Detection of microcracks in silicon solar cells using Otsu-Canny edge detection algorithm. (December 2022)

• Record Type: Journal Article
• Authors: Gayathri Monicka, S.; Manimegalai, D.; Karthikeyan, M.
• Is Part Of: Renewable energy focus. Volume 43(2022)
• Page Start: 183
• Page End: 190
• Publication Date: 2022-12
• Subjects: Defect detection -- Image edge detection -- Microcracks -- Otsu-canny operator -- Silicon panel; Renewable energy sources -- Periodicals; Solar energy -- Periodicals; 333.79405
• DOI: 10.1016/j.ref.2022.09.002
• Languages: English
• ISSNs: 1755-0084

Abstract

Abstract: The necessity for photovoltaic (PV) arrays has expanded due to the rising demand for solar electrical energy. The demand for solar cells has grown as they are a key component of the PV array. Recent years have seen a rise in solar cell production as a result of this demand. The quality of a silicon panel affects its lifespan and effectiveness in the production of solar power. The picture edge-detection method is regularly employed to identify silicon solar panel flaws. On the other hand, defect identification is impacted by the panel's grid shadow. The most modern defect detection technique uses artificial neural networks, and pre-training the model requires a large number of potential samples from picture data sets. This paper offers the innovative Otsu-Canny operator method as a fault discovery strategy to address this problem. Rapid and accurate identification of microcracks enhances the effectiveness of fault detection. The Otsu algorithm is used in this method to improve edge detection performance for microcracks by modifying the canny operator with a double threshold and eliminating the impact of grids on detection. The process was finished by performing noise removal with morphological operation, picture segmentation, and image binarization. The experimental findings demonstrate a significant improvement in purity and integrity of the image processing algorithm used in this research. The Otsu-Canny edge detection technique is more accurate than other conventional edge detection algorithms for detecting microcracks, with a 92.83 % detection rate.