Open AccessApplication of Improved BPNN Algorithm in GIS Insulation Defect Type Identification
Author(s) -
Xiao Feng,
Xinyi Hu,
Jun Yong,
BoRu Yang,
Xiang Sun,
Yuanfeng Duan,
Hai Lei Meng,
Yuanyuan Xu
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1346/1/012035
Subject(s) - artificial neural network , convergence (economics) , confusion , computer science , identification (biology) , algorithm , pattern recognition (psychology) , artificial intelligence , rate of convergence , feature (linguistics) , machine learning , data mining , key (lock) , psychology , linguistics , philosophy , botany , computer security , psychoanalysis , economics , biology , economic growth
In this paper, a new BP neural network has been built with GA algorithm, which possesses high effectivity, parallel processing ability and global search feature, in order to overcome the original shortcomings such as the low convergence rate and the confusion of local minimum points. Ten parameters of partial discharge characteristics are obtained as input of BPNN to identify four typical insulation defect physical models designed in this paper for the purpose of measuring the recognition accuracy of the improved BPNN. Experiments show that the improved BPNN has a better performance of convergence speed and recognition accuracy than that of adaptive momentum BP neural network.