Ensembling techniques in solar panel quality classification

Trong Hieu Luu, Phan Nguyen Ky Phuc, Tran Lam, Zhi-qiu Yu, Van Tinh Lam


Solar panel quality inspection is a time consuming and costly task. This study tries to develop as reliable method for evaluating the panels quality by using ensemble technique based on three machine learning models namely logistic regression, support vector machine and artificial neural network. The data in this study came from infrared camera which were captured in dark room. The panels are supplied with direct current (DC) power while the infrared camera is located perpendicular with panel surface. Dataset is divided into four classes where each class represent for a level of damage percentage. The approach is suitable for systems which has limited resources as well as number of training images which is very popular in reality. Result shows that the proposed method performs with the accuracy is higher than 90%.


electroluminescence image; ensembling; image processing; machine learning; solar panel quality;

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DOI: http://doi.org/10.11591/ijece.v13i5.pp5674-5680

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).