UV‐flashover evaluation of porcelain insulators based on deep learning

Abstract

Based on the analysis of the principle and structure of the convolutional neural network (CNN) model in a deep learning theory system, an intelligent method for judging the flashover of a porcelain insulator with ultraviolet discharge is proposed. In this method, the porcelain insulator chip was subjected to power frequency flashover testing, and the ultraviolet spectra of different discharge states without discharge, weak coronal discharge, and strong spark discharge were captured by FILIN UV imager. The Alexnet deep convolution neural network model was used to predict the discharge state of the UV spectrum for classification training and identification assessment. The new method doesn't use UV imaging to detect flashover warnings. It is necessary to extract the characteristics of the UV spectra and leakage current parameters manually. The multi-layer combination of UV imaging method with end-to-end autonomous learning with deep learning training and the adopted test method provided classification identification, through a large number of UV images in the deep CNN training. This allowed flashover evaluation of abstract feature parameters of the independent extraction. The results show that this method has the advantages of high accuracy, and provides a new idea for the intelligent detection of UV flashover.