DETECT PV CELL DEFECTION BASED ON ELECTROLUMINESCENCE LIGHT USING DEEP LEARNING

Abstract

PV cell surface defects inspection is essential to maintain safety, reliability and optimal efficiency of the solar plant. Cell surface defection is the most popular type of fault and it can be hardly detected by normal visual inspection. Defected cells without detection and maintainane can affect the performance of other normal cells since they are connected in series and parallel in large number. In this research, we present a method to automatically detect PV cell surface defection using image processing technique by deep learning model. The input data include 2146 high resolution electroluminescence images of mono and poly PV cells. This type of image is usually captured by drones. The images are then fed into convolutional neural network (CNN) for feature extraction and support vector machine (SVM) for image classification. The results show that the proposed deep learning model can classify the normal and surface defect cells with the accuracy of 91.63%.