This paper proposed a comparative study for detecting and classifying fault on a parallel transmission line
using cascade forward and feed forward back propagation.
Both calculations were based on discrete wavelet transform
(DWT) and Clarke’s transformation. Daubechies4 mother
wavelet (Db4) was used to decompose coefficients of wavelet
transforms coefficients (WTC) and wavelet energy coefficients (WEC) of high-frequency signals. The coefficients were inputs for the training of neural network back-propagation (BPNN).
The results showed that the feed forward back propagation
approach based on Artificial Neural Network (ANN) models
achieved better results than Cascade forward back
propagation approach models, particularly in detecting and
classifying fault on a parallel transmission line. The results showed that the introduced approach for fault analysis is capable to classify rapidly and correctly all the faults on the parallel transmission line.