JOURNAL ARTICLE

A novel wavelet transform and neural network based transmission line fault analysis method

Abstract

In the present scenario of market driven business, power supply has become more like a commodity. Reliable and quality power need to be ensured to meet customer requirements. In such a situation, it is extremely important that transmission line faults be identified accurately, reliably and in quick time. Advanced signal processing tools such as discrete wavelet transform (DWT) can be used very effectively for parameterisation and characterisation of the fault signals. On the other hand, properly configured Neural Network (NN) can be utilised for classification of the faults based on the DWT signal. The present contribution uses electromagnetic transient program (EMTP) for simulation of a model transmission system and MATLAB for DWT and NN. Various types of faults have been simulated at different locations along the transmission line and an attempt has been made to correctly identify and locate the fault.

Keywords:
Computer science Wavelet transform Wavelet Transmission line Artificial neural network Fault (geology) Artificial intelligence Pattern recognition (psychology) Telecommunications

Metrics

3
Cited By
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FWCI (Field Weighted Citation Impact)
0
Refs
0.07
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Citation History

Topics

Power Systems Fault Detection
Physical Sciences →  Engineering →  Control and Systems Engineering
Islanding Detection in Power Systems
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Machine Fault Diagnosis Techniques
Physical Sciences →  Engineering →  Control and Systems Engineering
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