Power transformer fault diagnosis by improved hybrid algorithm based on genetic algorithm and back propagation algorithm - Details

author：

Wang, S.-F. (Wang, S.-F..) ^[1] | Cai, J.-D. (Cai, J.-D..) ^[2] | Liu, Q.-Z. (Liu, Q.-Z..) ^[3]

Indexed by：

Scopus PKU CSCD

Abstract：

The　hybrid　algorithm　which　combines　improved　genetic　algorithm　(GA)　with　error　back-propagation　algorithm　(BP)　is　used　to　train　artificial　neural　network.　The　defects　of　conventional　BP　algorithm,　i.e.,　easy　to　fall　into　local　minimum,　slow　convergence　speed　of　the　weight　value　of　learning　network,　and　that　of　GA,　i.e.,　the　training　speed　is　too　slow　when　GA　is　used　to　train　the　neural　network　effectively　improved　by　itself,　are　effectively　improved　by　the　hybrid　algorithm.　The　application　of　the　hybrid　algorithm　to　power　transformer　fault　diagnosis　is　simulated,　the　results　show　that　the　hybrid　algorithm　possesses　faster　convergence　speed　and　higher　calculation　accuracy.

Keyword：

Artificial neural network; Fault diagnosis; Genetic algorithm; Power transformer

Community：

[ 1 ] [Wang, S.-F.]Dept. of Electrical Engineering, Fuzhou University, Fuzhou 350002, Fujian Province, China
[ 2 ] [Cai, J.-D.]Dept. of Electrical Engineering, Fuzhou University, Fuzhou 350002, Fujian Province, China
[ 3 ] [Liu, Q.-Z.]Dept. of Electrical Engineering, Fuzhou University, Fuzhou 350002, Fujian Province, China

Reprint 's Address：

[Wang, S.-F.]Dept. of Electrical Engineering, Fuzhou University, Fuzhou 350002, Fujian Province, China

Email：

Show more details

Related Keywords：

Source ：

Power System Technology

ISSN： 1000-3673

Year： 2004

Issue： 4

Volume： 28

Page： 30-33

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 0

Affiliated Colleges：

Get Fulltext

Library Discovery Baidu Scholar Search SCOPUS

Type
Departments

All Years Choose Year From to