Abstract
To implement the non-contact detection of contamination on insulators, a contamination severity assessment methodology using the deep learning of the colored image information of insulators can be used. For the insulator images taken at the substation site, a mathematical morphology-improved optimal entropic threshold (OET) method is utilized to extract the insulator from the background. By performing feature calculations of insulator images in RGB and HSI color spaces, sixty-six color features are obtained. By fusing the features of the two color spaces using kernel principal component analysis (KPCA), fused features are obtained. The recognition of contamination grades is then accomplished with a deep belief network (DBN) that consists of a three-layered restricted Boltzmann machine. The experimental results of the images taken on-site show that the fused features obtained by the KPCA can fully reflect the contamination state of the insulators. Compared with the identification obtained using RGB or HSI color-space features alone, accuracy is significantly improved, and insulator contamination grades can be effectively identified. The research provides a new method for the accurate, efficient, and non-contact detection of insulator contamination grades.
Highlights
Compared with image recognition algorithms such as convolutional neural network (CNN) and graph convolutional network(GCN), the scheme proposed in this paper uses surface color information to realize contamination grade recognition, avoid the influence of background, edge, structure, and other parameters independent of contamination severity, and realize a method that is more suitable for the research objectives and application scenarios
The fused features obtained from the kernel principal component analysis (KPCA) are utilized for the training of a deep belief network (DBN) to achieve the recognition of contamination grades
The DBN is trained by the fused features from the KPCA to create a recognition model for the insulator contamination grades
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The relationship between partial discharge intensity, contamination grade, environmental humidity, and other factors is complex, and there are still many problems that need to be further studied Compared to these methods, the method of identifying the contamination grade by analyzing a visible-light image is not affected by the ambient temperature and humidity, is cost effective, requires no power outage, and is non-contact. Compared with image recognition algorithms such as convolutional neural network (CNN) and graph convolutional network(GCN), the scheme proposed in this paper uses surface color information to realize contamination grade recognition, avoid the influence of background, edge, structure, and other parameters independent of contamination severity, and realize a method that is more suitable for the research objectives and application scenarios.
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