Research On Color Sorting Algorithm Of Solar Cell Based On Convolutional Self Encoder

Solar cells play an important role in the whole photovoltaic power generation process,and different cells have different surface colors.When different color cells are assembled together,they will not only affect the appearance,but also reduce the power generation efficiency of photovoltaic modules.Therefore,the study of solar cell color classification is of great significance to improve the utilization efficiency of solar power generation.This paper takes solar cells as the research object and conducts research on the color grading algorithm of solar cells.The research contents include as follows: the preparation process of solar cells,common color grades and their formation reasons.According to the corresponding detection requirements,the collection environment of solar cells was built,such as the selection of camera and lens and lighting design,and the required equipment conditions were determined to ensure that the images of silicon cells collected were in the same stable lighting environment.The pre-processing process of the collected silicon cell image is completed,and most of the influences that may interfere with the color classification of the cell are eliminated through the operations of tilt correction,region segmentation,main gate line removal,image Mosaic and color filling.Then,the designed classification network model based on convolutional autoencoder and VGG16 are used for training and classification of battery images.Finally,the classification network based on convolutional autoencoder is determined as the detection algorithm in this paper.By analyzing the experimental results,the shortcomings are put forward.Finally,according to the omissions of experiments,an improved particle swarm optimization algorithm is used to optimize the proposed algorithm and achieve high-precision color classification of solar cells.The experimental results show that the classification networks composed of convolutional autoencoders has good detection accuracy for solar cell color sorting.The improved particle swarm algorithm is feasible to optimize the parameters of the convolutional network.Compared with the unoptimized network,the detection accuracy of solar cell color sorting is improved by about 4%.In addition,by comparing the detection and classification accuracy of battery cells obtained by image stitching and color filling,it is determined that image stitching is more suitable for cell image processing than color filling.Combined with the image stitching algorithm and the optimized classification network,the detection accuracy of solar cell classification reaches 99.17%,which meets the demand of color classification of solar cell in industrial production.