Open AccessGIS Partial Discharge Patterns Recognition with Spherical Convolutional Neural Network
Author(s) -
Wei Yang,
Guobao Zhang,
Taiyun Zhu,
Mengyi Cai,
Haisen Zhao,
Jing Yan,
Yanxin Wang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/853/1/012001
Subject(s) - computer science , convolutional neural network , fault (geology) , feature extraction , partial discharge , artificial intelligence , pattern recognition (psychology) , artificial neural network , feature (linguistics) , data mining , convolution (computer science) , algorithm , engineering , linguistics , philosophy , voltage , seismology , electrical engineering , geology
The ubiquitous construction of the power Internet of Things provides a new idea for the real-time and accurate diagnosis of GIS partial discharge online monitoring fault diagnosis. However, the traditional partial discharge fault diagnosis method is difficult to solve the problem that the fault information of different online monitoring systems is different from the reference axis. In order to solve the problem that the fault information is difficult to identify in rotation and transformation, and improve the accuracy of fault diagnosis, this paper proposes a spherical convolutional neural network based on complex data sources. First, the PRPS picture transmitted to the ubiquitous power Internet of Things terminal is selected as the fault feature information. Secondly, a generalized Fourier algorithm (GFT) algorithm is used to construct a spherical convolution structure for PD pattern recognition. The algorithm can perform automatic feature extraction. Thirdly, the spherical convolutional neural network-based PD recognition method is applied to processing of the complex data sources with 84.88% average accuracy rate. It shows that the PRPS 3D map is one of effective way to avoid the complexity of artificial feature extraction for spherical CNN and in the meantime, it can also improve the accuracy of fault diagnosis.