Title :
Wavelet ANN based transformer fault diagnosis using gas-in-oil analysis
Author :
Honglei, Li ; Dengming, Xiao ; Yazhu, Chen
Author_Institution :
Shanghai Jiaotong Univ., China
Abstract :
This paper describes a wavelet artificial neural network (ANN) for signal classification, and applies it for transformer Fault Detection with dissolved gas analysis (DGA). The weights of the network are replaced by wavelet functions and are corrected by conjugate gradient method in the training iteration. Preliminary simulation results show wavelet ANN for DGA can get a 95% correct diagnosis rate, superior then BP ANN. Besides, precondition techniques of input data is studied, a suitable precondition algorithm play an important role in ANN
Keywords :
conjugate gradient methods; fault diagnosis; insulation testing; neural nets; power transformer insulation; power transformer testing; signal classification; transformer oil; wavelet transforms; conjugate gradient method; dissolved gas analysis; fault diagnosis; gas-in-oil analysis; power transformer insulation; precondition algorithm; signal classification; wavelet artificial neural network; Artificial neural networks; Dissolved gas analysis; Fault detection; Fault diagnosis; Gases; Oil insulation; Pattern classification; Power transformer insulation; Power transformers; Wavelet analysis;
Conference_Titel :
Properties and Applications of Dielectric Materials, 2000. Proceedings of the 6th International Conference on
Conference_Location :
Xi´an
Print_ISBN :
0-7803-5459-1
DOI :
10.1109/ICPADM.2000.875651
