Open AccessResearch on UHF PD detection method based on improved DBN
Author(s) -
Feng Yuan,
Pengran Ma,
Xuyang Zhang,
Hong Jia,
Haifeng Wang,
PengQiao Zhang,
Xiangyang Li,
Bowen Zhou,
Cong Yu,
Xiangchen Dai,
FuZhen Xuan,
Kai Wu,
Chunzheng Li,
Xiaoxiao Hu,
Yu Zhang
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/983/1/012005
Subject(s) - ultra high frequency , partial discharge , switchgear , computer science , reliability (semiconductor) , power grid , sigmoid function , power (physics) , data mining , reliability engineering , real time computing , pattern recognition (psychology) , artificial intelligence , electrical engineering , engineering , telecommunications , artificial neural network , voltage , physics , quantum mechanics
With the development of power grid technology and the widespread application of gas insulator switchgear (GIS) equipment, the power supply reliability of the power system has been greatly improved, but the problem of partial discharge (PD) faults in GIS has always been prominent, seriously affecting the safe and stable operation of the power grid. How to quickly determine the type and cause of GIS discharge is the key to online PD detection. In this paper, in order to deal with the very complicated data processing of ultra-high frequency (UHF) PD, the time-consuming and low efficiency of manually judging the type of PD, a classification model of UHF PD system based on deep confidence network (DBN) is established and an automatic classification method for UHF PD based on improved DBN is proposed; the activation function Sigmoid is improved to effectively prevent the occurrence of the gradient disappearance problem; the optimized DBN parameters are used to train and classify data of different PD types. The classification accuracy rate of the test results reached 96.7%, realizing the rapid classification evaluation of UHF PD types.