International Conference on Science, Technology and Innovation for Sustainable Well
-
Being
(STISWB), 23
-
24 July 2009, Mahasarakham University, Thailand
1
Distance Transmission Lines Protection
Based on Recurrent Neural Network
Saichoomdee
, S
.
,
Oonsivilai
,
A.
,
Marungsri, B.
,
Kulworawanichpong, T.
and
Pao
-
La
-
Or, P
.
Alternative and Sustainable Energy Research Unit, Power and Control Research Group,
Scho
ol of Electrical Engineering, Institute of Engineering, Suranaree University of Technology,
111 University Street, Muang District, Nakhon Ratchasima 30000, Thailand
E
-
mail:
saichoomdee@gmail.com,ana
n
t@sut.ac.th
,
bmshvee@sut.ac.th
,
thanatch@sut.ac.th
,
padej@sut.ac.th
,
Abstract
The
adaptation
of protective distance relay to determine the presence and location of faults in a
transmission based o
n the measure
of fixed settings such as line impedance has been achieved by the
application
of several different techniques. However
, for the modern power systems a
fast, accurate
and robust technique for real
-
time purposes is required. In this paper, the application o
f recurrent
neural network in transmission line protection is demonstrated. The method
applies the power system
using
voltage and current signals to learn the hidden relationship existing in the input patterns. It is
observed that the proposed technique is
able to ident
ify the precise fault direction
more rapidly.
System simulations studied show that the proposed approach is able to detect the direction of
a fault
on a transmission line swift
ly and correctly,
th
erefore
suitable for the real
-
time purposes.
Keywords:
recurrent neural network; distance protection; power system; relaying.
1.
Introduction
Transmission
line
system
is
regarded
with
great
importance in power system
.
Fault
s
that
occur
frequently with
tran
s
mi
ss
i
o
n
lines
system
, sho
uld affect elect
ricity user
s
.
F
aults,
aforementioned may be
cause
d by neither a
single person, animal or natural occurrences
.
Thus
to prevent
and
decrease
damage that
would
happen
,
must
syst
ematically protect
the
tran
s
mi
ss
i
o
n
line system
.
T
ransmiss
i
on line
system
using di
stance relay
is very popular
.
I
n
this current, using an intelligent system
solves
a problem i
n the remedy of
power system
widely.
F
or example
,
dynamic load modeling
[
8
]
,
short term load forecasting
[
9
]
,
stabili
ty
in
power system
[10
]
for
tran
smiss
ion lines
protection
have
been
using neur
al network
,
could
test by
distance
relay
[5
-
6] neural
network,
electric base
will n
ot be used in
calculation
but,
the path calculated
will be used
which
is
obtained by
the format of learning or
the ability
to memorize
of neu
ral network
;
accompanied with flexibility in itself
makes
neural network very interesting
.
W
e can use
neural network in learning and memorizing the
format of fault
and the format of condition
changing
of power
system
.
Although this
might force relay
,
it
pr
otect
s
tran
smission
lines, with increasing precision
(
zone 1)
and
can be applied accompanied with
dis
tance
prevention normally which, hypothetically
wi
ll
help testify the protection of transmiss
ion lines
become much more accurate
.
This paper proposed
dista
nce transmission
lines protection based on recurrent neural
network.
2.
Neural Network
T
he sys
tem assembles a mode format from
the human brain
.
T
he
duty and the work of
neural,
be
could
built
large
-
sized
and could
teach the
system for the lead gone are us
able,
especial
ly
,
the principle works of neural, will
find the
weight
value in work system of neural,
the
n
the
c
omparison output beg for neural, that
get with target value that fix
.
I
f
the value
output is not
equal to target
value,
t
he system
of neural wil
l find the value of the weight until
it reaches
the value of output
, t
he new
sub
stitute
value
is equal to target value
.
International Conference on Science, Technology and Innovation for Sustainable Well
-
Being
(STISWB), 23
-
24 July 2009, Mahasarakham University, Thailand
2
For the
neural
to be built give with the
capability to
learn
.
Mu
st have input and output
data to use in
comparison
. I
nspires the
use
ord
ered
pair
s in traini
ng network
[11
-
12
]
.
2.1
Recurrent Neural Network
Recurrent
network
is
the
connections
interval neural in feedback
,
recurrent neural
network structure is shown
in
Fig 1.
Fig
.
1
:
Recurrent
n
eural
n
etwork
s
t
ructure
Fig.1
Show
s
recurrent neural network
structure
;
Input patterns
have
P
1
,P
2
,…,P
R
,
a
1
(k)
to
be output of
hidden layer 1 and
to
be
input
of hidden layer 2
,
a
2
(k)
to
be input of
hidden layer 2
,
a
3
(k)
to
be
final output.
Have
ƒ
1
, ƒ
2
and
ƒ
3
are transfer
function,
a
1
(k),
a
2
(k)
and
a
3
(k)
can get from the algebraic equation
as follow
ing could
:
(1)
(2
)
(3
)
Whereas
:
;
weights
value
connections
between
input
layer
with
hidden layer
1
;
weights
value
connections between
hid
den layer
1 with hidden layer 2
;
weights
value
connections between
hidden layer 1 with
output layer
;
bias
value
in hidden layer
1
;
bias
value
in hidden layer
2
;
bias
value
in
output layer
3
Training neural network by
gradient descent
algorit
hm
with
tan sigmoid
transfer
function
u
sing n
eural
n
etwork
t
oolbox
of matlab
program
[3]
(4)
Where
as
:
n
;
summation output
b
;
bias adjust.
3
.
Application Recurrent
Neural
Network for
Transmission Lines
Protection
Fig.
2
show
s
recurrent neural network
for
t
ransmission
l
i
nes
p
rotection structure.
Fig.
3
the
recurrent
neural
network
for classifying
the
input patterns into expected categories.
There
are three input signals required at the input
layer
in recurrent
neural
network
:
V
,
I, and X
.
V is voltage, I is current, and
X
is
apparent
impedance
,
the measurement
of the faulted
transmission line
.
The output consists of
recurrent
neural
network which has a
continuous
-
value ou
tput in the region
[0
, 1
].Output
1 indicate
s
tripping,
0
indicates
non
-
tripping
.
Fig.
2
:
Recurrent
neural network
for
transmission lines
protection structure
Fig.
3
:
Input
patterns
classify
of recurrent
neural network
International Conference on Science, Technology and Innovation for Sustainable Well
-
Being
(STISWB), 23
-
24 July 2009, Mahasarakham University, Thailand
3
4.
Simulation
and Result
Training patterns
and
test pat
terns
g
o
t
from
fault
simulation
on
transmission
line
of
power
system
study
using
MATLAB
and
SIMULINK
.
Fig.
4
depicts
the
115
kV,
5
0 Hz
si
mulated system one
-
line diagram
. The other
related parameters of the simula
ted system are
show
n
in
Table 1
.
Fig.
4
:
One
-
line diagram of simulation system
Table
1
:
The parameters of the simulation
t
ransmission
system
Bus 1
:
Voltage 1
15 kV , 50 Hz
Equivalent source impedance
Z
1
= 0.00499 +j0.03384
p.u. /km
Z
0
=
0.00425+j0.0369 p.u. /km
Length of transmission line
:
1
km
䱩湥潮ot慮t
:
Z
1
= 0.085811+j0.36204 p.u./km
Z
0
= 0.25485+j1.4223 p.u./km
Bus 2
:
Load=
㌮㌫3j㈮㈠2VA
T桥h t敳t will 扥bi渠 wit栠
f慵at
潣o畲r敮e攠
獩m畬慴i潮
慴 t桥h 摩獴慮a攠
〬㈰ⰴ〬㘰ⰸ0
ⰸ㐬㠸ⰹ㈬㤶,
慮a
〠
%
of the
total line length
.
Every the distance fault
occurrence
has
fault
resistance
0.000001,5,10
,
15
, 20, 25,30,35,40
and 50 ohms
respectively.
A
lready lead the data has that
go to test with
the recurrent neural network.
Zone 1
pro
tection
is
80
% of the total line length.
Fig. 4 shows
the phase A current waveform,
and the phase
A voltage waveform for the single phase to
ground fault,
the fault resistance is 10
oh
ms
,
at
times 20 ms
-
60
ms
.
Some part calculation
s
of
pattern
data
tests for the recurrent neural
network in case of fault resistance
5 oh
ms are
show
n
in Table
2
.
Fig
.
5
:
Voltage and current
waveform
a
-
g
fault
Table
2
:
T
he
pattern
data
tests
in
case
of fault
resistance
10
ohm
Distance
Magnitude
A
ngle
(%)
VA
IA
VA
IA
0
114820.0
18635.00
-
0.400
-
8.612
20
114780.0
13840.00
-
0.302
-
19.739
40
114810.0
10910.00
-
0.251
-
22.768
60
114840.0
9108.50
-
0.224
-
22.813
80
114860.0
7996.70
-
0.208
-
22.112
84
114860.0
7844.70
-
0.206
-
22.055
88
114870.0
7718
.50
-
0.204
-
22.064
92
114870.0
7621.60
-
0.202
-
22.149
96
114870.0
7559.00
-
0.201
-
22.314
100
114870.0
7537.70
-
0.200
-
22.544
International Conference on Science, Technology and Innovation for Sustainable Well
-
Being
(STISWB), 23
-
24 July 2009, Mahasarakham University, Thailand
4
Table
3
:
Accuracy
transmission
line protection
Distance
(%)
Accuracy
(%)
Error
(%)
0
100
0
20
100
0
40
100
0
60
100
0
80
10
0
0
84
90
1
0
88
100
0
92
100
0
96
100
0
100
100
0
5.
Conclusion
This paper analyze
s recurrent neural
network intelligent computational techniques
application associated with protective distance
relay for transmission line protection.
B
e the
one
method
transmission line
prevention
,
t
he
back manages to apply
recurrent neural
network
in transmission
line
prevention
. From
testing it is correct in
90
transmission line
percentage preventions
,
from the data tests 100
all the data set
tests
.
T
hus
,
recurrent
neural
network
with
the ability to relay one type
in
transmission
line
prevention
.
Therefore
recurrent neural network
c
ould
be used as an
effective tool for real
-
time digital relaying
purposes.
That
might allow
distance relay
work
more accur
acy
and
precision
.
6.
References
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P.M.
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