Potentials - January/February 2016 - 40

A sampling rate of 3 kHz is selected,
and various wavelets are separately
employed as the mother wavelet. From
this value of sampling frequency, the
signal is decomposed into four levels
by passing through the MRA filter
bank. Figure 2 depicts the stages of
decomposition and the detail coefficients up to four levels.
As shown by Chanda et al., in the
event of fault occurrence, the second
and third harmonics in the transient
signal are dominant and can therefore be used to detect the fault. This
feature also provides useful information for the process of fault classification. From Fig. 2, it is observed
that the fourth-level detail coefficient
(d4) gives the frequency components
(100-150 Hz) corresponding to second and third harmonics.
In the process of transmission line
fault analysis, the information located
in these frequency bands provides im-

portant clues that help in the detection and classification of faults. By
randomly shifting the point of fault on
the 300-km transmission line, simulations are carried out using MATLAB. Thus based on the fourth-level
detail coefficients of the time domain
current signals at both buses, a fault
classification algorithm based on the
wavelet MRA technique is presented
in the next section, "Algorithm for
fault classification."

algorithm for fault classification
Wavelet MRA has been used to
obtain the fourth-level detail coefficients of the fault current signals
from each phase of the three-phase
transmission line. These coefficients are used for the fault classification procedure in the following
manner. The summation of the
wavelet coefficients for each phase
is performed.

Let M r1, M y1, M b1 be the summation of fourth-level detail coefficients
of the transient current signals line
(near bus 1) for phases R, Y, and
B, respectively, of the three-phase
transmission line. Similarly, let
M r2, M y2, M b2 be the summation of
fourth-level detail coefficients of the
transient current signals (near bus
2) for r, y, and b phases, respectively.
From the above coefficients, the following notations are derived.
D r12 = absolute (eM r1 e - eM r2 e),
D y12 = absolute (eM y1 e - eM y2 e),
D b12 = absolute (eM b1 e - eM b2 e),
M r12 = M r1 + M r 2,
M y12 = M y1 + M y2,
M b12 = M b1 + M b2 .
The value of fault inception angle (FIA) has an effect on the phase
current samples obtained and,
therefore, also on the output of the
wavelet transform of the post-fault
signals. The signals are periodic,

Check If
OMr1 - Mr2K c 0 Yes

Fault Is
Y - B Fault

No
Fault Is
L-L
or
L-L-L

Check If
OMy1 - My2K c 0 Yes
No

Begin
Get
Mr1, My1, Mb1,
Mr2, My2, Mb2.
Calculate
Dr12 = KKMr1K- OMr2KK

Check If
OMb1 - Mb2K c 0 Yes
Check If
Yes
(Mr12 + My12 +
No
Mb12) c 0

Dy12 = KKMy1K- O(My2KK

Mr12 = Mr1 + Mr2,
My12 = My1 + My2,
Mb12 = Mb1 + Mb2

No

Check If
Dr12 c Dy12

Db12 = KKMb1K- O(Mb2KK

Check If
Dy12 c Db12

fig3 A flowchart showing the fault classification algorithm.

J a n u a r y/Febr uar y 2016

Fault Is
B - G Fault

Yes

Fault Is
R - G Fault

Yes

Fault Is
Y - G Fault

Fault Is
R - Y- B Fault
Db12

R - Y- G
Fault

IEEE PotEntIals

Dy12

B - R- G
Fault

No

No

n	

Yes

Fault Is
R - Y Fault

No
Fault Is
L-G
or
L-L-G

Check If
Db12 c Dr12

40

Fault Is
R - B Fault

Minimum of
(Dr12, Dy12, Db12)

Dr12

Y-B-G
Fault



Table of Contents for the Digital Edition of Potentials - January/February 2016

Potentials - January/February 2016 - Cover1
Potentials - January/February 2016 - Cover2
Potentials - January/February 2016 - 1
Potentials - January/February 2016 - 2
Potentials - January/February 2016 - 3
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Potentials - January/February 2016 - 48
Potentials - January/February 2016 - Cover3
Potentials - January/February 2016 - Cover4
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