Intelligent Recognition of Insulator Contamination Grade Based on the Deep Learning of Ultraviolet Discharge Image Information

Abstract

In order to achieve the noncontact detection of the contamination grade of insulators and to provide guidance for preventing the contamination flashover of insulators based on the pollution state, we propose a contamination grade recognition method based on the deep learning of ultraviolet discharge images using a sparse autoencoder (SAE) and a deep belief network (DBN). Under different humidity conditions, we filmed and preprocessed the ultraviolet discharge images of insulators at different contamination grades and we obtained the ultraviolet spot area sequence as original data for contamination grade recognition. A double-layer sparse autoencoder was used to extract sparse features that could characterize different contamination grades from the ultraviolet spot area sequence. Using the extracted features, a DBN composed of three layers of restricted Boltzmann machine was trained to provide contamination grade recognition. To verify the effectiveness of the method proposed in this paper, high-voltage experiments were performed on contaminated insulators at relative humidity levels of 80%, 85%, and 90%, and ultraviolet images were recorded. The proposed SAE–DBN method was used to identify the ultraviolet images of the insulators with different contamination grades. The recognition accuracy rates at the three humidity levels were 91.25%, 93.125%, and 92.5%. The experimental results showed that this method could accurately recognize the contamination grade of the insulator and provide guidance for the prevention of contamination flashover based on the pollution severity.