Loading...
Improvement of algorithm to reduce training time of back-propagation neural network for transformer interturn fault location
Author(s)
Date Issued
October 29, 2012
Type
Conference Paper
Abstract
This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and back-propagation neural networks for location of interturn faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented by MATLAB. In addition, the choice of initial number of neurons for the first hidden layer to decrease duration time of train process is taken into account. A comparison between the proposed technique and conventional training is presented. The result is shown that the proposed technique is very effective in reduce training time and gives a satisfactory accuracy. © 2012 IEEE.
Citation
International Conference on Wavelet Analysis and Pattern Recognition, 131-136, 2012