Bulletin of Electrical Engineering and Informatics
Vol 5, No 3: September 2016

Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN

Purva Sharma (Swami Keshvanand Institute of Technology Management & Gramothan)
Deepak Saini (Swami Keshvanand Institute of Technology Management & Gramothan)
Akash Saxena (Swami Keshvanand Institute of Technology Management & Gramothan)



Article Info

Publish Date
01 Sep 2016

Abstract

Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed that ANN architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network.

Copyrights © 2016






Journal Info

Abbrev

EEI

Publisher

Subject

Electrical & Electronics Engineering

Description

Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the ...