Potentials - January/February 2016 - 38
Fault classification
in transmission lines
using wavelet
multiresolution analysis
P. Rajaraman, N.A. Sundaravaradan, Rounak Meyur,
M. Jaya Bharatha Reddy, and D.K. Mohanta
T
ransmission lines are the
vital links between generating stations and distribution substations, and it is
essential to study, understand, and analyze the faults that occur in
transmission lines. The isolation of the faulty line(s) is
an important aspect of
power system protection,
as it not only prevents a
calamitous situation but
also protects the apparatus
and increases its useful life.
Transient signal analysis in the frequency domain has become popular
in recent years due to its
wide range of applications. The analysis gives
very detailed information
about the frequency content present in the signals.
A wavelet transform is a
well-known tool used to
convert the original timedomain signals to frequency-domain signals.
The wavelet transform has a promising application in the field of fault analysis in power systems. The efficient
processing and sufficient resolution
it provides in both the time and fre-
Digital Object Identifier 10.1109/MPOT.2015.2468775
Date of publication: 8 January 2016
38
n
J a n u a r y/Febr uar y 2016
quency domains renders it an almost indispensable tool in modernday research. Employing wavelet
analysis, information regarding the
sub-band frequencies can be ex-
To implement the wavelet multiresolution analysis (MRA), we make
use of a mother wavelet. The choice
of the mother wavelet plays an important role for fault detection and
iMAge liceNSeD By iNgRAM PuBliShiNg
tracted from the transient signals,
which is useful for fault detection
and classification. An efficient algorithm is essential so as to quickly
identify a fault and to facilitate the
repair and/or restoration of the
line. This improves the reliability of
the system.
IEEE PotEntIals
classification in the transmission
line. Based on a given application,
the appropriate wavelet must be
chosen. We have studied the comparative use of 16 different wavelets
for the purpose of fault classification. For this application, we have
investigated the results obtained
0278-6648/16©2016IEEE
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