The application of particle swarm optimization-based RBF neural network in fault diagnosis of power transformer

Author

Niu, Wu ; Xu, Liang-Fa ; Wu, Ji-Lin

Author_Institution

Dept. of Found., First Aeronaut. Inst. of Air Force, Xinyang, China

fYear

2009

fDate

8-11 Aug. 2009

Firstpage

534

Lastpage

536

Abstract

In order to solve the problem of dasiaover-fittingpsila, local optimal solution existing in BP neural network, particle swarm optimization-based RBF neural network (PSO-RBFNN) is proposed. Particle swarm optimization (PSO) is an intelligent swarm optimization method, which not only has strong global search capability, but also is very easy to implement. Thus, PSO is used to determine free parameters of RBF neural network. Finally, the effectiveness and correctness of this method are validated by the result of fault diagnosis cases.

Keywords

fault diagnosis; particle swarm optimisation; power engineering computing; power transformer testing; radial basis function networks; RBF neural network; fault diagnosis; intelligent swarm optimization method; particle swarm optimization; power transformer; Arithmetic; Birds; Dissolved gas analysis; Fault diagnosis; Feedforward neural networks; Hydrogen; IEC; Neural networks; Particle swarm optimization; Power transformers; RBF neural network; classification arithmetic; fault diagnosis; parameter optimization; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-4519-6

Electronic_ISBN

978-1-4244-4520-2

Type

conf

DOI

10.1109/ICCSIT.2009.5234794

Filename

5234794