Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition

Authors:
Sami Ekici;Selcuk Yildirim;Mustafa Poyraz
Affiliations:
Department of Electrical Education, Firat University, 23119 Elazig, Turkey;Department of Electrical Education, Firat University, 23119 Elazig, Turkey;Electrical and Electronics Engineering, Firat University, 23119 Elazig, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

The aim of this paper is to estimate the fault location on transmission lines quickly and accurately. The faulty current and voltage signals obtained from a simulation are decomposed by wavelet packet transform (WPT). The extracted features are applied to artificial neural network (ANN) for estimating fault location. As data sets increase in size, their analysis become more complicated and time consuming. The energy and entropy criterion are applied to wavelet packet coefficients to decrease the size of feature vectors. The test results of ANN demonstrate that the applying of energy criterion to current signals after WPT is a very powerful and reliable method for reducing data sets in size and hence estimating fault locations on transmission lines quickly and accurately.