Cross-correlation based distance estimation of single line to ground faults using Elman back-propagation neural network

In this article a novel method for the estimation of distance of fault location for single line to ground faults using cross- correlation and Elman Back Propagation Neural Network has been presented. In this proposed work a distinctive analogy has been incorporated between the cross-correlogram obtained from a non- faulty phase and a faulty phase in electric power system and an Electrocardiogram (ECG) of human heart at normal condition. Importance is also involved to the feature extraction & ECG-fault signals analogy, otherwise the majority of the scheme may not be implemented accurately. Furthermore this proposed method alleviates the problems related with fault distances by estimating it & reduces the faulty impacts on transmission line.