State Recognition of Surface Discharges by Visible Images and Machine Learning

To solve the problems of low resolution, weak antinoise performance, and imprecise fault location in discharge recognition of ultraviolet imaging, an intelligent state recognition method of surface discharge based on visible images and machine learning (ML) is proposed in this article. A visible image library of surface discharge (ac) under different experimental conditions was established, and the chromatic, gray-scale, and morphological features of images were extracted. The image library was divided into four stages by clustering. Combined with spectrum correlation experiments, the physical meaning of the division results was explained. Supervised learning algorithms, including classic algorithms and deep learning, were used to train intelligent recognition models. The results showed that models based on chromatic features had a higher accuracy for recognition; the accuracy of artificial neural networks reached 0.982, which was obviously higher than that of classical learning algorithms (0.886). The results show that visible images can be used for the state recognition of surface discharges effectively. The application of ML ensures high efficiency and accuracy. This method will enable state recognition and fault location to be completed simultaneously.

Automated summary

An intelligent state recognition method of surface discharge based on visible images and machine learning (ML) is proposed in this article, which achieved high efficiency and accuracy through establishing a visible image library and using supervised learning algorithms. The models based on chromatic features had higher accuracy for recognition, with artificial neural networks reaching an accuracy of 0.982.