Abstract

Renewable energy resources such as solar energy, biomass, tidal, geothermal, and hydroelectric energy are becoming increasingly important due to their potential to mitigate the negative impacts of climate change and reduce our dependence on finite and polluting fossil fuels. Solar power can provide a clean, sustainable, and reliable source of renewable energy. Important component of solar power generation is the silicon panel and its surface quality is highly related to its robustness and power generation efficiency. Cell breakages resulting from micro-cracks, degradation and shunted areas on cells are proven to cause major issues and these affect the photovoltaic module efficiency and performance. Solar cell defect identification is important because defects in solar cells significantly reduce their efficiency, which in turn affects their power output and lifespan. By identifying and classifying defects during the production of these cells, engineers and researchers can improve the quality control of solar cells, leading to more reliable and efficient solar energy systems. The proposed method in this research paper, utilizes image processing operations such as adaptive Gaussian thresholding, horizontal and vertical line extraction morphological operations, Canny edge detection, K- Means clustering and VGG16 convolutional neural network to identify the defects in solar cells and classify them as defective or non-defective during the manufacturing process itself. Once the defects are classified, the classification data is exported to Excel file and the results are visually represented as labelled images. OpenCV and Keras modules in Python are used to perform the image processing operations which contributes to cost-effective, reduced computation and high-precision solution.

Authors

S. Kanthalakshmi¹, S. Maalathy², P. Satheesh Kumar³
PSG College of Technology, India^1,2, Nachimuthu Polytechnic College, India³

Keywords

Black Core Fault, Broken Gate Fault, Crack Fault, Shunt Fault, Image Segmentation, Adaptive Gaussian Thresholding, K-Means Clustering, Convolutional Neural Network, VGG16