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,
r
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-
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[
1
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.
R
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m
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tr
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in
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[
2
]
.
T
h
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s
m
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r
id
p
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t c
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[
3
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Fu
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6
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an
o
et
a
l.
[
8
]
em
p
lo
y
ed
s
m
ar
t
elec
tr
ical
n
etwo
r
k
d
ata
an
aly
s
is
tech
n
iq
u
es
to
d
etec
t
d
is
tu
r
b
an
ce
s
u
s
in
g
p
h
aso
r
m
ea
s
u
r
em
en
t
u
n
its
(
P
MU
s
)
.
T
h
e
u
tili
za
tio
n
o
f
t
h
is
alg
o
r
ith
m
c
o
u
ld
p
o
ten
tially
lead
to
a
d
ec
r
ea
s
e
i
n
d
ata
v
o
l
u
m
e,
r
esu
ltin
g
i
n
th
e
ex
tr
ac
tio
n
o
f
m
ea
n
in
g
f
u
l
in
f
o
r
m
atio
n
f
r
o
m
th
e
d
ataset.
R
esear
ch
er
s
in
tr
o
d
u
ce
d
an
Ap
ac
h
e
s
p
ar
k
f
r
a
m
ewo
r
k
d
esig
n
ed
f
o
r
e
m
b
ed
d
ed
c
o
m
p
u
tin
g
in
th
e
c
o
n
tex
t
o
f
d
ata
an
al
y
s
is
in
s
m
ar
t
p
o
wer
g
r
id
e
n
v
ir
o
n
m
en
ts
[
9
]
.
Ah
m
ed
et
a
l.
[
1
0
]
c
r
ea
ted
a
b
id
ir
ec
tio
n
al
co
m
m
u
n
icatio
n
n
etwo
r
k
c
o
n
n
ec
tin
g
m
u
ltip
l
e
r
esid
en
ce
s
u
s
in
g
clien
t
ag
en
ts
with
in
th
e
tr
an
s
f
o
r
m
er
ag
en
ts
.
T
h
e
ev
alu
atio
n
o
f
th
e
p
r
ec
is
i
o
n
o
f
th
ese
m
o
d
els
was a
s
s
e
s
s
ed
th
r
o
u
g
h
th
e
u
tili
za
tio
n
o
f
e
r
r
o
r
co
ef
f
icien
ts
.
An
et
a
l.
[
1
1
]
em
p
lo
y
ed
a
r
ei
n
f
o
r
ce
m
en
t
d
ee
p
lear
n
in
g
m
o
d
e
l
to
id
en
tify
in
s
tan
ce
s
o
f
d
ata
attac
k
s
in
AC
elec
tr
ical
g
r
id
s
.
T
h
e
f
in
d
i
n
g
s
f
r
o
m
th
e
s
im
u
latio
n
in
d
i
ca
te
a
lim
ited
ab
ilit
y
to
d
etec
t
attac
k
s
wh
en
th
e
m
o
d
el
is
b
ein
g
im
p
lem
e
n
ted
.
L
iao
an
d
An
an
i
[
1
2
]
was
d
ev
elo
p
ed
a
n
e
u
r
al
n
etwo
r
k
f
o
r
th
e
p
u
r
p
o
s
e
o
f
id
en
tify
in
g
d
ef
icien
cies
in
v
o
ltag
e
s
ag
.
T
h
e
c
o
m
p
lex
it
y
id
en
tific
atio
n
is
co
n
s
tr
ain
ed
b
y
th
is
ap
p
r
o
ac
h
.
T
h
e
u
tili
za
tio
n
o
f
h
o
m
o
m
o
r
p
h
ic
en
cr
y
p
tio
n
-
b
ased
d
ata
ag
g
r
eg
atio
n
an
d
b
lo
ck
ch
ai
n
was
s
u
g
g
ested
in
[
1
3
]
to
en
h
an
ce
d
ata
s
ec
u
r
ity
wh
ile
m
ain
tain
in
g
a
h
ig
h
lev
el
o
f
tr
ain
in
g
tim
e
e
f
f
icien
cy
.
A
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
h
as
b
ee
n
u
s
ed
t
o
id
en
tify
th
e
ex
p
o
s
u
r
e
o
f
u
r
b
an
a
r
ea
s
to
s
p
ec
if
ic
s
eismic
h
az
ar
d
s
[
1
4
]
,
as
well
as
t
o
d
is
cr
im
in
ate
b
etwe
en
d
if
f
er
en
t
ty
p
es
o
f
a
r
tific
ial
s
eismic
s
o
u
r
ce
s
[
1
5
]
.
Acc
o
r
d
in
g
to
Ab
d
alza
h
er
et
a
l.
[
1
6
]
,
a
tr
u
s
t
m
o
d
el
b
ased
o
n
a
d
ee
p
au
to
-
en
co
d
er
(
AE
)
is
em
p
lo
y
ed
to
id
en
tify
attac
k
s
in
I
o
T
s
y
s
tem
s
w
ith
th
e
ass
is
tan
ce
o
f
co
g
n
itiv
e
r
a
d
io
.
Fu
r
th
er
m
o
r
e
,
Mo
u
s
taf
a
et
a
l.
[
1
7
]
p
r
esen
ts
th
e
im
p
lem
e
n
tatio
n
o
f
an
o
p
tim
ized
r
eg
r
ess
io
n
m
o
d
el
to
p
r
ed
ict
g
r
o
u
n
d
v
ib
r
atio
n
s
ca
u
s
ed
b
y
b
la
s
t
-
d
r
iv
en
ac
tiv
ities
.
I
n
a
s
m
ar
t g
r
id
,
th
e
p
r
ed
ictio
n
o
f
s
o
lar
g
e
n
er
atio
n
is
ac
co
m
p
l
is
h
ed
u
s
in
g
an
in
tellig
e
n
t m
o
d
el,
as d
em
o
n
s
tr
ated
in
[
1
8
]
.
D
e
e
p
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
es
s
u
c
h
a
s
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
tw
o
r
k
(
C
N
N
)
h
a
v
e
t
h
e
c
a
p
a
b
il
i
t
y
to
i
d
e
n
t
i
f
y
a
n
o
m
a
l
i
e
s
w
it
h
i
n
el
e
c
t
r
i
c
a
l
g
r
i
d
s
.
I
n
a
s
t
u
d
y
c
o
n
d
u
c
t
e
d
b
y
D
i
a
b
a
e
t
a
l
.
[
1
9
]
,
t
h
e
i
m
p
lem
e
n
t
a
t
i
o
n
o
f
C
N
N
,
g
a
t
e
d
r
e
c
u
r
r
e
n
t
u
n
i
t
(
GR
U
)
,
an
d
l
o
n
g
s
h
o
r
t
-
t
e
r
m
m
e
m
o
r
y
(
L
S
T
M
)
m
o
d
e
ls
w
as
c
a
r
r
ie
d
o
u
t
t
o
d
e
t
e
c
t
p
h
y
s
i
c
al
c
y
b
e
r
-
a
t
t
a
c
k
s
i
n
t
h
e
s
m
a
r
t
g
r
id
a
n
d
S
C
A
DA
m
e
t
e
r
i
n
g
i
n
f
r
a
s
t
r
u
c
t
u
r
e
.
T
h
is
m
o
d
e
l
m
u
s
t
c
o
n
s
i
d
e
r
n
u
m
e
r
o
u
s
p
a
r
a
m
e
t
e
r
s
w
it
h
i
n
a
n
e
t
w
o
r
k
en
v
i
r
o
n
m
e
n
t
.
S
i
m
u
lt
a
n
e
o
u
s
l
y
,
th
e
a
d
a
p
t
i
v
e
n
e
u
r
o
-
f
u
z
z
y
i
n
f
e
r
en
c
e
s
y
s
t
e
m
(
A
NF
I
S
)
m
o
d
e
l
w
a
s
s
t
u
d
i
e
d
t
o
i
d
e
n
t
i
f
y
an
d
c
a
t
e
g
o
r
i
z
e
f
a
u
l
ts
wi
t
h
i
n
a
s
m
a
r
t
g
r
i
d
.
T
h
e
p
r
im
ar
y
co
n
tr
ib
u
tio
n
o
f
t
h
is
s
tu
d
y
ca
n
b
e
o
u
tlin
ed
as f
o
llo
ws:
‒
Var
io
u
s
d
ee
p
lear
n
in
g
tech
n
iq
u
es
f
o
r
an
aly
zin
g
s
m
ar
t
g
r
id
d
ata
h
av
e
b
ee
n
c
o
n
s
o
lid
ated
in
o
u
r
r
esear
ch
.
W
e
h
av
e
o
u
tlin
ed
t
h
e
ca
p
ab
ilit
ies an
d
co
n
s
tr
ain
ts
o
f
ea
c
h
m
e
th
o
d
in
d
etail
.
‒
W
e
h
av
e
in
tr
o
d
u
ce
d
an
i
n
n
o
v
ativ
e
in
teg
r
ated
d
ee
p
lear
n
in
g
f
r
am
ew
o
r
k
u
s
in
g
ANFI
S
an
d
L
STM
t
o
id
en
tify
an
d
ca
teg
o
r
ize
v
ar
io
u
s
f
au
lts
with
in
a
s
m
ar
t g
r
id
b
y
an
aly
zin
g
d
ata
co
llected
f
r
o
m
s
m
ar
t m
eter
s
.
‒
T
h
e
ef
f
icac
y
o
f
th
e
s
u
g
g
ested
m
o
d
el
was
also
ass
ess
ed
t
h
r
o
u
g
h
th
e
ex
am
in
ati
o
n
o
f
v
ar
io
u
s
m
etr
ic
s
in
clu
d
in
g
ac
cu
r
a
cy
,
lo
s
s
cu
r
v
e
an
aly
s
is
,
F1
-
s
co
r
e,
R
O
C
a
n
aly
s
is
,
m
o
d
el
co
m
p
lex
ity
,
p
r
ec
is
io
n
-
r
ec
all
ev
alu
atio
n
,
a
n
d
ca
lib
r
atio
n
ass
ess
m
en
t
.
T
h
e
r
est
o
f
th
is
wo
r
k
is
o
r
g
an
ized
as
f
o
llo
ws:
s
ec
tio
n
2
o
u
tlin
es
th
e
ex
p
er
im
en
tal
s
etu
p
u
s
ed
an
d
th
e
d
ee
p
lear
n
in
g
m
eth
o
d
s
em
p
lo
y
ed
.
Sectio
n
3
g
iv
es
th
e
r
esu
lt
s
an
d
d
is
cu
s
s
io
n
u
s
in
g
Op
en
DSS
an
d
MA
T
L
AB
,
alo
n
g
with
th
e
f
u
zz
y
r
u
les
em
p
lo
y
ed
f
o
r
f
au
lt
id
e
n
tific
atio
n
an
d
class
if
icatio
n
.
T
h
e
co
n
cl
u
s
io
n
o
f
th
is
p
ap
er
ca
n
b
e
f
o
u
n
d
in
s
ec
tio
n
4
with
p
er
s
p
ec
tiv
es.
2.
M
E
T
H
O
DO
L
O
G
Y
AND
E
XP
E
RI
M
E
NT
AL
S
E
T
UP
2
.
1
.
E
x
perim
ent
a
l set
up
T
h
e
e
x
p
e
r
i
m
e
n
t
a
l
p
l
at
f
o
r
m
c
o
n
s
i
s
t
s
o
f
a
De
l
l
c
o
m
p
u
t
e
r
e
q
u
i
p
p
e
d
w
i
t
h
a
n
I
n
t
e
l
C
o
r
e
i
7
p
r
o
c
e
s
s
o
r
r
u
n
n
i
n
g
a
t
2
.
2
0
G
H
z
,
6
GB
o
f
R
A
M
,
a
n
d
t
h
e
W
i
n
d
o
w
s
1
0
o
p
e
r
a
t
i
n
g
s
y
s
t
e
m
.
T
h
e
d
a
t
a
a
n
a
l
y
s
is
a
n
d
a
l
g
o
r
it
h
m
i
c
i
m
p
l
e
m
e
n
t
a
ti
o
n
w
e
r
e
ca
r
r
i
e
d
o
u
t
u
s
i
n
g
P
y
t
h
o
n
a
n
d
M
A
T
L
AB
R
2
0
2
3
.
T
h
e
e
l
e
ct
r
i
c
a
l
n
e
t
w
o
r
k
s
i
m
u
l
a
ti
o
n
s
w
e
r
e
p
e
r
f
o
r
m
e
d
w
i
t
h
t
h
e
O
p
e
n
DS
S
s
o
f
t
w
a
r
e
,
w
h
ic
h
e
n
a
b
l
es
d
e
t
ai
l
e
d
m
o
d
e
l
i
n
g
o
f
d
i
s
t
r
i
b
u
t
i
o
n
s
y
s
te
m
s
.
T
h
e
f
a
u
lt
d
e
t
e
c
t
i
o
n
f
r
a
m
e
w
o
r
k
w
as
d
e
p
l
o
y
e
d
o
n
t
h
e
I
E
E
E
1
2
3
-
n
o
d
e
t
e
s
t
f
e
e
d
e
r
,
a
u
g
m
e
n
t
e
d
w
i
t
h
v
i
r
tu
a
l
s
m
a
r
t
m
e
t
e
r
s
f
o
r
d
a
t
a
a
c
q
u
i
s
i
t
i
o
n
a
n
d
m
o
n
i
t
o
r
i
n
g
.
T
h
i
s
c
o
n
f
i
g
u
r
a
t
i
o
n
p
r
o
v
i
d
e
s
a
r
e
a
l
is
t
i
c
e
n
v
i
r
o
n
m
e
n
t
f
o
r
v
a
l
id
a
t
i
n
g
t
h
e
p
r
o
p
o
s
e
d
d
e
t
e
c
t
i
o
n
m
o
d
e
l
.
T
h
e
o
v
e
r
a
ll
s
tr
u
c
t
u
r
e
o
f
t
h
e
e
x
p
e
r
i
m
e
n
t
a
l
s
e
tu
p
i
s
i
ll
u
s
t
r
at
e
d
i
n
Fi
g
u
r
e
1
.
2
.
2
.
P
r
o
po
s
ed
m
et
ho
d f
o
r
f
a
ult
identif
ica
t
io
n
T
h
e
id
en
tific
atio
n
o
f
m
alf
u
n
c
tio
n
s
in
an
elec
tr
ical
g
r
id
e
n
a
b
les
th
e
elim
in
atio
n
o
f
f
au
lts
th
at
ar
is
e
with
in
an
elec
tr
icity
d
is
tr
ib
u
ti
o
n
s
y
s
tem
.
T
h
e
f
au
lt d
iag
n
o
s
is
p
r
o
ce
d
u
r
e
c
o
m
p
r
is
es th
r
ee
d
is
tin
ct
s
tag
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
er
20
25
:
5
0
9
6
-
5
1
0
5
5098
i)
I
n
itially
,
ab
n
o
r
m
al
v
o
ltag
e
a
n
d
cu
r
r
en
t
p
a
r
am
eter
s
in
t
h
e
i
m
p
ac
ted
p
o
r
tio
n
o
f
th
e
elec
tr
i
ca
l
g
r
id
ca
n
b
e
d
etec
ted
an
d
r
ec
o
g
n
ized
.
ii)
S
u
b
s
e
q
u
e
n
t
ly
,
th
e
d
e
te
r
m
in
a
ti
o
n
o
f
t
h
e
o
c
cu
r
r
e
n
c
e
an
d
c
h
a
r
a
c
t
er
i
s
t
i
c
s
o
f
t
h
e
m
a
l
f
u
n
c
t
io
n
i
s
e
s
s
e
n
t
i
a
l
t
o
ex
p
ed
i
t
e
ac
c
e
s
s
i
b
i
l
i
t
y
a
n
d
o
f
f
er
a
d
ep
e
n
d
ab
l
e
r
e
s
o
l
u
t
i
o
n
f
o
r
an
y
i
s
s
u
e
s
t
h
a
t
a
r
is
e
w
i
t
h
i
n
t
h
e
e
l
e
c
t
r
i
c
a
l
g
r
i
d
.
iii)
Ultim
ately
,
r
ec
tify
in
g
th
e
er
r
o
r
p
r
o
m
p
tly
is
ess
en
tial
to
p
r
ev
en
t
an
y
h
ar
m
to
th
e
u
n
af
f
ec
t
ed
s
ec
tio
n
s
o
f
th
e
n
etwo
r
k
.
T
o
ac
co
m
p
lis
h
th
is
task
,
a
u
n
iq
u
e
in
teg
r
ated
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
was
u
s
ed
,
in
c
o
r
p
o
r
atin
g
th
e
L
STM
m
o
d
el
an
d
th
e
ANFI
S
alg
o
r
ith
m
.
T
h
is
m
eth
o
d
in
co
r
p
o
r
ates
f
u
zz
y
lo
g
ic
an
d
n
eu
r
al
n
etwo
r
k
s
tr
ateg
ies
to
ef
f
ec
tiv
ely
d
iag
n
o
s
e
f
au
lts
with
in
a
s
m
ar
t e
lectr
ical
n
etw
o
r
k
u
s
in
g
d
ata
f
r
o
m
s
m
ar
t m
eter
s
.
Fig
u
r
e
2
d
ep
icts
th
e
f
lo
wch
ar
t
o
f
th
e
p
r
o
p
o
s
e
d
m
o
d
el
f
o
r
f
a
u
lt
d
etec
tio
n
,
wh
ich
is
co
n
s
tr
u
cted
u
s
in
g
th
e
n
eu
r
o
-
f
u
zz
y
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
.
I
n
itially
,
t
h
e
attr
ib
u
tes
o
f
th
e
d
ata
o
b
t
ain
ed
f
r
o
m
th
e
in
tellig
en
t
m
eter
s
ar
e
ex
tr
ac
ted
.
Su
b
s
eq
u
en
tly
,
t
h
e
af
o
r
em
en
ti
o
n
ed
d
ata
is
s
et
as
th
e
in
p
u
ts
f
o
r
t
r
ain
in
g
t
h
e
L
STM
m
o
d
e
l.
T
h
e
s
m
ar
t
m
eter
d
ata
is
th
en
u
s
ed
f
o
r
f
au
lt
cl
ass
if
icatio
n
th
r
o
u
g
h
th
e
a
p
p
li
ca
tio
n
o
f
th
e
n
e
u
r
o
-
f
u
zz
y
s
y
s
tem
.
I
f
a
f
au
lt
is
id
en
tifie
d
,
th
e
h
y
b
r
id
s
y
s
tem
will
p
r
o
m
p
tly
p
in
p
o
in
t
an
d
is
o
late
th
e
f
au
lt.
C
o
n
v
er
s
ely
,
if
n
o
f
au
lt
is
d
etec
ted
,
th
e
s
y
s
tem
will
p
r
o
ce
e
d
to
r
e
tr
iev
e
d
ata
o
n
ce
m
o
r
e
f
r
o
m
t
h
e
s
m
ar
t
m
eter
s
.
Fo
llo
win
g
th
e
d
etec
tio
n
o
f
th
e
er
r
o
r
,
a
n
ass
ess
m
en
t
o
f
its
p
r
ec
is
io
n
is
co
n
d
u
cted
.
I
f
th
is
lev
el
o
f
ac
cu
r
ac
y
m
ee
ts
th
e
r
eq
u
i
r
ed
s
tan
d
ar
d
s
,
d
ata
is
p
r
o
d
u
ce
d
t
o
f
ac
ilit
ate
d
ec
is
io
n
-
m
ak
in
g
to
m
an
ag
e
o
p
er
atio
n
s
o
f
r
esto
r
atio
n
.
I
n
ca
s
e
s
o
f
lo
w
ac
cu
r
ac
y
,
ad
ju
s
tm
en
ts
ar
e
m
ad
e
to
th
e
weig
h
t,
h
y
p
e
r
p
ar
am
eter
s
to
e
n
h
an
ce
th
e
r
eliab
ilit
y
o
f
th
e
m
o
d
el.
T
h
e
L
STM
h
y
p
er
-
p
ar
am
eter
s
ar
e
d
eter
m
in
ed
u
s
in
g
th
e
d
ataset,
th
e
n
u
m
b
er
o
f
iter
atio
n
an
d
t
h
e
ac
cu
r
ac
y
ex
p
ec
ted
.
Fig
u
r
e
1
.
E
x
p
er
im
e
n
tal
s
etu
p
Fig
u
r
e
2
.
Flo
w
ch
a
r
t o
f
p
r
o
p
o
s
ed
m
o
d
el
T
h
er
ef
o
r
e,
a
s
tu
d
y
ca
s
e
is
c
o
n
d
u
cte
d
u
s
in
g
an
I
E
E
E
1
2
3
-
b
u
s
test
n
etwo
r
k
.
T
h
is
test
i
n
g
s
y
s
tem
co
n
s
is
ts
o
f
th
r
ee
d
is
tin
ct
p
h
as
es:
p
h
ase
A,
p
h
ase
B
,
an
d
p
h
ase
C
.
T
h
er
ef
o
r
e,
wh
en
th
e
c
u
r
r
en
t
d
ev
iates
f
r
o
m
its
u
s
u
al
p
ath
,
it
in
d
icate
s
a
f
a
u
lt.
T
h
is
test
in
g
s
y
s
tem
co
n
s
i
s
ts
o
f
th
r
ee
d
is
tin
ct
p
h
ases
:
p
h
ase
A,
p
h
ase
B
,
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
s
ma
r
t g
r
id
fa
u
lt d
etec
tio
n
u
s
in
g
n
eu
r
o
-
f
u
z
z
y
d
ee
p
lea
r
n
in
g
a
lg
o
r
ith
m
(
E
tien
n
e
F
r
a
n
ço
is
Mo
u
ck
o
mey
)
5099
p
h
ase
C
.
T
h
er
ef
o
r
e,
wh
en
th
e
cu
r
r
en
t
d
ev
iates
f
r
o
m
its
u
s
u
al
p
ath
,
it
in
d
icate
s
a
f
au
lt.
A
s
i
n
g
le
-
p
h
ase
f
au
lt
is
ch
ar
ac
ter
ized
b
y
th
e
o
cc
u
r
r
en
ce
o
f
a
f
au
lt
b
etwe
en
p
h
ase
A
an
d
g
r
o
u
n
d
,
p
h
ase
B
an
d
g
r
o
u
n
d
,
o
r
p
h
ase
C
an
d
g
r
o
u
n
d
.
Fu
r
th
er
m
o
r
e,
a
two
-
p
h
ase
f
au
lt
r
ef
er
s
to
a
f
au
lt
o
cc
u
r
r
in
g
b
etwe
en
A
an
d
B
,
o
r
A
an
d
C
o
r
B
an
d
C
.
T
h
e
f
au
lt
o
cc
u
r
r
in
g
b
etwe
en
p
h
ase
A
an
d
p
h
ase
B
,
as
wel
l
as
p
h
ase
C
,
is
class
if
ied
as
a
th
r
ee
-
p
h
ase
f
au
lt.
Dev
iatio
n
s
f
r
o
m
th
e
n
o
r
m
al
v
o
ltag
e
r
an
g
e
ca
n
lead
to
o
v
er
v
o
ltag
e
an
d
v
o
ltag
e
d
ip
s
.
Fig
u
r
e
3
illu
s
tr
ates
th
e
n
eu
r
o
-
f
u
zz
y
c
o
n
tr
o
ller
m
o
d
el
.
T
h
is
m
o
d
el
co
n
s
id
er
s
s
ix
in
p
u
t
p
ar
am
eter
s
th
at
co
r
r
es
p
o
n
d
to
th
e
p
h
ase
cu
r
r
en
ts
an
d
v
o
ltag
es,
s
p
ec
if
ic
ally
:
,
,
,
,
,
an
d
.
T
h
e
co
n
tr
o
ller
ca
lcu
lates
th
e
in
p
u
ts
.
T
h
e
r
esu
lt
is
a
n
u
m
er
ical
v
alu
e
th
at
s
ig
n
if
ies
a
s
p
ec
if
ic
o
cc
u
r
r
e
n
ce
o
f
a
m
alf
u
n
ctio
n
with
in
th
e
ele
ctr
ical
d
is
tr
ib
u
tio
n
s
y
s
tem
.
T
h
e
r
esu
lt
is
a
n
u
m
er
ical
v
alu
e
th
at
s
ig
n
if
ies
a
s
p
ec
if
ic
o
cc
u
r
r
en
ce
o
f
a
m
alf
u
n
ctio
n
with
in
th
e
elec
tr
ical
d
is
tr
ib
u
tio
n
s
y
s
tem
.
I
n
itially
,
th
e
d
ata
is
ac
q
u
ir
ed
t
h
r
o
u
g
h
f
au
lt
s
im
u
latio
n
u
s
in
g
th
e
Op
en
DSS
s
o
f
twar
e
o
n
th
e
I
E
E
E
1
2
3
b
u
s
n
etwo
r
k
.
Su
b
s
eq
u
e
n
tly
,
t
h
e
af
o
r
e
m
en
tio
n
e
d
d
ata
is
g
at
h
er
ed
th
r
o
u
g
h
th
e
u
tili
za
tio
n
o
f
in
tellig
en
t
m
eter
s
an
d
s
u
b
s
eq
u
en
tly
s
u
b
jecte
d
t
o
an
aly
s
is
b
y
MA
T
L
AB
’
s
ad
v
an
ce
d
f
u
zz
y
s
y
s
tem
.
T
h
is
s
o
p
h
is
ticated
s
y
s
tem
en
ab
les
th
e
d
etec
tio
n
an
d
p
r
e
cise
lo
ca
lizatio
n
o
f
v
ar
io
u
s
f
au
lts
with
in
th
e
d
is
tr
ib
u
tio
n
n
etwo
r
k
.
Mo
r
e
o
v
er
,
Fig
u
r
e
3
p
r
esen
ts
th
e
d
ata
co
l
lecte
d
f
r
o
m
th
e
s
m
ar
t
m
eter
s
in
s
talled
in
th
e
I
E
E
E
1
2
3
b
u
s
n
etwo
r
k
.
T
h
is
d
ata
in
clu
d
es
th
e
m
ea
s
u
r
e
m
en
ts
o
f
v
o
ltag
e
an
d
cu
r
r
en
t
c
h
ar
ac
ter
i
s
tics
d
u
r
in
g
in
s
tan
ce
s
o
f
p
h
ase
f
au
lts
.
T
h
e
c
u
r
r
en
t
an
d
v
o
ltag
e
ca
n
b
e
class
if
ied
as
“L
o
w”
wh
e
n
th
ei
r
m
a
g
n
itu
d
es
f
all
with
in
th
e
r
an
g
e
o
f
0
to
0
.
1
p
er
u
n
it
(
p
u
)
.
On
th
e
o
th
er
h
an
d
,
th
ey
ar
e
co
n
s
id
er
ed
“Hig
h
”
wh
e
n
th
eir
v
a
lu
es e
x
ce
ed
1
0
%
o
f
th
e
b
ase
v
alu
e.
Fig
u
r
e
3
.
Ar
c
h
itectu
r
e
o
f
n
eu
r
o
-
f
u
zz
y
s
y
s
tem
with
s
ix
in
p
u
ts
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Fig
u
r
es
4
to
9
s
h
o
w
t
h
e
s
im
u
latio
n
r
esu
lts
r
esp
ec
tiv
ely
f
o
r
n
o
r
m
al
c
o
n
d
itio
n
s
,
s
in
g
le
-
p
h
ase
f
au
lt,
two
-
p
h
ase
f
au
lt,
an
d
th
r
ee
-
p
h
ase
f
au
lt.
I
t
ca
n
b
e
illu
s
tr
ated
th
at
th
e
r
esu
lts
v
ar
y
ac
co
r
d
in
g
to
th
e
ca
s
es
co
n
s
id
er
ed
.
Fig
u
r
es
4
(
a)
an
d
Fig
u
r
e
4
(
b
)
g
iv
e
a
co
n
s
tan
t
e
v
o
lu
tio
n
o
f
th
e
cu
r
r
en
t
a
n
d
v
o
lt
ag
e
in
th
e
elec
tr
ical
n
etwo
r
k
.
I
n
n
o
r
m
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e
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n
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e
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m
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r
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0
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an
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Fig
u
r
e
4
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latio
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r
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u
n
d
er
n
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al
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l
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,
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6
,
Dec
em
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er
20
25
:
5
0
9
6
-
5
1
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5100
I
n
Fig
u
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ase
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ated
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u
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Fig
u
r
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5
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Simu
latio
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r
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ase
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Fig
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ticu
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ated
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Fig
u
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6
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Simu
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r
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ase
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d
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c
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Fig
u
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7
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n
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7
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r
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ate
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ag
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ase
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ase
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ates
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ile
Fig
u
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8
(
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d
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T
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ates
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Fig
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,
a
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ates
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ase
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r
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f
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s
y
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te
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y
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e
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ti
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i
es
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e
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al
ty
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es o
f
f
a
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:
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-
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ase
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n
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as
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d
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,
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h
as
e
f
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u
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n
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–
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,
a
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d
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e
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e
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f
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r
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d
r
esp
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o
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3
6
,
2
2
,
1
5
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9
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3
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d
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I
SS
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2
2
5
2
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8
9
3
8
I
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I
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l.
14
,
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.
6
,
Dec
em
b
er
20
25
:
5
0
9
6
-
5
1
0
5
5102
o
f
th
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f
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f
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s
y
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tem
s
.
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h
e
tr
ain
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o
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o
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r
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s
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ec
r
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e
u
p
t
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f
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al
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,
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e
m
o
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atin
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ai
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h
e
v
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p
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ase
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v
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test
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th
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n
eu
r
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f
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el
with
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n
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ee
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icate
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ig
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0
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9
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.
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m
o
r
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e
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r
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o
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th
e
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e
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ar
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e
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icted
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Fig
u
r
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1
0
,
wh
er
e
Fig
u
r
es
1
0
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a
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to
10
(
c
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.
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h
e
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m
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ativ
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al
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ly
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ates
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r
i
d
m
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s
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y
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h
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its
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u
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r
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n
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ec
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e
r
f
o
r
m
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ce
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e
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e
o
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e
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t
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ch
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e
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0
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ep
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h
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r
th
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m
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e,
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d
el
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h
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its
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n
o
ta
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le
en
h
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ce
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en
t
in
a
cc
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r
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,
attr
i
b
u
ted
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ity
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o
p
tim
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o
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er
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s
.
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itio
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ally
,
T
ab
le
1
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a
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m
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ar
is
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with
tech
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iq
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e
s
u
s
ed
in
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atu
r
e.
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h
e
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ed
m
et
h
o
d
d
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n
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tr
ates
s
u
p
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io
r
p
r
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n
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n
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m
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ar
is
o
n
to
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ativ
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m
eth
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s
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wh
ile
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f
ec
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class
if
y
in
g
an
d
p
i
n
p
o
in
tin
g
all
f
au
lts
.
T
h
e
f
in
d
i
n
g
s
in
d
icate
th
at
th
e
s
u
g
g
este
d
m
eth
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d
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r
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th
e
o
n
es f
o
u
n
d
i
n
ex
is
tin
g
r
ese
ar
ch
.
(
a)
(
b
)
(
c)
Fig
u
r
e
1
0
.
Pre
cisi
o
n
-
r
ec
all
co
m
p
ar
is
o
n
(
a
)
ANFI
S,
(
b
)
L
ST
M,
an
d
(
c
)
p
r
o
p
o
s
ed
m
o
d
el
T
ab
le
1
.
C
o
m
p
a
r
is
o
n
with
liter
atu
r
e
Ref
M
e
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h
o
d
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s t
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d
?
I
s fau
l
t
l
o
c
a
t
e
d
?
P
r
e
c
i
s
i
o
n
[
2
0
]
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Evaluation Warning : The document was created with Spire.PDF for Python.
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lab
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f
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F
UNDING
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Au
th
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s
s
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v
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.
AUTHO
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is
jo
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Aut
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am
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DATA AV
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[
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NC
E
S
[
1
]
A
.
H
a
q
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e
,
A
.
K
a
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a
m,
a
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.
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Ed
.
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o
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R
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[
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]
T.
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.
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C
.
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.
El
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1
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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Dec
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5104
[
4
]
A
.
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a
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mj
o
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.
,
“
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y
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c
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i
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[
5
]
V
.
J.
F
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,
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.
T.
B
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.
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O
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[
6
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Y
.
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,
C
.
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.
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