Intelligent Recognition of Corona Discharges by Visible Images

This paper proposed a corona discharge intelligent diagnosis method based on visible images and machine learning. Through multiple groups corona discharge experiments, a visible image library containing corona discharge under different gap distances was established. The color, morphological, and gray features of the image were extracted to form 7-dimensional feature data. At the same time, the RGB gray level histogram (RGB-GLHs) and the gray level histogram (GLHs) were extracted as contrast features. Unsupervised learning method was used to divide the image library according to the severities; then supervised learning algorithms were applied to train intelligent diagnosis models, which were able to identify the discharge stage corresponding to the image automatically. The recognition results showed that the 7-dimensional feature data had a higher recognition accuracy than RGB-GLHs and GLHs. This showed that the 7-dimensional feature data could effectively cover the discharge information contained in original images, and filter out useless information and noise, improving recognition accuracy. The use of high-resolution visible images solves the problems of low spatial resolution and severe noise interference in ultraviolet imaging diagnosis method, and enables the state recognition and fault location to be finished at the same time. The application of machine learning eliminated the interference of human subjective factors in traditional diagnosis methods, greatly improving efficiency and accuracy of diagnoses.