for the initial detection system of damage that occurs on the surface of the Solar Panel.

show abstract

“…The noncolored surface in the image can contain texture information if the surface has a certainpattern such as used wood surface, stone surface, sand beds, etc. Texture analysis is commonly used as an intermediate process [10]. For performing image classification and interpretation, An image classification process based on texture analysis generally requires feature extraction which can consist of two types of methods: statistical methods and structural methods.…”

Section: Texture Of Imagementioning

confidence: 99%

Statistical lCharacteristics For Identification Defect of Solar Panel with Naive Bayes

Wiliani¹,

Rahman²,

Sani³

et al. 2019

Preprint

0

View full text Add to dashboard Cite

Energy that comes from the sun is energy without limits and never runs out. This energy is alternative energy that can be converted into electrical energy, namely by using solar cells. But people who live in remote areas will have difficulty getting electricity. Solar panels are an alternative power source. Solar panels are an alternative way to produce electricity. The production of good solar panels is an important thing that must be done to produce the desired electrical energy. The uncontrolled production process causes various types of defects that appear in solar panels. This study applies the Bayes theorem to classify data by estimating the probability that tuple X is in a class. Using thirty samples consisting of fifteen images of undamaged solar panels and fifteen images. The level of accuracy of image processing for identification of flawed solar panel textures by the Naive Bayesian Classifier method or Simple Bayesian Classifier is around eighty three percent. The results of this study are expected to be used as a reference for the initial detection system of damage that occurs on the surface of the Solar Panel

show abstract

Automatic detection of micro-crack in solar wafers and cells: a review

Cited by 34 publications

References 53 publications

Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells

Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells

Statistical Characteristics For Identification Defect of Solar Panel with Naive Bayes

Statistical lCharacteristics For Identification Defect of Solar Panel with Naive Bayes

Contact Info

Product

Resources

About