International Journal of Scientific & Engineering Research, Volume 5, Issue 2, February-2014 1229
ISSN 2229-5518
IJSER © 2014
http://www.ijser.org
Abstract— Proper detection of various faults occurring on the transmission line is very essential. In this paper, detection and
classification of some these faults is done based on the information conveyed by the wavelet analysis of power systems transients.
Maximum norm values, maximum detail coefficient, energy of the current signals are calculated from the Wavelet Toolbox in
MATLAB/Simulink. Maximum norm value and energy of the signals detects the fault and threshold detail coefficient classifies the fault
into different types such as L-G, L-L, L-L-G, L-L-L.
Index Terms—Fault detection, Fault classification, Multiresolution analysis (MRA), Power systems, Wavelet Transform (WT),
Signal energy, Dabauchies Wavelet.
—————————— ——————————
1. INTRODUCTION:
ARGE number of faults occurs on the transmission
line. These faults cause irregularities in the power
flow through the line. Basically, a fault occurs when
two or more conductors come in contact with each other
or ground. In three phase systems transmission line faults
are classified as Single line-to-ground faults, Line-to-line
faults, Double line-to-ground faults, and three phase
faults [1]. For it is at such times that the power system
components are subjected to the greatest stresses from
excessive currents. These faults give rise to hazardous
damage of power system equipment and also the power
quality. To carry on the regular power flow in the system,
these faults are to be detected. Recently, distance relays
have experienced much improvement in the field of fault
detection due to the adoption of digital relaying. Signal
processing is one of the most important parts of the
operation for fault detection. Until recently, Fourier
analysis and Kalman filtering methods were the main
tools in signal processing for fault detection.
Wavelets are a recently developed mathematical tool
for signal processing. Compared to Fourier analysis,
which relies on a single basis function, a number of basis
functions of a rather wide functional form are available in
wavelet analysis [2]-[3]. The basic concept in wavelet
transform (WT) is to select an appropriate wavelet
function “mother wavelet” and then perform analysis
using shifted and dilated versions of this wavelet.
Wavelet can be chosen with very desirable frequency and
A. D. Borkhade is research scholar at Govt. College of Engineering,
Amravati (M.S.) India. (E-mail: borkhadeanurag26@gmail.com)
Dr. N. D. Ghawghawe is Associate Professor in Department of
Electrical Engineering, Govt. College of Engineering, Amravati (M.S.)
India. (E-mail: g_nit@rediffmail.com)
time characteristics as compared to Fourier techniques.
The basic difference is that, in contrast to the short time
Fourier transform which uses a single analysis window,
the WT uses short windows at high frequencies and long
windows at low frequencies. The basic functions in WT
employ time compression or dilation rather than a
variation in time frequency of the modulated signal.
2. WAVELET THEORY:
Wavelet theory is very new (about 25 years old) but
has already proved useful in many contexts. Wavelet may
be seen as a complement to classical Fourier
decomposition method.
Wavelet calculations are based on two fundamental
equations: the scaling function
) (t ϕ
and the wavelet
function
) (t ψ
[4].
(1)
(2)
These functions are based on the chosen scaling function
) (t ϕ
(mother wavelet) which satisfies the following
conditions:
Detection and Classification of
Transmission Line Faults Using Wavelet
Transform
A. D. Borkhade, N. D. Ghawghawe
L
) 2 ( ) ( k t h t
k
k
− =
∑
ϕ ϕ
) 2 ( ) ( k t g t
k
k
− =
∑
ψ ψ
2
1
=
∑
=
N
k
k
h
1 .
2
1
=
+
=
∑ l k
N
k
k
h h
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