UV‐flashover evaluation of porcelain insulators based on deep learning. Issue 6 (1st September 2018)

• Record Type: Journal Article
• Authors: Pei, Shaotong; Liu, Yunpeng; Ji, Xinxin; Geng, Jianghai; Zhou, Guangyang; Wang, Shenghui
• Is Part Of: IET science, measurement & technology. Volume 12:Issue 6(2018)
• Page Start: 770
• Page End: 776
• Publication Date: 2018-09-01
• Subjects: flashover -- porcelain insulators -- learning (artificial intelligence) -- neural nets -- ultraviolet spectra -- corona -- sparks -- image classification -- leakage currents -- charge measurement -- electric current measurement -- computerised instrumentation -- power engineering computing; UV‐flashover evaluation -- convolutional neural network model -- deep learning theory system -- intelligent method -- ultraviolet discharge -- porcelain insulator chip -- power frequency flashover testing -- ultraviolet spectra -- weak coronal discharge -- spark discharge -- FILIN UV imaging method -- modified Alexnet deep CNN model -- classification training -- identification assessment -- flashover warning detection -- leakage current parameter -- multilayer combination -- end‐to‐end autonomous learning -- intelligent detection; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2017.0465
• Languages: English
• ISSNs: 1751-8822

Abstract

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.