Title :
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
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;
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
DOI :
10.1109/ICCSIT.2009.5234794
