Open AccessBased on Rough Set and RBF Neural Network Power Grid Fault Diagnosis
Author(s) -
Xiaoqin Liu,
Hongchang Sun,
Mengchan Wu,
Jiayao Yang
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/300/4/042113
Subject(s) - rough set , decision table , troubleshooting , reduction (mathematics) , artificial neural network , fault (geology) , computer science , sample (material) , set (abstract data type) , data mining , training set , artificial intelligence , table (database) , pattern recognition (psychology) , machine learning , mathematics , chemistry , geometry , chromatography , seismology , programming language , geology , operating system
In order to improve the accuracy of troubleshooting results and save diagnostic time, rough set and RBF neural network are used to diagnosed fault of power grid.Rough sample reduction program is used to reduce the fault samples, and the reduction decision table is obtained as the input of the RBF neural network, get the training results.The fault samples without rough set reduction are input into RBF for training, and the two training results are compared. It is found that the samples using the rough set reduction have the same diagnostic ability as the initial decision table.Obviously, the rough decision set initial decision table can greatly reduce the size of the training sample and save diagnosis time.