I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
1
6
,
No
.
4
,
A
u
g
u
s
t
20
2
6
,
p
p
.
1
7
0
4
~
1
7
2
3
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
1
6
i
4
.
pp
1
7
0
4
-
1
7
2
3
1704
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
A deep
learning
-
d
riv
en t
ra
v
eling
w
a
v
e met
ho
d f
o
r
G
PS
-
f
ree
a
nd no
ise
-
resilien
t
fault
loca
tion in
co
mpens
a
ted
po
w
er
networks
Asm
a
T
a
lbi
,
Abdeha
f
id
B
a
y
a
di
A
u
t
o
ma
t
i
c
l
a
b
o
r
a
t
o
r
y
o
f
S
e
t
i
f
,
e
l
e
c
t
r
i
c
a
l
e
n
g
i
n
e
e
r
i
n
g
d
e
p
a
r
t
m
e
n
t
,
U
n
i
v
e
r
si
t
y
o
f
F
e
r
h
a
t
A
b
b
a
s Se
t
i
f
1
,
S
e
t
i
f
,
A
l
g
e
r
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
2
,
2
0
2
6
R
ev
is
ed
Ma
y
1
6
,
2
0
2
6
Acc
ep
ted
J
u
l 2
2
,
2
0
2
6
Th
is
p
a
p
e
r
in
tr
o
d
u
c
e
s
a
n
o
v
e
l
h
y
b
rid
fa
u
lt
l
o
c
a
ti
o
n
tec
h
n
i
q
u
e
fo
r
h
ig
h
-
v
o
lt
a
g
e
tran
sm
issio
n
li
n
e
s,
i
n
te
g
ra
ti
n
g
trav
e
ll
in
g
wa
v
e
(T
W)
p
rin
c
i
p
les
,
d
isc
re
te
wa
v
e
let
tran
sfo
rm
s
(D
WT
),
a
n
d
lo
n
g
s
h
o
rt
-
term
m
e
m
o
ry
(LS
TM
)
n
e
u
ra
l
n
e
two
rk
s.
T
h
e
p
r
o
p
o
se
d
m
e
th
o
d
e
n
h
a
n
c
e
s
fa
u
lt
d
e
tec
ti
o
n
s
p
e
e
d
,
imp
ro
v
e
s
l
o
c
a
ti
o
n
a
c
c
u
ra
c
y
,
a
n
d
d
e
m
o
n
stra
tes
re
sili
e
n
c
e
a
g
a
in
st
h
i
g
h
-
imp
e
d
a
n
c
e
fa
u
lt
s.
Th
e
LS
T
M
n
e
two
rk
is
sp
e
c
ifi
c
a
ll
y
train
e
d
to
d
e
tec
t
th
e
a
rriv
a
l
o
f
th
e
in
it
ial
wa
v
e
fro
n
t
t
h
ro
u
g
h
sin
g
le
-
e
n
d
e
d
m
e
a
su
re
m
e
n
ts,
wh
il
e
DWT
e
ffe
c
ti
v
e
l
y
e
x
trac
ts
th
e
h
ig
h
-
fre
q
u
e
n
c
y
c
o
m
p
o
n
e
n
ts
o
f
tran
sie
n
t
sig
n
a
ls.
A
sim
u
lati
o
n
o
f
a
4
0
0
k
V,
1
2
0
k
m
tran
sm
issio
n
li
n
e
,
m
o
d
e
led
o
n
re
a
l
p
a
ra
m
e
ter
s
fro
m
th
e
Alg
e
ria
n
g
rid
,
wa
s
c
o
n
d
u
c
ted
u
sin
g
ATP
-
EM
TP
.
Th
e
m
e
th
o
d
o
l
o
g
y
wa
s imp
lem
e
n
ted
in
M
ATLAB
a
n
d
c
o
m
p
a
re
d
wi
th
se
v
e
ra
l
sta
te
-
of
-
th
e
-
a
rt
a
p
p
r
o
a
c
h
e
s,
in
c
l
u
d
i
n
g
g
lo
b
a
l
p
o
siti
o
n
i
n
g
sy
ste
m
(
G
P
S
)
sy
n
c
h
r
o
n
ize
d
TW
m
e
th
o
d
s
,
u
n
d
e
r
v
a
rio
u
s
n
o
ise
c
o
n
d
i
ti
o
n
s
wit
h
sig
n
a
l
-
to
-
n
o
ise
ra
ti
o
s
(
S
NR)
a
s
l
o
w
a
s
5
d
B.
Ad
d
it
i
o
n
a
ll
y
,
th
e
in
fl
u
e
n
c
e
o
f
th
y
risto
r
-
c
o
n
tro
ll
e
d
se
ries
c
o
m
p
e
n
sa
to
rs
(
TCS
C)
o
n
lo
c
a
ti
o
n
a
c
c
u
ra
c
y
wa
s
e
x
p
l
o
re
d
.
Th
e
re
su
lt
s
c
o
n
firm
th
e
a
p
p
l
ica
b
il
it
y
o
f
t
h
e
p
ro
p
o
se
d
tec
h
n
iq
u
e
in
m
o
d
e
rn
wid
e
-
a
re
a
p
ro
tec
ti
o
n
sc
h
e
m
e
s,
e
sp
e
c
ially
fo
r
re
m
o
te
re
lay
s
a
n
d
n
e
x
t
-
g
e
n
e
ra
ti
o
n
d
i
g
it
a
l
fa
u
lt
re
c
o
r
d
e
rs (DF
Rs).
K
ey
w
o
r
d
s
:
Fau
lt lo
ca
tio
n
L
o
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
Neu
r
al
n
etwo
r
k
Neu
r
al
n
etwo
r
k
i
m
ag
e
p
r
o
ce
s
s
in
g
T
r
av
elin
g
wav
e
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Asma
T
alb
i
Au
to
m
atic
lab
o
r
ato
r
y
o
f
Setif
elec
tr
ical
en
g
in
ee
r
in
g
d
ep
a
r
tm
en
t,
Un
iv
er
s
ity
o
f
Fer
h
at
Ab
b
a
s
Setif
1
Setif,
Alg
er
E
m
ail: a
s
m
atalb
i@
u
n
iv
-
s
etif
.
d
z
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
r
eliab
ilit
y
o
f
p
o
wer
s
y
s
tem
s
is
o
f
p
ar
am
o
u
n
t
im
p
o
r
ta
n
ce
in
to
d
ay
'
s
en
er
g
y
lan
d
s
ca
p
e,
wh
er
e
u
n
in
ter
r
u
p
ted
s
er
v
ice
is
cr
itical
f
o
r
b
o
t
h
r
esid
en
tial
an
d
i
n
d
u
s
tr
ial
co
n
s
u
m
er
s
.
As
th
e
elec
tr
icity
d
em
an
d
co
n
tin
u
es
to
g
r
o
w,
th
e
ab
ilit
y
t
o
q
u
ick
ly
a
n
d
ac
cu
r
ately
id
en
t
if
y
an
d
lo
ca
te
f
au
lts
in
h
ig
h
-
v
o
ltag
e
tr
an
s
m
is
s
io
n
lin
es
b
ec
o
m
es
in
cr
ea
s
in
g
ly
ess
en
tial.
E
f
f
ec
tiv
e
f
au
lt
lo
ca
tio
n
m
in
im
izes
o
u
tag
e
tim
es
an
d
en
h
a
n
ce
s
th
e
o
v
er
all
s
tab
ilit
y
o
f
p
o
wer
n
et
wo
r
k
s
.
T
o
t
h
is
en
d
,
r
esear
ch
e
r
s
h
av
e
d
e
v
elo
p
e
d
a
r
a
n
g
e
o
f
m
eth
o
d
s
aim
e
d
at
im
p
r
o
v
in
g
f
a
u
lt d
etec
tio
n
an
d
lo
ca
lizatio
n
,
u
tili
zin
g
ad
v
an
ce
m
en
ts
in
tech
n
o
lo
g
y
a
n
d
s
ig
n
a
l p
r
o
ce
s
s
in
g
.
Fau
lt
lo
ca
tio
n
m
eth
o
d
s
th
at
u
s
e
tr
av
elin
g
wav
e
th
e
o
r
y
ar
e
c
h
ar
ac
ter
ized
b
y
th
eir
ab
ilit
y
to
ac
cu
r
ately
esti
m
ate
an
d
th
eir
e
n
h
an
ce
d
r
esis
tan
ce
to
v
ar
iatio
n
s
in
f
a
u
lt
r
esis
tan
ce
,
f
au
lt
ty
p
e
,
an
d
o
p
er
atin
g
co
n
d
itio
n
s
.
Alth
o
u
g
h
s
in
g
le
-
e
n
d
ed
m
eth
o
d
s
ar
e
m
o
r
e
ec
o
n
o
m
ical
[
1
]
,
th
ey
ten
d
to
b
e
less
ac
cu
r
ate
th
an
d
o
u
b
le
-
en
d
ed
ap
p
r
o
ac
h
es.
Glo
b
al
p
o
s
itio
n
in
g
s
y
s
tem
(
GPS)
s
y
n
ch
r
o
n
ized
d
o
u
b
le
-
e
n
d
ed
m
eth
o
d
s
o
f
f
er
h
ig
h
p
r
ec
is
io
n
[
2
]
,
b
u
t
im
p
lem
e
n
tin
g
th
em
co
m
es
with
s
ev
er
al
ch
allen
g
es.
Hig
h
-
ac
cu
r
ac
y
GPS
s
y
n
ch
r
o
n
izatio
n
s
y
s
tem
s
ar
e
ex
p
en
s
iv
e,
o
f
te
n
co
s
tin
g
th
o
u
s
an
d
s
to
ten
s
o
f
th
o
u
s
an
d
s
o
f
d
o
llar
s
p
er
u
n
it,
n
o
t
in
clu
d
i
n
g
in
s
tallatio
n
an
d
m
ain
ten
an
ce
co
s
ts
.
T
h
is
m
ak
es
lar
g
e
-
s
ca
le
d
ep
lo
y
m
en
t
d
if
f
icu
lt,
esp
ec
ially
in
wid
e
-
ar
ea
p
r
o
tectio
n
s
y
s
tem
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
d
ee
p
lea
r
n
in
g
-
d
r
iven
tr
a
ve
lin
g
w
a
ve
meth
o
d
f
o
r
…
(
A
s
ma
Ta
lb
i
)
1705
[
3
]
.
R
ec
en
t
ad
v
a
n
ce
m
en
ts
in
t
r
av
elin
g
-
wav
e
-
b
ased
f
au
lt
lo
c
atio
n
tech
n
iq
u
es
h
a
v
e
s
ig
n
if
ic
an
tly
im
p
r
o
v
ed
th
e
p
r
ec
is
io
n
an
d
s
p
ee
d
o
f
d
etec
ti
n
g
f
au
lts
in
h
ig
h
-
v
o
ltag
e
tr
a
n
s
m
is
s
io
n
lin
es.
T
r
ad
itio
n
al
m
et
h
o
d
s
r
ely
o
n
GPS
-
s
y
n
ch
r
o
n
ize
d
p
h
aso
r
m
ea
s
u
r
e
m
en
t
u
n
its
(
PMUs)
to
ac
h
iev
e
ac
cu
r
ate
tim
e
alig
n
m
en
t
b
et
wee
n
m
ea
s
u
r
em
en
t
p
o
in
ts
,
en
ab
lin
g
d
o
u
b
le
-
en
d
e
d
tr
av
elin
g
-
wav
e
f
au
lt
lo
ca
tio
n
[
4
]
.
Ho
wev
er
,
t
h
e
p
r
ac
tical
im
p
lem
en
tatio
n
o
f
GPS
-
d
ep
en
d
en
t
s
ch
em
es
f
ac
e
s
cr
itical
ch
allen
g
es.
GP
S
s
ig
n
als
m
ay
b
e
u
n
av
ailab
le
o
r
u
n
r
eliab
le
in
r
em
o
te
ar
ea
s
.
T
h
ey
ar
e
also
v
u
ln
e
r
ab
le
to
s
p
o
o
f
in
g
attac
k
s
.
F
u
r
th
er
m
o
r
e,
t
h
e
r
e
q
u
ir
em
e
n
t
f
o
r
s
y
n
ch
r
o
n
ized
m
ea
s
u
r
em
en
t
u
n
its
in
cr
ea
s
es
s
y
s
tem
co
m
p
lex
ity
an
d
c
o
s
t,
p
ar
ticu
lar
ly
in
wid
e
-
a
r
ea
p
r
o
t
ec
tio
n
s
y
s
tem
s
[
5
]
.
Ad
d
itio
n
ally
,
GPS
-
b
ased
s
o
l
u
tio
n
s
ar
e
n
o
t
alwa
y
s
r
eliab
l
e
u
n
d
e
r
r
ea
l
-
wo
r
ld
co
n
d
itio
n
s
.
Sig
n
als
m
ay
b
e
u
n
av
ailab
le
in
r
em
o
te
ar
ea
s
,
a
n
d
th
e
tech
n
o
lo
g
y
is
v
u
ln
e
r
ab
le
to
s
p
o
o
f
in
g
attac
k
s
t
h
at
ca
n
in
tr
o
d
u
ce
s
er
io
u
s
er
r
o
r
s
in
f
a
u
lt
lo
ca
tio
n
.
T
h
es
e
lim
itatio
n
s
u
n
d
er
s
co
r
e
th
e
n
ec
ess
ity
f
o
r
f
au
lt
lo
ca
tio
n
m
eth
o
d
s
th
at
d
o
n
o
t
d
ep
en
d
o
n
GPS
wh
ile
s
till
en
s
u
r
in
g
h
ig
h
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
.
R
ec
en
t
ad
v
an
ce
m
e
n
ts
in
tr
a
v
elin
g
-
wav
e
-
b
ased
f
au
lt lo
ca
tio
n
tec
h
n
iq
u
e
s
h
av
e
s
ig
n
if
ican
tly
im
p
r
o
v
e
d
th
e
ac
cu
r
ac
y
a
n
d
r
a
p
id
ity
o
f
f
a
u
lt id
en
tific
atio
n
in
h
ig
h
-
v
o
ltag
e
tr
an
s
m
is
s
io
n
n
et
wo
r
k
s
[
6
]
.
An
o
th
er
im
p
o
r
tan
t
ch
allen
g
e
is
th
e
p
r
esen
ce
o
f
s
er
ie
s
co
m
p
en
s
atio
n
d
ev
ices,
s
u
ch
as
th
y
r
is
to
r
-
co
n
tr
o
lled
s
er
ies
ca
p
ac
ito
r
s
(
T
C
SC
)
,
wh
ich
d
is
to
r
t
tr
av
elli
n
g
wav
e
s
ig
n
als.
R
esear
ch
co
n
f
ir
m
s
th
at
v
ar
y
in
g
co
m
p
en
s
atio
n
r
atio
s
s
ig
n
if
ican
tly
im
p
ac
t
p
r
o
p
a
g
atio
n
ch
ar
ac
ter
is
tics
,
n
ec
es
s
itatin
g
ad
v
an
ce
d
alg
o
r
ith
m
s
to
m
ain
tain
ac
cu
r
ac
y
in
co
m
p
en
s
ated
co
n
d
itio
n
s
[
7
]
.
T
h
e
p
ap
er
b
y
[
8
]
p
r
o
p
o
s
es
an
in
n
o
v
ati
v
e
m
eth
o
d
f
o
r
f
au
lt
lo
ca
tio
n
o
n
th
r
ee
-
ter
m
in
al
tr
a
n
s
m
is
s
io
n
lin
es
with
s
er
ies
co
m
p
en
s
atio
n
u
s
in
g
d
ee
p
n
eu
r
al
n
etwo
r
k
s
(
DNNs)
.
T
h
is
ap
p
r
o
ac
h
u
s
es
cu
r
r
e
n
t
a
n
d
v
o
ltag
e
m
ea
s
u
r
em
en
ts
f
r
o
m
all
th
r
ee
ter
m
in
als
to
ac
cu
r
ately
esti
m
ate
f
au
lt
lo
ca
tio
n
s
,
ev
en
in
th
e
p
r
esen
c
e
o
f
n
o
n
lin
ea
r
ities
in
tr
o
d
u
ce
d
b
y
s
er
ies
co
m
p
e
n
s
atio
n
.
T
h
e
r
esu
lts
d
em
o
n
s
tr
ate
h
ig
h
r
o
b
u
s
tn
ess
an
d
ac
cu
r
ac
y
,
o
u
tp
er
f
o
r
m
in
g
tr
a
d
itio
n
al
m
et
h
o
d
s
,
p
ar
ticu
lar
ly
u
n
d
er
c
o
m
p
lex
co
n
d
itio
n
s
s
u
ch
as
v
ar
iab
le
f
a
u
lt
r
esis
tan
ce
s
an
d
d
i
v
er
s
e
f
au
lt
t
y
p
es.
T
h
at
is
wh
y
,
i
n
r
ec
e
n
t
y
ea
r
s
,
f
au
lt
lo
ca
lizatio
n
h
as
b
ee
n
g
r
ea
tly
im
p
r
o
v
e
d
th
r
o
u
g
h
t
h
e
u
s
e
o
f
v
ar
io
u
s
in
tellig
en
t
tech
n
iq
u
es,
s
u
ch
as
ar
tific
ial
n
e
u
r
a
l
n
etwo
r
k
s
(
ANNs)
,
as
p
r
o
p
o
s
ed
in
r
ef
er
e
n
ce
[
9
]
.
An
in
n
o
v
ativ
e
ap
p
r
o
ac
h
is
p
r
o
p
o
s
ed
f
o
r
f
au
lt
d
etec
tio
n
,
class
if
icatio
n
,
an
d
d
is
tan
ce
esti
m
atio
n
o
n
f
ix
ed
s
er
ies
co
m
p
en
s
atio
n
(
FS
C
)
h
i
g
h
-
v
o
ltag
e
tr
an
s
m
is
s
io
n
lin
es
u
s
in
g
m
etal
o
x
id
e
v
ar
is
to
r
(
MO
V)
en
er
g
y
as
in
p
u
t
to
an
ANN.
T
h
is
m
eth
o
d
p
r
o
v
id
es
h
ig
h
ac
cu
r
ac
y
an
d
in
cr
ea
s
ed
r
o
b
u
s
tn
ess
.
I
t
also
s
im
p
lifie
s
co
m
p
u
tatio
n
al
co
m
p
lex
ity
b
y
u
s
in
g
o
n
e
-
way
d
ata.
ANNs
o
f
f
er
p
o
wer
f
u
l
c
ap
ab
ilit
ies
f
o
r
f
au
lt
lo
ca
tio
n
.
Ho
wev
er
,
th
e
y
s
u
f
f
er
f
r
o
m
lim
itatio
n
s
r
elate
d
to
d
ata
ac
q
u
is
itio
n
an
d
q
u
ality
,
m
o
d
el
co
m
p
lex
ity
,
an
d
th
e
n
ee
d
f
o
r
f
r
eq
u
en
t
u
p
d
at
es
[
9
]
,
[
1
0
]
.
A
h
y
b
r
id
ap
p
r
o
ac
h
co
m
b
i
n
in
g
ANN
with
tr
ad
itio
n
al
m
eth
o
d
s
(
tr
av
ellin
g
wav
es,
an
d
im
p
e
d
an
ce
.
)
h
as
b
ee
n
p
r
o
p
o
s
ed
in
t
h
e
liter
atu
r
e
[
1
1
]
,
[
1
2
]
.
Alth
o
u
g
h
s
o
m
e
p
r
ev
i
o
u
s
s
tu
d
ies h
av
e
attem
p
ted
to
u
s
e
t
r
ad
itio
n
al
ANNs to
an
aly
ze
tr
av
elin
g
wav
e
s
ig
n
als f
o
r
f
au
lt lo
ca
tio
n
[
1
2
]
,
th
ese
m
o
d
els
h
av
e
lim
itatio
n
s
in
h
a
n
d
lin
g
c
o
m
p
lex
tem
p
o
r
al
d
ata
.
ANNs
lack
th
e
"m
em
o
r
y
"
m
ec
h
an
is
m
to
ca
p
tu
r
e
lo
n
g
tem
p
o
r
al
d
ep
e
n
d
en
cies,
m
ak
in
g
th
em
less
ef
f
ec
tiv
e
at
an
aly
zin
g
elec
tr
ical
s
ig
n
als
th
at
co
n
tain
im
p
o
r
ta
n
t
tem
p
o
r
al
in
f
o
r
m
atio
n
.
L
o
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
ST
M)
n
eu
r
al
n
etwo
r
k
s
ar
e
ca
p
ab
le
o
f
ef
f
icien
tly
p
r
o
ce
s
s
in
g
tem
p
o
r
al
s
eq
u
en
ce
s
b
ec
au
s
e
th
ey
ar
e
s
p
ec
if
ically
d
esig
n
e
d
to
h
a
n
d
le
l
o
n
g
an
d
co
m
p
lex
tem
p
o
r
a
l
d
ep
en
d
e
n
cies.
Stu
d
ies
h
av
e
s
h
o
wn
th
at
L
STM
s
o
u
t
p
er
f
o
r
m
ANNs
in
tim
e
s
er
ies
p
r
ed
ictio
n
task
s
,
m
ak
in
g
th
em
a
b
etter
ch
o
ice
f
o
r
an
aly
zin
g
tr
av
elin
g
wav
e
s
ig
n
als an
d
lo
ca
tin
g
f
a
u
lts
with
g
r
ea
ter
a
cc
u
r
ac
y
[
1
3
]
.
Fu
r
th
er
m
o
r
e
,
co
n
v
en
tio
n
al
f
a
u
lt
lo
ca
tio
n
m
eth
o
d
s
o
f
ten
d
e
g
r
ad
e
in
p
er
f
o
r
m
an
ce
i
n
th
e
p
r
esen
ce
o
f
s
er
ies
co
m
p
en
s
atio
n
d
e
v
ices,
s
u
ch
as
T
C
SC
s
,
an
d
u
n
d
e
r
l
o
w
s
ig
n
al
-
to
-
n
o
is
e
r
atio
(
SN
R
)
co
n
d
itio
n
s
.
T
h
is
lim
its
th
eir
r
o
b
u
s
tn
ess
in
r
ea
l
-
wo
r
ld
tr
an
s
m
is
s
io
n
n
etwo
r
k
s
.
Acc
u
r
ate
f
au
lt
d
etec
tio
n
in
s
u
ch
en
v
ir
o
n
m
en
ts
is
cr
u
cial
to
en
s
u
r
in
g
th
e
r
eliab
i
lity
o
f
d
is
tan
ce
r
elay
s
,
o
v
er
cu
r
r
en
t
r
elay
s
,
an
d
d
if
f
e
r
en
tial
p
r
o
tectio
n
s
ch
em
es
an
d
p
r
e
v
en
tin
g
m
is
o
p
er
atio
n
o
r
d
elay
s
in
f
au
lt c
lear
an
ce
t
h
a
t c
o
u
ld
co
m
p
r
o
m
is
e
s
y
s
tem
s
t
ab
ilit
y
[
1
4
]
.
Ho
wev
er
,
ex
is
tin
g
f
au
lt
lo
ca
t
io
n
m
eth
o
d
s
ex
h
ib
it
s
ig
n
if
ica
n
t
p
er
f
o
r
m
an
ce
d
e
g
r
ad
atio
n
u
n
d
er
lo
w
SNR
co
n
d
itio
n
s
an
d
in
th
e
p
r
esen
ce
o
f
s
er
ies
co
m
p
e
n
s
atio
n
d
ev
ices
s
u
ch
as
T
C
SC
s
.
Mo
r
eo
v
e
r
,
m
o
s
t
h
i
g
h
-
ac
cu
r
ac
y
a
p
p
r
o
ac
h
es
r
ely
o
n
GPS
s
y
n
ch
r
o
n
izatio
n
,
wh
ic
h
lim
its
th
eir
p
r
ac
ticality
,
i
n
cr
ea
s
es
co
s
t,
an
d
in
tr
o
d
u
ce
s
v
u
l
n
er
ab
ilit
y
to
s
ig
n
al
lo
s
s
o
r
s
p
o
o
f
in
g
.
T
h
er
e
f
o
r
e,
th
er
e
is
a
clea
r
n
ee
d
f
o
r
a
r
o
b
u
s
t,
GPS
-
in
d
ep
en
d
en
t
f
au
lt
lo
ca
tio
n
m
eth
o
d
ca
p
a
b
le
o
f
m
ain
tain
i
n
g
h
ig
h
ac
c
u
r
ac
y
u
n
d
er
n
o
is
y
an
d
co
m
p
en
s
ated
n
etwo
r
k
c
o
n
d
itio
n
s
.
T
o
clea
r
l
y
h
ig
h
li
g
h
t
th
e
lim
itatio
n
s
o
f
ex
is
tin
g
ap
p
r
o
ac
h
es
a
n
d
p
o
s
itio
n
th
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
with
in
th
e
cu
r
r
e
n
t
s
tate
o
f
th
e
a
r
t,
a
s
tr
u
ct
u
r
ed
co
m
p
ar
is
o
n
o
f
r
ec
en
t
f
a
u
lt
l
o
ca
tio
n
m
eth
o
d
s
is
p
r
esen
ted
in
T
a
b
le
1
.
As
s
h
o
wn
in
T
ab
le
1
,
r
ec
en
t
ap
p
r
o
ac
h
es
eith
er
r
ely
o
n
c
o
m
p
lex
m
u
lti
-
ter
m
in
al
s
y
n
ch
r
o
n
izatio
n
,
s
u
f
f
er
f
r
o
m
lim
ited
r
o
b
u
s
tn
ess
u
n
d
er
n
o
is
y
co
n
d
itio
n
s
,
o
r
lack
th
e
ab
ilit
y
to
ef
f
ec
tiv
ely
ca
p
tu
r
e
tem
p
o
r
a
l
d
y
n
am
ics.
I
n
co
n
t
r
ast,
th
e
p
r
o
p
o
s
ed
T
W
+
L
STM
f
r
am
ewo
r
k
p
r
o
v
id
es a
b
alan
ce
d
s
o
lu
tio
n
b
y
co
m
b
in
in
g
h
ig
h
ac
cu
r
ac
y
,
s
tr
o
n
g
r
o
b
u
s
tn
ess
,
a
n
d
p
r
ac
tical
d
e
p
lo
y
m
en
t f
ea
s
i
b
ilit
y
with
o
u
t r
eq
u
ir
in
g
GPS s
y
n
ch
r
o
n
izatio
n
.
T
h
is
s
tu
d
y
p
r
esen
ts
a
n
o
v
el,
GPS
-
f
r
ee
,
d
ep
lo
y
a
b
le
h
y
b
r
id
f
au
lt
lo
ca
tio
n
f
r
a
m
ewo
r
k
f
o
r
h
i
g
h
-
v
o
ltag
e
tr
an
s
m
is
s
io
n
lin
es.
T
h
is
f
r
am
ewo
r
k
in
teg
r
ates
tr
av
elin
g
wav
e
(
T
W
)
th
eo
r
y
an
d
a
d
ee
p
lear
n
in
g
–
b
ased
L
STM
n
eu
r
al
n
etwo
r
k
.
U
n
lik
e
co
n
v
e
n
tio
n
al
T
W
an
d
m
ac
h
in
e
lear
n
in
g
-
b
ased
a
p
p
r
o
ac
h
es,
wh
ich
r
eq
u
ir
e
p
r
ec
is
e
tim
e
s
y
n
ch
r
o
n
izatio
n
o
r
i
d
ea
lized
s
ig
n
al
co
n
d
itio
n
s
,
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
ca
n
ac
c
u
r
ately
id
en
tify
th
e
a
r
r
iv
al
tim
es
o
f
th
e
f
ir
s
t
tr
an
s
ien
t
wav
e
with
o
u
t
GPS.
I
t
m
ain
tain
s
h
ig
h
p
er
f
o
r
m
an
ce
u
n
d
er
lo
w
SNR
an
d
co
m
p
en
s
ated
n
etwo
r
k
co
n
d
iti
o
n
s
.
C
o
m
b
in
in
g
d
ata
-
d
r
iv
en
t
em
p
o
r
al
f
ea
tu
r
e
ex
tr
ac
tio
n
with
th
e
p
r
in
cip
les
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
4
,
Au
g
u
s
t
20
2
6
:
1
7
0
4
-
1723
1706
p
h
y
s
ical
s
ig
n
al
p
r
o
p
a
g
atio
n
e
n
h
an
ce
s
th
e
m
eth
o
d
'
s
r
o
b
u
s
tn
ess
ag
ain
s
t
n
o
is
e
an
d
s
tr
en
g
th
en
s
th
e
o
p
er
atio
n
al
r
eliab
ilit
y
o
f
p
r
o
tectiv
e
r
elay
s
(
in
clu
d
in
g
d
is
tan
ce
,
o
v
er
cu
r
r
en
t,
an
d
d
if
f
e
r
en
tial
s
ch
em
es).
T
h
is
en
s
u
r
es
th
e
f
r
am
ewo
r
k
'
s
p
r
ac
tical
ap
p
lica
b
ilit
y
an
d
d
e
p
lo
y
m
e
n
t f
ea
s
ib
ilit
y
in
r
ea
l
-
wo
r
l
d
h
ig
h
-
v
o
ltag
e
n
etwo
r
k
s
.
T
ab
le
1
.
C
o
m
p
a
r
is
o
n
with
r
ec
en
t state
-
of
-
th
e
-
ar
t
f
au
lt lo
ca
ti
o
n
m
eth
o
d
s
T
AB
L
E
I
.
S
tudy/
M
e
t
hod
T
AB
L
E
I
I
.
T
e
c
hnique
T
AB
L
E
I
I
I
.
GPS
R
e
quir
e
ment
T
AB
L
E
I
V
.
Nois
e
R
obus
tnes
s
T
AB
L
E
V
.
C
ompens
a
ti
on
Ha
ndli
ng
T
AB
L
E
VI
.
S
tr
e
ng
ths
T
AB
L
E
VI
I
.
L
im
i
tations
T
AB
L
E
VI
I
I
.
De
ploym
e
nt
F
e
a
s
ibi
li
ty
T
AB
L
E
I
X
.
L
i
e
t
al.
2026
[
15]
T
AB
L
E
X
.
T
W
+
R
e
dunda
nt
I
nf
o
r
matio
n
T
AB
L
E
XI
.
R
e
quir
e
d
(
mu
lt
i
-
ter
mi
na
l
s
ync
hr
oniza
ti
o
n)
T
AB
L
E
XI
I
.
High
T
AB
L
E
XI
I
I
.
Not
a
ddr
e
s
s
e
d
T
AB
L
E
XI
V
.
Ac
c
ur
a
te
f
or
c
omp
lex
DC
ne
twor
ks
;
r
e
dun
da
nc
y
im
pr
o
ve
s
r
e
li
a
bil
it
y
T
AB
L
E
XV
.
R
e
quir
e
s
mul
ti
p
le
s
ync
hr
onize
d
mea
s
ur
e
ments
;
c
ompl
e
x
inf
r
a
s
tr
uc
tu
r
e
T
AB
L
E
XV
I
.
L
ow
T
AB
L
E
XV
I
I
.
Dhole
2024
[
16
]
T
AB
L
E
XV
I
I
.
DNN
T
AB
L
E
XI
X.
Not
r
e
quir
e
d
T
AB
L
E
XX
.
M
ode
r
a
te
T
AB
L
E
XX
I
.
L
im
i
ted
T
AB
L
E
XX
I
I
.
G
oo
d
lea
r
ning
c
a
pa
bil
it
y;
s
uit
a
ble
f
or
nonl
inea
r
s
ys
tems
T
AB
L
E
XX
I
I
I
.
W
e
a
k
tempor
a
l
modeling
;
r
e
quir
e
s
lar
ge
da
tas
e
t
T
AB
L
E
XX
I
V.
M
ode
r
a
te
T
AB
L
E
XX
V
.
R
a
ha
ngda
le
a
nd
G
up
ta,
2024
[
17]
T
AB
L
E
XXVI
.
DW
T
+
E
R
T
T
AB
L
E
XX
VI
I
.
Not
r
e
quir
e
d
T
AB
L
E
XX
VI
I
I
.
S
e
ns
it
ive
to
nois
e
T
AB
L
E
XX
I
X
.
Not
a
ddr
e
s
s
e
d
T
AB
L
E
XX
X
.
G
oo
d
a
c
c
ur
a
c
y
unde
r
idea
l
c
ondit
ions
;
s
im
ple
im
pleme
n
tation
T
AB
L
E
XX
XI
.
P
e
r
f
o
r
manc
e
de
gr
a
de
s
unde
r
nois
e
;
li
mi
ted
a
da
ptabili
ty
T
AB
L
E
XX
XI
I
.
M
ode
r
a
te
T
AB
L
E
XX
XI
I
I
.
T
his
W
or
k
(
T
W
+
L
S
T
M
)
T
AB
L
E
XX
XI
V
.
Hybr
id
T
W
+
L
S
T
M
(
G
P
S
-
f
r
e
e
)
T
AB
L
E
XX
XV
.
Not
r
e
quir
e
d
T
AB
L
E
XX
XV
I
.
High
(
r
ob
us
t
a
t
low
S
NR
)
T
AB
L
E
XX
XV
I
I
.
E
f
f
e
c
t
ive
(
tes
ted
with
T
C
S
C
)
T
AB
L
E
XX
XV
I
I
I
.
Ac
c
ur
a
te,
nois
e
-
r
e
s
il
ient,
ha
ndles
tempor
a
l
dyna
mi
c
s
;
va
li
da
ted
on
r
e
a
li
s
ti
c
s
ys
tem
T
AB
L
E
XX
XI
X.
R
e
quir
e
s
tr
a
ini
ng
da
ta;
model
tr
a
in
ing
ne
e
de
d
T
AB
L
E
XL
.
High
(
c
os
t
-
e
f
f
e
c
ti
ve
&
de
ployable
)
T
h
e
k
ey
c
o
n
tr
ib
u
tio
n
s
o
f
th
is
wo
r
k
ar
e
s
u
m
m
ar
ized
as f
o
llo
ws:
−
E
n
h
an
ce
d
d
ep
lo
y
m
en
t
f
ea
s
ib
ilit
y
th
r
o
u
g
h
GPS
-
f
r
ee
o
p
er
atio
n
;
Un
lik
e
co
n
v
en
tio
n
al
t
r
av
eli
n
g
-
wav
e
-
b
ased
m
eth
o
d
s
th
at
r
ely
o
n
p
r
ec
is
e
tim
e
s
y
n
ch
r
o
n
izatio
n
,
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
elim
in
ates
th
e
n
ee
d
f
o
r
GPS
s
ig
n
als
wh
ile
p
r
eser
v
in
g
h
ig
h
lo
ca
lizatio
n
ac
cu
r
ac
y
.
T
h
is
s
ig
n
if
ican
tly
im
p
r
o
v
es
th
e
m
eth
o
d
'
s
d
ep
lo
y
m
en
t
f
ea
s
ib
ilit
y
,
esp
ec
ially
in
r
em
o
t
e
o
r
r
eso
u
r
ce
-
co
n
s
tr
ain
ed
p
o
wer
s
y
s
tem
s
.
−
Hig
h
r
o
b
u
s
tn
ess
u
n
d
er
n
o
is
y
an
d
lo
w
SNR
co
n
d
itio
n
s
.
T
h
e
p
r
o
p
o
s
ed
f
r
a
m
ewo
r
k
is
r
o
b
u
s
t
ag
ain
s
t
n
o
is
e
an
d
elec
tr
o
m
ag
n
etic
i
n
ter
f
er
e
n
ce
b
ec
au
s
e
it
ef
f
ec
tiv
ely
lear
n
s
th
e
tem
p
o
r
al
f
ea
t
u
r
es
o
f
tr
a
n
s
ien
t
s
ig
n
als.
I
t
m
ain
tain
s
ac
cu
r
ate
p
e
r
f
o
r
m
a
n
ce
ev
en
u
n
d
er
s
ev
er
e
n
o
is
e
lev
els
(
as
lo
w
as
5
d
B
)
,
o
v
er
co
m
i
n
g
th
e
lim
itatio
n
s
o
f
tr
ad
itio
n
al
s
ig
n
a
l p
r
o
ce
s
s
in
g
tech
n
iq
u
es.
−
Stro
n
g
p
r
ac
tical
ap
p
licab
ilit
y
i
n
m
o
d
e
r
n
co
m
p
en
s
ated
n
etwo
r
k
s
.
T
h
e
m
eth
o
d
r
e
m
ain
s
ef
f
ec
tiv
e
in
co
m
p
lex
tr
an
s
m
is
s
io
n
s
y
s
tem
s
,
in
clu
d
in
g
t
h
o
s
e
with
s
er
ies
co
m
p
en
s
atio
n
d
ev
ices,
s
u
ch
a
s
T
C
SC
s
.
T
h
is
d
em
o
n
s
tr
ates
its
ef
f
ec
tiv
e
n
ess
in
r
ea
l
-
wo
r
ld
g
r
id
co
n
f
ig
u
r
atio
n
s
,
wh
er
e
co
n
v
en
tio
n
al
m
eth
o
d
s
o
f
ten
ex
p
er
ien
ce
p
er
f
o
r
m
an
ce
d
eg
r
a
d
atio
n
.
−
Valid
atio
n
o
n
a
r
ea
lis
tic,
lar
g
e
-
s
ca
le
p
o
wer
s
y
s
tem
m
o
d
el:
T
h
e
p
r
o
p
o
s
ed
a
p
p
r
o
ac
h
was
v
alid
ated
u
s
in
g
a
4
0
0
k
V,
1
2
0
k
m
tr
a
n
s
m
is
s
io
n
lin
e
m
o
d
el
im
p
lem
en
te
d
in
AT
P
-
E
MT
P.
T
h
is
m
o
d
el
in
c
o
r
p
o
r
ates
d
iv
e
r
s
e
f
au
lt
s
ce
n
ar
io
s
an
d
p
r
ac
tical
o
p
er
atin
g
c
o
n
d
itio
n
s
.
R
ely
in
g
o
n
r
ea
lis
tic
s
y
s
tem
p
ar
am
eter
s
en
s
u
r
es
th
at
th
e
s
im
u
latio
n
en
v
ir
o
n
m
en
t
clo
s
e
ly
em
u
lates
r
ea
l
-
wo
r
ld
p
o
we
r
n
etwo
r
k
s
.
T
h
is
s
ig
n
if
ican
tly
s
tr
en
g
th
en
s
th
e
r
eliab
ilit
y
,
s
ca
lab
ilit
y
,
an
d
p
r
ac
tical
ap
p
licab
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
s
u
p
p
o
r
tin
g
it
s
s
u
itab
ilit
y
f
o
r
r
ea
l
-
wo
r
ld
d
e
p
lo
y
m
e
n
t.
T
h
e
r
em
ai
n
d
er
o
f
th
is
p
a
p
er
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
Se
ctio
n
2
o
u
tlin
es
t
h
e
r
elev
a
n
t
th
eo
r
etica
l
b
ac
k
g
r
o
u
n
d
to
th
e
s
tu
d
y
.
Sec
tio
n
3
d
etails
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
an
d
d
escr
ib
es
th
e
s
im
u
latio
n
s
etu
p
.
Sectio
n
4
p
r
esen
ts
an
d
d
is
cu
s
s
es th
e
r
esu
lts
f
o
r
h
ig
h
-
v
o
ltag
e
tr
an
s
m
is
s
io
n
n
etwo
r
k
s
,
with
a
p
ar
ticu
lar
f
o
c
u
s
o
n
th
e
ac
cu
r
ac
y
an
d
r
o
b
u
s
tn
ess
o
f
f
au
lt
lo
ca
tio
n
u
n
d
e
r
v
a
r
io
u
s
n
o
is
e
co
n
d
itio
n
s
.
Sectio
n
5
co
m
p
a
r
es
th
e
p
r
o
p
o
s
ed
m
eth
o
d
with
o
th
e
r
s
tate
-
of
-
th
e
-
a
r
t a
p
p
r
o
ac
h
es.
Fin
ally
,
s
ec
tio
n
6
co
n
clu
d
es th
e
p
ap
er
.
2.
T
H
E
O
RE
T
I
CAL
B
ACK
G
RO
UND
2
.
1
.
T
ra
v
elling
wa
v
e
t
heo
ry
A
d
is
tu
r
b
an
ce
r
ef
er
s
to
an
y
e
v
en
t
th
at
alter
s
t
h
e
n
o
r
m
al
o
p
er
atin
g
co
n
d
itio
n
s
o
f
a
tr
an
s
m
is
s
io
n
lin
e,
in
clu
d
in
g
s
witch
in
g
o
p
er
atio
n
s
o
r
f
a
u
lt
o
cc
u
r
r
en
ce
s
s
u
c
h
as
s
h
o
r
t
cir
c
u
its
.
T
h
e
r
esu
ltin
g
t
r
an
s
ien
t
p
r
o
p
ag
ates
alo
n
g
th
e
lin
e
in
b
o
th
d
ir
ec
ti
o
n
s
with
a
v
elo
city
clo
s
e
to
th
e
s
p
ee
d
o
f
lig
h
t.
T
r
an
s
m
is
s
io
n
lin
e
co
n
d
u
cto
r
s
in
h
er
en
tly
p
o
s
s
ess
r
esis
tan
ce
an
d
in
d
u
ctan
ce
p
ar
a
m
eter
s
d
i
s
tr
ib
u
ted
co
n
tin
u
o
u
s
ly
o
v
e
r
th
e
en
tire
lin
e
len
g
t
h
.
As s
h
o
wn
in
Fig
u
r
e
1
,
a
d
if
f
er
en
tial sectio
n
o
f
th
e
tr
an
s
m
is
s
i
o
n
lin
e
m
ay
b
e
m
o
d
ele
d
as a
q
u
ad
r
u
p
o
le
n
etwo
r
k
[
1
8
]
,
[
1
9
]
wh
er
e
Z
is
th
e
s
er
ies im
p
ed
an
ce
a
n
d
s
h
u
n
t a
d
m
itta
n
ce
Y
d
ef
in
ed
p
er
u
n
it len
g
t
h
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
d
ee
p
lea
r
n
in
g
-
d
r
iven
tr
a
ve
lin
g
w
a
ve
meth
o
d
f
o
r
…
(
A
s
ma
Ta
lb
i
)
1707
x
L
x
R
x
G
x
C
x
x
+
x
x
I
(
x
+
x
,
t
)
V
(
x
+
x
,
t
)
I
(
x
,
t
)
V
(
x
,
t
)
Fig
u
r
e
1
.
Diag
r
a
m
o
f
b
asic tr
a
n
s
m
is
s
io
n
lin
e
co
m
p
o
n
e
n
ts
T
h
e
teleg
r
a
p
h
e
q
u
atio
n
f
u
lly
d
escr
ib
es
th
e
in
ter
ac
tio
n
b
etw
ee
n
v
o
ltag
e,
cu
r
r
en
t,
d
is
tan
ce
,
an
d
tim
e
in
tr
an
s
m
is
s
io
n
lin
es
m
o
d
eled
with
d
is
tr
ib
u
ted
p
ar
am
eter
s
.
I
t
r
ev
er
ts
to
th
e
well
-
k
n
o
wn
w
av
e
eq
u
atio
n
wh
en
lo
s
s
es a
r
e
n
eg
lig
ib
le
[
2
0
]
.
{
2
(
,
)
2
=
⋅
2
(
,
)
2
2
(
,
)
2
=
⋅
2
(
,
)
2
(
1
)
W
e
o
b
tain
a
class
ic
wav
e
eq
u
a
tio
n
o
f
th
e
f
o
r
m
2
2
=
1
2
⋅
2
2
(
2
)
W
ith
=
or
,
an
d
=
1
√
: w
av
e
p
r
o
p
ag
atio
n
s
p
ee
d
(
m
/s
)
T
h
e
g
en
er
al
s
o
lu
tio
n
is
th
e
s
u
p
er
p
o
s
itio
n
o
f
two
wav
es,
s
o
th
at
th
e
v
o
ltag
e
s
ig
n
al
ca
n
b
e
ex
p
r
ess
ed
as
th
e
s
u
m
o
f
a
n
in
cid
e
n
t tr
av
elli
n
g
wav
e
an
d
a
r
e
f
lecte
d
tr
av
el
lin
g
wav
e.
(
,
)
=
1
(
−
/
)
+
2
(
+
/
)
(
3
)
1
: is th
e
in
cid
en
t w
av
e,
a
n
d
2
: is a
r
ef
lecte
d
wav
e
I
n
d
is
tr
ib
u
te
d
tr
a
n
s
m
is
s
io
n
lin
e
m
o
d
els,
t
h
e
c
h
ar
ac
ter
is
tic
im
p
ed
an
ce
d
ep
e
n
d
s
o
n
t
h
e
lin
e
in
d
u
cta
n
ce
L
an
d
ca
p
ac
itan
ce
C
p
er
u
n
it
len
g
th
.
Un
d
e
r
th
r
ee
-
p
h
ase
o
p
e
r
atin
g
co
n
d
itio
n
s
,
th
e
v
o
ltag
e
an
d
c
u
r
r
en
t
wav
ef
o
r
m
s
p
r
o
p
ag
atin
g
th
r
o
u
g
h
th
e
n
etwo
r
k
ar
e
m
u
tu
ally
co
u
p
led
b
ec
a
u
s
e
o
f
th
e
m
u
tu
al
in
d
u
ctan
ce
s
an
d
ca
p
ac
itan
ce
s
b
etwe
en
p
h
ases
.
As
a
r
esu
lt,
th
e
s
y
s
tem
o
f
eq
u
atio
n
s
g
o
v
e
r
n
in
g
th
e
b
eh
a
v
io
r
o
f
th
e
lin
e
b
ec
o
m
es
v
ec
to
r
ial,
with
d
ep
en
d
en
cies
ac
r
o
s
s
all
p
h
ases
.
T
o
s
im
p
lify
th
e
an
aly
s
is
an
d
is
o
late
in
d
iv
id
u
al
p
r
o
p
ag
atio
n
p
ath
s
,
a
m
o
d
al
tr
a
n
s
f
o
r
m
atio
n
m
atr
ix
is
ap
p
lied
.
T
h
is
p
r
o
ce
s
s
d
ec
o
u
p
les th
e
p
h
ase
d
o
m
ain
q
u
an
titi
es in
to
a
s
et
o
f
in
d
e
p
en
d
e
n
t m
o
d
al
e
q
u
atio
n
s
,
ea
ch
co
r
r
esp
o
n
d
i
n
g
to
a
d
if
f
er
en
t p
r
o
p
ag
atio
n
m
o
d
e
[
2
0
]
.
{
(
,
)
=
(
,
)
(
,
)
=
(
,
)
(
4
)
T
h
r
ee
-
p
h
ase
v
o
ltag
e
(
cu
r
r
e
n
t)
s
ig
n
als in
to
th
eir
m
o
d
al
co
m
p
o
n
en
ts
:
{
0
,
=
−
1
×
,
,
0
,
=
−
1
×
,
,
(
5
)
I
n
th
e
ca
s
e
o
f
b
alan
ce
d
,
f
u
lly
tr
an
s
p
o
s
ed
t
h
r
ee
-
p
h
ase
tr
an
s
m
is
s
io
n
lin
es,
th
e
m
o
d
al
tr
an
s
f
o
r
m
atio
n
m
atr
ices
an
d
th
ey
a
r
e
id
e
n
tical
an
d
co
n
s
is
t
o
f
d
is
tin
ct,
r
ea
l
-
v
alu
ed
elem
e
n
ts
.
T
h
is
p
r
o
p
e
r
t
y
is
co
n
s
is
ten
t
with
th
e
Kar
r
e
n
b
au
e
r
tr
a
n
s
f
o
r
m
atio
n
[
2
1
]
,
a
wid
ely
r
e
co
g
n
ized
m
eth
o
d
i
n
elec
tr
ic
al
en
g
in
ee
r
in
g
f
o
r
d
ec
o
u
p
lin
g
m
u
lti
-
p
h
ase
s
y
s
tem
s
.
I
n
th
e
co
n
tex
t
o
f
th
is
s
tu
d
y
,
wh
ich
is
b
ased
o
n
tr
av
elin
g
wav
e
th
eo
r
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
4
,
Au
g
u
s
t
20
2
6
:
1
7
0
4
-
1723
1708
co
m
b
in
ed
with
th
e
d
is
cr
ete
wav
elet
tr
an
s
f
o
r
m
(
DW
T
)
,
th
e
Kar
r
en
b
au
e
r
tr
an
s
f
o
r
m
atio
n
p
r
o
v
es
to
b
e
th
e
m
o
s
t
ef
f
ec
tiv
e
ap
p
r
o
ac
h
f
o
r
en
h
an
ci
n
g
s
ig
n
al
s
ep
ar
atio
n
.
As
a
r
es
u
lt,
it
s
ig
n
if
ican
tly
im
p
r
o
v
es
th
e
ac
cu
r
ac
y
o
f
f
a
u
lt
lo
ca
tio
n
in
h
i
g
h
-
v
o
ltag
e
tr
an
s
m
is
s
io
n
lin
es
[
2
2
]
.
=
=
[
1
1
1
1
−
2
1
1
1
−
2
]
(
6
)
−
1
=
−
1
=
1
3
[
1
1
1
1
−
1
0
1
0
−
1
]
(
7
)
2
.
2
.
Wa
v
elet
t
r
a
ns
f
o
rm
s
I
n
th
is
s
tu
d
y
,
th
e
DW
T
is
e
m
p
lo
y
ed
d
u
e
to
its
ef
f
ec
tiv
e
n
ess
in
p
r
ac
tical
im
p
lem
en
tatio
n
an
d
its
p
r
o
v
e
n
p
er
f
o
r
m
an
ce
i
n
p
o
w
er
s
y
s
tem
f
au
lt
an
aly
s
is
.
A
m
o
n
g
v
a
r
io
u
s
wav
elet
f
am
il
ies,
th
e
Dau
b
ec
h
ies
wav
elets,
p
ar
ticu
lar
ly
d
b
4
,
ar
e
s
elec
ted
f
o
r
th
eir
o
r
th
o
g
o
n
ality
,
co
m
p
ac
t su
p
p
o
r
t,
an
d
ab
ilit
y
to
p
r
eser
v
e
s
ig
n
al
en
er
g
y
.
T
h
e
d
b
4
wav
elet,
ch
ar
ac
ter
ized
b
y
f
o
u
r
v
an
is
h
in
g
m
o
m
en
ts
,
p
r
o
v
id
es
a
s
u
itab
l
e
tr
ad
e
-
o
f
f
b
etwe
en
tim
e
an
d
f
r
e
q
u
en
c
y
r
eso
lu
tio
n
,
en
ab
lin
g
ac
c
u
r
ate
d
etec
tio
n
o
f
f
au
lt
-
in
d
u
ce
d
tr
an
s
ien
ts
.
T
h
e
th
e
o
r
etica
l
f
o
u
n
d
atio
n
o
f
DW
T
is
b
ased
o
n
th
e
s
ca
lin
g
an
d
tr
a
n
s
latio
n
o
f
a
m
o
t
h
er
w
av
elet.
T
h
e
s
ca
lin
g
p
ar
am
eter
co
n
tr
o
ls
th
e
d
ilatio
n
an
d
co
m
p
r
ess
io
n
o
f
th
e
wav
e
f
o
r
m
,
wh
ile
th
e
tr
an
s
latio
n
p
ar
am
eter
d
eter
m
in
es
its
p
o
s
itio
n
in
tim
e.
T
h
ese
p
ar
am
eter
s
ar
e
d
is
cr
etize
d
lo
g
ar
ith
m
ically
,
lead
in
g
to
a
s
et
o
f
wav
elet
b
asis
f
u
n
ctio
n
s
d
ef
in
e
d
as
[
2
3
]
:
,
(
)
=
2
/
2
(
2
−
)
(
8
)
Fo
r
a
d
is
cr
ete
s
ig
n
al
[
]
th
e
d
ec
o
m
p
o
s
itio
n
u
s
in
g
d
b
4
is
p
er
f
o
r
m
ed
v
ia
co
n
v
o
lu
tio
n
with
lo
w
-
p
ass
an
d
h
ig
h
-
p
ass
f
ilter
s
,
f
o
llo
we
d
b
y
d
o
wn
s
am
p
lin
g
.
T
h
e
ap
p
r
o
x
im
atio
n
a
n
d
d
etail
co
e
f
f
i
cien
ts
at
lev
el
1
a
r
e
g
iv
en
b
y
(
9
)
an
d
(
1
0
)
.
1
[
]
=
∑
[
]
⋅
ℎ
[
2
−
]
(
9
)
1
[
]
=
∑
[
]
⋅
[
2
−
]
(
1
0
)
T
h
is
h
ier
ar
ch
ical
s
tr
u
ctu
r
e
e
n
ab
les
ac
cu
r
ate
e
x
tr
ac
tio
n
o
f
tim
e
-
lo
ca
lized
f
ea
t
u
r
es
f
o
r
ef
f
i
cien
t
f
au
lt
d
etec
tio
n
,
class
if
icatio
n
,
an
d
lo
ca
lizatio
n
in
h
ig
h
-
v
o
ltag
e
tr
a
n
s
m
is
s
io
n
n
etwo
r
k
s
.
T
h
e
DW
T
-
d
b
4
c
o
ef
f
icien
ts
,
wh
ich
ca
p
tu
r
e
tr
an
s
ien
t
s
ig
n
al
f
ea
tu
r
es,
s
er
v
e
as
in
p
u
ts
to
th
e
L
STM
n
etwo
r
k
.
T
h
is
co
m
b
in
atio
n
en
ab
les
th
e
m
o
d
el
to
lear
n
tem
p
o
r
al
p
atter
n
s
cr
u
cial
f
o
r
ac
cu
r
ate
f
a
u
lt c
lass
if
icatio
n
an
d
lo
ca
lizatio
n
[
2
4
]
.
2
.
3
.
L
ST
M
a
rc
hite
ct
ure
f
o
r
f
a
ult
lo
ca
t
io
n
L
o
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
n
eu
r
al
n
etwo
r
k
s
ar
e
an
ad
v
an
ce
d
class
o
f
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
s
(
R
NNs)
s
p
ec
if
ically
d
esig
n
ed
to
p
r
o
ce
s
s
tem
p
o
r
al
s
eq
u
en
ce
s
an
d
o
v
er
c
o
m
e
th
e
m
em
o
r
y
lim
itatio
n
s
o
f
co
n
v
en
tio
n
al
ar
c
h
itectu
r
es.
Owin
g
to
th
eir
s
tr
u
ctu
r
e,
w
h
ich
in
clu
d
es
m
em
o
r
y
ce
lls
an
d
g
atin
g
m
ec
h
an
is
m
s
,
L
STM
n
etwo
r
k
s
ca
n
ef
f
ec
tiv
ely
ca
p
tu
r
e
lo
n
g
-
te
r
m
tem
p
o
r
al
d
ep
en
d
en
cies
[
2
4
]
.
T
h
is
ca
p
a
b
ilit
y
m
ak
es
th
em
well
s
u
ited
f
o
r
an
aly
zin
g
tr
a
n
s
ien
t
elec
tr
ical
s
ig
n
als,
s
u
ch
as
th
o
s
e
g
en
er
ated
b
y
f
au
lts
in
h
ig
h
-
v
o
ltag
e
tr
an
s
m
is
s
io
n
n
etwo
r
k
s
,
an
d
s
u
p
p
o
r
ts
th
eir
ap
p
licatio
n
in
in
te
llig
en
t
f
au
lt
lo
ca
tio
n
an
d
p
r
e
d
i
ctiv
e
m
ain
ten
an
ce
s
tr
ateg
ies
[
2
4
]
.
An
L
STM
u
n
it
co
n
s
is
ts
o
f
a
c
ell
s
tate
an
d
th
r
ee
m
ain
g
ates
(
in
p
u
t,
f
o
r
g
et,
a
n
d
o
u
tp
u
t)
w
h
ich
r
eg
u
late
th
e
f
lo
w
o
f
in
f
o
r
m
atio
n
b
y
d
e
ter
m
in
in
g
wh
at
is
s
to
r
ed
,
u
p
d
ated
,
o
r
d
is
ca
r
d
ed
at
ea
ch
tim
e
s
tep
b
ased
o
n
th
e
in
p
u
t
v
ec
to
r
,
p
r
ev
io
u
s
h
id
d
e
n
s
tate,
an
d
p
r
ev
io
u
s
ce
ll
s
tate
.
Fig
u
r
e
2
s
h
o
ws
th
e
b
asic
u
n
it
o
f
an
L
STM
n
etwo
r
k
[
2
4
]
,
[
2
5
]
.
a.
Fo
r
g
et
g
ate
:
T
h
e
f
o
r
g
et
g
at
e
r
em
o
v
es o
l
d
,
u
s
eless
tr
ac
es.
=
(
.
[
ℎ
−
1
,
]
+
)
(
1
1
)
wh
er
e
:
Fo
r
g
ettin
g
g
ate
o
u
tp
u
t
(
b
etwe
en
0
an
d
1
)
,
:
Sig
m
o
id
ac
tiv
atio
n
f
u
n
ctio
n
,
:
Ob
liv
io
n
g
at
e
weig
h
t
,
ℎ
−
1
: Pr
ev
io
u
s
h
id
d
en
s
tat
e
,
: I
n
p
u
t a
t tim
e
,
an
d
: Bi
as o
f
th
e
f
o
r
g
ettin
g
g
ate.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
d
ee
p
lea
r
n
in
g
-
d
r
iven
tr
a
ve
lin
g
w
a
ve
meth
o
d
f
o
r
…
(
A
s
ma
Ta
lb
i
)
1709
s
i
g
t
a
nh
s
i
g
s
i
g
t
a
nh
x
t
h
t
-
1
C
t
-
1
f
t
Č
t
i
t
C
t
h
t
o
t
Ou
t
p
u
t
G
at
e
In
t
p
u
t
Gat
e
F
or
ge
t
Gat
e
C
e
ll
S
t
at
e
U
p
d
at
e
L
STM
CE
L
L
Fig
u
r
e
2
.
T
h
e
b
asic u
n
it
o
f
an
L
STM
n
etwo
r
k
T
h
e
g
ate'
s
f
u
n
ctio
n
is
to
d
eter
m
in
e
wh
ich
n
ew
i
n
f
o
r
m
atio
n
s
h
o
u
ld
b
e
s
to
r
ed
i
n
th
e
ce
ll st
ate.
a
.
I
n
p
u
t
g
ate
an
d
ca
n
d
id
ate
m
em
o
r
y
:
Dec
id
es wh
at
n
ew
i
n
f
o
r
m
atio
n
will b
e
s
to
r
ed
i
n
m
em
o
r
y
.
=
(
⋅
[
ℎ
−
1
,
]
+
)
(
1
2
)
̃
=
ℎ
(
⋅
[
ℎ
−
1
,
]
+
)
(
1
3
)
wh
er
e
: A
ctiv
atio
n
o
f
th
e
in
p
u
t g
ate
,
an
d
̃
: N
ew
ca
n
d
id
ate
v
a
lu
e
f
o
r
m
em
o
r
y
.
b
.
C
ell
s
tate
u
p
d
ate
:
T
h
is
g
ate
d
eter
m
in
es wh
at
n
ew
I
n
f
o
r
m
a
tio
n
is
s
to
r
ed
in
m
em
o
r
y
.
~
1
t
t
t
t
t
C
f
C
i
C
−
=+
(
1
4
)
wh
er
e
: N
ew
s
tate
o
f
th
e
m
em
o
r
y
.
: H
ad
am
ar
d
p
r
o
d
u
ct
(
ele
m
en
t b
y
elem
e
n
t)
.
c.
Ou
tp
u
t
g
ate
an
d
h
id
d
e
n
s
tate
:
I
t
d
ec
id
es
wh
at
in
f
o
r
m
ati
o
n
is
tr
an
s
m
itted
f
r
o
m
th
e
L
STM
o
u
tp
u
t
to
th
e
d
ec
is
io
n
lay
er
.
=
(
⋅
[
ℎ
−
1
,
]
+
)
(
1
5
)
ta
n
h
(
)
t
t
t
h
o
C
=
(
1
6
)
wh
er
e
: A
ctiv
atio
n
o
f
th
e
o
u
tp
u
t g
ate
,
an
d
ℎ
: N
ew
h
id
d
en
s
tate
(
u
s
ed
f
o
r
p
r
e
d
ictio
n
)
.
T
h
e
f
o
r
g
et
g
ate
f
ilter
s
o
u
t
ir
r
elev
an
t
p
ast
in
f
o
r
m
atio
n
,
p
r
es
er
v
in
g
ess
en
tial
co
n
te
x
t.
T
h
e
in
p
u
t
g
ate
s
elec
ts
r
elev
an
t
cu
r
r
en
t
d
ata
t
o
u
p
d
ate
th
e
ce
ll
s
tate,
wh
ile
th
e
o
u
tp
u
t
g
ate
d
eter
m
in
es
th
e
n
ex
t
h
id
d
en
s
tate.
T
o
g
eth
er
,
th
ese
g
ates e
n
ab
le
L
STM
s
to
ca
p
tu
r
e
lo
n
g
-
ter
m
d
e
p
en
d
en
cies e
f
f
icien
tl
y
[
2
6
]
.
2
.
4
.
T
he
im
pa
ct
o
f
t
hy
risto
r
-
co
ntr
o
lled series c
a
pa
cit
o
rs o
n wa
v
e
pro
pa
g
a
t
io
n
R
ec
en
tly
,
s
ev
er
al
s
tu
d
ies
h
av
e
f
o
cu
s
ed
o
n
f
au
lt
lo
ca
tio
n
in
lin
es
eq
u
ip
p
ed
with
T
C
SC
s
.
W
h
il
e
class
ical
im
p
ed
an
ce
-
b
ased
a
p
p
r
o
ac
h
es
r
em
ain
wid
ely
u
s
ed
,
o
th
er
m
eth
o
d
s
ex
p
lo
it
n
eu
r
al
n
etwo
r
k
s
an
d
d
eter
m
in
is
tic
m
o
d
els
to
esti
m
ate
th
e
v
o
ltag
e
ac
r
o
s
s
th
e
co
m
p
en
s
atio
n
d
ev
ice
[
2
7
]
.
T
h
e
T
C
SC
co
n
s
is
t
s
o
f
a
s
er
ies
ca
p
ac
ito
r
,
a
th
y
r
is
to
r
-
co
n
tr
o
lled
r
ea
cto
r
,
an
d
a
m
etal
-
o
x
id
e
v
ar
is
to
r
(
MO
V)
f
o
r
o
v
er
v
o
ltag
e
p
r
o
tectio
n
.
T
h
e
f
ir
in
g
an
g
le
d
eter
m
in
es its
eq
u
iv
alen
t im
p
ed
an
ce
α
an
d
c
an
b
e
ex
p
r
ess
ed
as f
o
llo
ws
:
(
)
=
(
)
(
)
−
′
(
1
7
)
=
1
′
(
1
8
)
(
)
=
−
2
+
(
2
)
(
1
9
)
T
h
e
eq
u
iv
ale
n
t lin
e
im
p
e
d
an
c
e
b
ec
o
m
es:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
4
,
Au
g
u
s
t
20
2
6
:
1
7
0
4
-
1723
1710
,
=
+
(
)
(
2
0
)
W
h
ich
m
o
d
if
ies th
e
p
r
o
p
ag
ati
o
n
co
n
s
tan
t:
=
+
=
√
(
+
′
)
(
+
′
)
(
2
1
)
wh
er
e
L
'
an
d
C
'
ar
e
ef
f
ec
tiv
e
v
alu
es u
n
d
er
c
o
m
p
e
n
s
atio
n
.
T
h
e
p
h
ase
v
elo
city
is
th
en
:
=
(
2
2
)
wh
er
e
:
atten
u
atio
n
co
n
s
tan
t
(
i
n
n
e
p
er
s
p
e
r
m
ete
r
,
Np
/
m
)
,
:
p
h
ase
co
n
s
tan
t (
in
r
ad
ian
s
p
er
m
eter
,
r
a
d
/m
)
.
T
h
er
ef
o
r
e
If
At
0
°,
th
e
lin
e
is
id
ea
l (
n
o
lo
s
s
es);
o
n
ly
a
p
h
ase
s
h
if
t o
cc
u
r
s
.
If
⟩
0
T
h
e
wa
v
e
am
p
litu
d
e
d
ec
r
ea
s
es
alo
n
g
th
e
lin
e,
wh
ic
h
i
s
a
r
ea
lis
tic
ca
s
e
in
p
r
ac
tical
tr
an
s
m
is
s
io
n
s
y
s
tem
s
.
I
n
ca
p
ac
itiv
e
m
o
d
e
(
<0
)
,
th
e
s
er
ies
r
ea
ctan
ce
d
ec
r
ea
s
es,
r
e
d
u
cin
g
an
d
th
er
ef
o
r
e
in
c
r
ea
s
in
g
:
in
in
d
u
ctiv
e
m
o
d
e
(
>0
)
,
in
cr
ea
s
es,
th
u
s
r
ed
u
cin
g
vp
[
2
8
]
.
Mo
r
eo
v
er
,
th
e
T
C
SC
in
tr
o
d
u
ce
s
a
d
is
co
n
tin
u
ity
in
lin
e
i
m
p
ed
a
n
c
e,
with
th
e
r
ef
lectio
n
co
ef
f
icie
n
t
d
ef
in
ed
as
:
=
−
−
′
(
2
3
)
T
h
is
in
cr
ea
s
es
th
e
m
is
m
atch
b
etwe
en
ad
jace
n
t
s
ec
tio
n
s
,
d
is
to
r
tin
g
th
e
r
ef
lecte
d
an
d
tr
an
s
m
itted
wav
es.
T
h
ese
d
is
to
r
tio
n
s
m
ak
e
it
d
if
f
icu
lt
to
id
en
tify
th
e
f
i
r
s
t
wav
ef
r
o
n
t
a
r
r
iv
al,
a
cr
itica
l
s
tep
in
tr
av
elin
g
-
wav
e
-
b
ased
f
a
u
lt
lo
ca
tio
n
m
e
th
o
d
s
[
2
9
]
.
R
ec
en
t
r
esear
ch
h
as
em
p
h
asized
th
e
u
s
e
o
f
d
is
tr
ib
u
ted
-
p
a
r
am
eter
lin
e
m
o
d
els
an
d
ad
v
an
ce
d
tec
h
n
iq
u
es,
s
u
ch
as
wav
elet
tr
an
s
f
o
r
m
s
an
d
d
ee
p
lear
n
i
n
g
,
to
i
m
p
r
o
v
e
th
e
ac
cu
r
ac
y
o
f
f
au
lt lo
ca
tio
n
in
c
o
m
p
en
s
at
ed
n
etwo
r
k
s
[
3
0
]
,
[
3
1
]
.
3.
T
H
E
P
RO
P
O
SE
D
M
E
T
H
O
DO
L
O
G
Y
I
n
h
ig
h
-
v
o
ltag
e
t
r
an
s
m
is
s
io
n
n
etwo
r
k
s
,
p
ar
ticu
lar
l
y
at
4
0
0
k
V,
f
ast
an
d
ac
cu
r
ate
f
au
lt
l
o
ca
tio
n
is
ess
en
tial
f
o
r
m
ain
tain
in
g
s
y
s
tem
s
tab
ilit
y
an
d
m
in
im
izin
g
o
u
tag
e
d
u
r
atio
n
s
.
T
r
av
elin
g
wav
e
(
T
W
)
m
eth
o
d
s
ex
p
lo
it
h
ig
h
-
f
r
e
q
u
en
c
y
tr
an
s
ie
n
ts
g
en
er
ated
at
f
a
u
lt
in
ce
p
tio
n
,
en
ab
lin
g
p
r
ec
is
e
f
a
u
lt
lo
ca
lizatio
n
u
s
in
g
s
in
g
le
-
en
d
ed
o
r
d
o
u
b
le
-
en
d
ed
m
ea
s
u
r
em
en
ts
.
W
h
ile
s
in
g
le
-
en
d
ed
ap
p
r
o
ac
h
es
a
r
e
ea
s
ier
to
im
p
lem
en
t,
th
e
y
g
en
er
ally
o
f
f
er
l
o
wer
ac
cu
r
ac
y
co
m
p
ar
e
d
to
GPS
-
s
y
n
ch
r
o
n
iz
ed
d
o
u
b
le
-
en
d
ed
m
eth
o
d
s
.
T
o
ad
d
r
ess
th
is
lim
itatio
n
,
a
L
STM
n
eu
r
al
n
etwo
r
k
is
in
teg
r
ated
to
ef
f
ec
tiv
ely
p
r
o
ce
s
s
co
m
p
lex
tem
p
o
r
al
s
ig
n
als
an
d
ac
c
u
r
atel
y
d
etec
t
th
e
ar
r
iv
al
o
f
th
e
f
ir
s
t
tr
av
ellin
g
wav
e.
T
h
e
p
r
o
p
o
s
e
d
h
y
b
r
id
ap
p
r
o
ac
h
co
m
b
in
es
th
e
h
ig
h
tem
p
o
r
al
r
eso
lu
tio
n
o
f
T
W
an
al
y
s
is
with
th
e
lear
n
in
g
ca
p
ab
ilit
y
o
f
L
STM
n
etwo
r
k
s
,
r
esu
ltin
g
in
a
r
o
b
u
s
t
a
n
d
ac
c
u
r
ate
f
a
u
lt
lo
ca
tio
n
f
r
a
m
ewo
r
k
s
u
itab
le
f
o
r
co
m
p
le
x
an
d
c
o
m
p
en
s
ated
p
o
wer
s
y
s
tem
s
.
T
h
e
o
v
er
all
p
r
o
ce
d
u
r
e
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
is
illu
s
tr
ated
in
Fig
u
r
e
3
,
wh
ich
p
r
e
s
en
ts
th
e
GPS
-
f
r
ee
f
au
lt
lo
ca
tio
n
s
ch
em
e
in
teg
r
atin
g
DW
T
-
b
ased
f
ea
tu
r
e
ex
tr
ac
tio
n
with
L
STM
-
b
ased
d
etec
tio
n
f
o
r
n
o
is
e
-
r
esil
ien
t a
n
d
ac
cu
r
ate
f
a
u
lt lo
c
aliza
tio
n
.
L
S
T
M
N
e
t
w
o
r
k
F
a
u
l
t
l
o
c
a
t
i
o
n
B
U
S
A
B
U
S
B
F
au
l
t
I
n
p
u
t
:
V
(
t
)
r
e
c
o
r
d
e
d
Wa
v
e
l
e
t
d
e
c
o
m
p
o
s
i
t
i
o
n
D
W
T
K
a
r
r
e
n
b
a
u
r
t
r
a
n
s
f
o
r
m
a
t
i
o
n
P
e
a
k
p
r
e
d
i
c
t
i
o
n
p
o
s
i
t
i
o
n
s
C
a
l
c
u
l
a
t
e
t
i
m
e
i
n
t
e
r
v
a
l
b
e
t
w
e
e
n
t
h
e
t
w
o
d
e
t
e
c
t
e
d
p
e
a
k
s
T
C
S
C
T
r
a
v
e
l
l
i
n
g
w
a
v
e
D
F
R
Fig
u
r
e
3
.
Flo
wch
ar
t
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
d
ee
p
lea
r
n
in
g
-
d
r
iven
tr
a
ve
lin
g
w
a
ve
meth
o
d
f
o
r
…
(
A
s
ma
Ta
lb
i
)
1711
3
.
1
.
Sim
ula
t
i
o
n f
ra
m
ewo
rk
f
o
r
f
a
ult
lo
c
a
t
io
n us
ing
T
W
-
L
ST
M
T
o
v
alid
ate
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
T
W
-
L
STM
f
au
lt
lo
ca
tio
n
ap
p
r
o
ac
h
,
a
r
ep
r
esen
tativ
e
p
o
r
tio
n
o
f
th
e
Alg
e
r
ian
4
0
0
k
V
tr
an
s
m
is
s
io
n
n
etwo
r
k
wa
s
m
o
d
eled
u
s
in
g
AT
P/EM
T
P
.
T
h
e
test
s
y
s
tem
co
n
s
is
ts
o
f
a
1
2
0
k
m
lin
e
co
n
n
ec
tin
g
O
u
ed
Ath
m
an
ia
to
R
a
m
d
an
e
Djam
el,
wh
ich
in
clu
d
e
s
a
m
id
-
li
n
e
T
C
SC
m
o
d
u
le
to
e
m
u
late
s
er
ies
c
o
m
p
en
s
atio
n
ef
f
ec
ts
.
T
h
e
tr
an
s
m
i
s
s
io
n
lin
e
was
r
e
p
r
esen
ted
u
s
in
g
Ma
r
ti’s
m
o
d
el
to
ca
p
tu
r
e
its
tr
an
s
ien
t
b
eh
a
v
io
r
ac
cu
r
ately
.
Fig
u
r
e
4
illu
s
tr
ates
th
e
s
ch
em
atic
m
o
d
el
im
p
lem
en
ted
i
n
AT
P/EM
T
P.
A
i
r
-
ga
ps
M
O
V
s
S
C
s
T
CS
C
G
e
ne
r
a
t
or
L
i
ne
L
i
ne
CB
1
CB
2
B
U
S
A
BU
S
B
S
ou
r
c
e
t
r
a
n
s
f
o
r
m
er
60
km
60
km
Fig
u
r
e
4
.
Sch
em
atic
tr
a
n
s
m
is
s
io
n
lin
e
with
T
C
SC
m
o
d
u
le
i
m
p
lem
en
ted
in
AT
P/EM
T
P
T
h
e
T
C
SC
m
o
d
u
le
is
co
n
n
ec
ted
in
s
er
ies
with
th
e
tr
a
n
s
m
is
s
io
n
lin
e
an
d
is
eq
u
ip
p
ed
wi
th
a
s
u
r
g
e
ar
r
ester
f
o
r
o
v
er
v
o
ltag
e
p
r
o
te
ctio
n
,
th
is
s
ch
em
atic
h
ig
h
lig
h
ts
th
e
ess
en
tial
co
m
p
o
n
en
ts
in
f
lu
en
cin
g
tr
an
s
ien
t
wav
e
p
r
o
p
a
g
atio
n
with
o
u
t
d
e
lv
in
g
in
to
u
n
n
ec
ess
ar
y
im
p
le
m
en
tatio
n
d
etails,
m
ak
in
g
it
well
-
s
u
ited
f
o
r
th
e
p
u
r
p
o
s
e
o
f
f
a
u
lt
lo
ca
tio
n
a
n
aly
s
is
.
T
h
e
o
v
er
all
s
im
u
latio
n
s
ch
em
e
is
d
ep
icted
in
Fig
u
r
e
5
,
wh
er
e
a
f
au
lt
is
ap
p
lied
at
d
if
f
er
en
t lo
ca
tio
n
s
alo
n
g
th
e
lin
e
to
g
e
n
er
ate
tr
an
s
i
en
t sig
n
als u
s
ed
in
th
e
an
al
y
s
is
.
Fig
u
r
e
5
.
Mo
d
el
o
f
a
tr
an
s
m
is
s
io
n
lin
e
T
h
e
tr
an
s
m
is
s
io
n
lin
e
m
o
d
el
is
d
ef
in
e
d
b
ased
o
n
k
e
y
ele
ctr
ical
an
d
g
eo
m
et
r
ical
p
ar
a
m
eter
s
th
at
s
ig
n
if
ican
tly
in
f
lu
en
ce
t
h
e
p
r
o
p
ag
atio
n
o
f
tr
an
s
ien
t
wav
es.
I
n
p
ar
ticu
lar
,
th
e
g
eo
m
etr
ical
c
o
n
f
ig
u
r
atio
n
o
f
th
e
to
wer
p
lay
s
a
cr
u
cial
r
o
le
i
n
ac
cu
r
ately
r
ep
r
esen
tin
g
th
e
p
h
y
s
ical
lay
o
u
t
o
f
th
e
co
n
d
u
cto
r
s
.
T
h
e
m
ain
p
ar
am
eter
s
o
f
t
h
e
tr
an
s
m
is
s
io
n
lin
e
ar
e
s
u
m
m
ar
ized
in
T
ab
le
2
.
T
ab
le
2
.
Key
t
r
an
s
m
is
s
io
n
lin
e
an
d
to
wer
g
eo
m
etr
ical
p
ar
am
eter
s
P
a
r
a
me
t
e
r
S
a
mp
l
i
n
g
f
r
e
q
u
e
n
c
y
(
f
s)
R
e
si
st
a
n
c
e
p
e
r
u
n
i
t
l
e
n
g
t
h
(
R
)
C
o
n
d
u
c
t
o
r
e
x
t
e
r
n
a
l
r
a
d
i
u
s (r
)
To
t
a
l
c
r
o
ss
-
a
r
m
l
e
n
g
t
h
H
o
r
i
z
o
n
t
a
l
sp
a
c
i
n
g
b
e
t
w
e
e
n
c
o
n
d
u
c
t
o
r
s
V
e
r
t
i
c
a
l
h
e
i
g
h
t
(
t
o
p
c
o
n
d
u
c
t
o
r
l
e
v
e
l
)
M
i
d
-
sp
a
n
h
e
i
g
h
t
S
u
b
-
c
o
n
d
u
c
t
o
r
sp
a
c
i
n
g
V
a
l
u
e
2
M
H
z
0
.
0
5
8
3
Ω
/
k
m
1
5
.
5
2
5
mm
1
3
.
7
m
10
m
25
.
05
m
1
7
.
7
2
m
50
cm
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
4
,
Au
g
u
s
t
20
2
6
:
1
7
0
4
-
1723
1712
Fig
u
r
e
6
s
h
o
ws
a
d
etailed
s
ch
em
atic
d
iag
r
a
m
o
f
a
T
C
SC
m
o
d
u
le
th
at
h
as
b
ee
n
im
p
le
m
en
ted
in
AT
P/EM
T
P.
T
h
is
co
n
f
ig
u
r
ati
o
n
co
m
p
r
is
es
s
ev
er
al
id
en
tical
s
in
g
le
-
p
h
ase
u
n
its
co
n
n
ec
te
d
in
s
er
ies
with
th
e
tr
an
s
m
is
s
io
n
lin
e.
E
ac
h
m
o
d
u
le
is
d
esig
n
ed
to
en
ab
le
d
y
n
a
m
ic
lin
e
r
ea
ctan
ce
co
n
tr
o
l
an
d
is
eq
u
ip
p
ed
with
r
o
b
u
s
t
p
r
o
tectio
n
co
m
p
o
n
en
t
s
.
E
ac
h
T
C
S
C
u
n
it
co
n
s
i
s
ts
o
f
a
th
y
r
is
to
r
-
co
n
tr
o
lled
r
ea
cto
r
(
T
C
R
)
,
wh
ich
co
m
p
r
is
es
an
tip
ar
allel
th
y
r
is
to
r
s
co
n
n
ec
ted
in
s
er
ies
with
an
in
d
u
ctan
ce
(
L
T
)
.
T
h
e
T
C
R
is
co
n
tr
o
lled
v
ia
a
tr
ig
g
er
in
g
m
ec
h
an
is
m
r
ep
r
ese
n
ted
b
y
th
e
'
MO
DE
L
'
b
lo
ck
.
T
h
is
b
r
an
ch
o
p
er
ates
as
a
v
ar
iab
le
in
d
u
ctan
ce
,
m
o
d
u
latin
g
t
h
e
n
et
r
ea
ctan
ce
i
n
tr
o
d
u
ce
d
b
y
t
h
e
m
o
d
u
le.
A
f
i
x
ed
ca
p
ac
ito
r
(
C
T
)
co
n
n
ec
ted
i
n
p
ar
allel
with
th
e
T
C
R
p
r
o
v
id
es
th
e
ca
p
ac
itiv
e
co
m
p
o
n
en
t.
T
o
en
h
an
ce
p
r
o
tectio
n
ag
ain
s
t
o
v
er
v
o
lta
g
e,
a
m
etal
o
x
id
e
v
ar
is
to
r
(
MO
V)
is
co
n
n
ec
ted
in
p
a
r
allel
with
ea
ch
m
o
d
u
le.
T
h
e
MO
V
ac
ts
as
a
n
o
n
-
lin
ea
r
r
esis
to
r
,
d
iv
er
tin
g
e
x
ce
s
s
iv
e
cu
r
r
en
t
d
u
r
i
n
g
tr
an
s
ien
t
co
n
d
i
tio
n
s
,
s
u
ch
as
f
au
lts
o
r
s
witch
in
g
o
p
er
atio
n
s
.
E
ac
h
m
o
d
u
l
e
also
in
co
r
p
o
r
ates
r
esis
tiv
e
an
d
ca
p
ac
itiv
e
s
n
u
b
b
er
cir
cu
its
(
R
S
an
d
C
S
)
an
d
d
am
p
in
g
r
esis
to
r
s
(
R
R
an
d
R
F
)
to
r
estrict
v
o
ltag
e
tr
an
s
ien
ts
ac
r
o
s
s
th
e
th
y
r
is
to
r
s
an
d
g
u
a
r
an
tee
s
tab
le
o
p
e
r
at
io
n
.
T
h
is
m
o
d
u
lar
c
o
n
f
ig
u
r
ati
o
n
im
p
r
o
v
es
f
au
lt
to
ler
an
ce
an
d
e
n
ab
les
ef
f
ec
tiv
e
s
er
ies
co
m
p
en
s
atio
n
an
d
d
y
n
am
ic
co
n
t
r
o
l
o
f
p
o
wer
f
lo
w
o
n
lo
n
g
t
r
an
s
m
is
s
io
n
lin
es.
Fig
u
r
e
6
.
T
h
y
r
is
to
r
-
c
o
n
tr
o
lled
s
tatic
co
m
p
en
s
ato
r
m
o
d
el
i
n
A
T
P
-
EMTP
T
h
e
f
o
llo
win
g
p
ar
am
eter
s
f
o
r
th
y
r
is
to
r
c
o
n
tr
o
lled
s
tatic
co
m
p
e
n
s
ato
r
ar
e:
=
160
,
=
13
.
31
;
=
0
.
01
,
=
0
.
1
;
=
0
.
05
,
=
0
.
05
s
u
r
g
e
ar
r
ester
s
in
s
talled
in
th
e
co
m
p
en
s
atio
n
o
f
ty
p
e
AB
B
(
E
XL
I
M
Q
-
D)
with
th
e
r
ef
er
en
ce
v
o
ltag
e
(
V
REF
)
=
1
7
0
k
V
,
an
d
its
tech
n
ical
ch
ar
ac
ter
is
tics
ar
e
in
T
ab
le
3
[
3
2
]
.
T
ab
le
3
.
T
ec
h
n
ical
d
ata
o
f
th
e
AB
B
s
u
r
g
e
ar
r
ester
EX
LI
M
Q
-
D
/
/
(
)
0
.
5
1
2
5
10
20
40
(
)
2
5
4
2
6
2
2
7
2
2
9
5
3
1
1
3
4
2
3
8
2
Var
io
u
s
f
au
lt
s
ce
n
ar
io
s
(
L
–
G,
L
–
L
,
L
–
L
–
G,
a
n
d
L
–
L
–
L
)
w
er
e
s
im
u
lated
o
n
a
4
0
0
k
V
tr
a
n
s
m
is
s
io
n
lin
e
to
ev
alu
ate
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
T
h
e
p
r
esen
ce
o
f
th
e
T
C
SC
af
f
ec
ts
wav
e
p
r
o
p
ag
atio
n
an
d
in
f
l
u
en
ce
s
th
e
ar
r
iv
al
tim
e
o
f
tr
av
elin
g
wav
e
s
.
Simu
latio
n
s
wer
e
p
er
f
o
r
m
e
d
in
AT
P/EM
T
P,
with
v
o
ltag
e
an
d
cu
r
r
en
t
s
ig
n
als
m
ea
s
u
r
ed
at
a
s
in
g
le
s
u
b
s
tati
o
n
.
Un
lik
e
co
n
v
en
tio
n
al
ap
p
r
o
ac
h
es,
th
is
s
tu
d
y
em
p
h
asizes
th
e
u
s
e
o
f
v
o
ltag
e
s
ig
n
als f
o
r
f
au
lt d
etec
tio
n
an
d
lo
ca
tio
n
in
co
m
p
en
s
ated
n
etwo
r
k
s
.
T
h
e
r
ec
o
r
d
ed
s
ig
n
als
wer
e
p
r
o
ce
s
s
ed
in
MA
T
L
AB
,
b
eg
in
n
in
g
with
m
o
d
al
tr
a
n
s
f
o
r
m
atio
n
an
d
s
q
u
ar
in
g
to
en
h
an
ce
th
e
d
is
tu
r
b
an
ce
s
in
d
u
ce
d
b
y
t
h
e
f
au
lt.
Dis
cr
ete
wav
elet
tr
an
s
f
o
r
m
with
Dau
b
ec
h
ies
-
4
(
DB
4
)
was
th
en
ap
p
lied
an
d
th
e
ex
tr
ac
ted
d
etail
co
ef
f
icie
n
ts
(
D1
–
D3
)
wer
e
u
s
ed
as
in
p
u
t
f
ea
tu
r
es
to
tr
ain
a
L
STM
n
eu
r
al
n
etwo
r
k
f
o
r
au
to
m
atic
f
au
lt
d
etec
tio
n
.
As
il
lu
s
tr
ated
in
Fig
u
r
e
7
,
th
e
r
esu
lts
o
f
th
e
wav
elet
an
aly
s
is
f
o
r
a
two
-
p
h
ase
(
L
L
)
f
au
lt
o
cc
u
r
r
in
g
3
0
0
k
m
f
r
o
m
B
u
s
A
d
em
o
n
s
tr
ate
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
.
T
h
e
d
b
4
wav
elet,
with
it
s
f
o
u
r
v
an
is
h
in
g
m
o
m
en
ts
,
s
tr
ik
es
an
o
p
tim
al
b
alan
ce
b
etwe
en
tim
e
an
d
f
r
eq
u
e
n
cy
r
eso
lu
tio
n
,
r
en
d
er
in
g
it
p
ar
ticu
lar
ly
well
-
s
u
ited
to
h
ig
h
-
v
o
ltag
e
f
au
lt
an
aly
s
is
.
L
ev
el
D1
was
s
elec
ted
d
u
e
to
its
a
b
ilit
y
to
ca
p
tu
r
e
th
e
h
i
g
h
-
f
r
eq
u
e
n
cy
co
n
ten
t
ass
o
ciate
d
with
th
e
in
i
tial
f
au
lt
tr
a
n
s
ien
t.
T
h
is
m
eth
o
d
o
lo
g
y
alig
n
s
with
p
r
e
v
io
u
s
s
tu
d
ies
[
3
3
]
,
wh
ic
h
co
n
f
ir
m
ed
th
e
ef
f
ec
tiv
e
n
ess
o
f
co
m
b
in
in
g
d
b
4
-
DW
T
with
AI
m
o
d
els in
tr
an
s
ien
t sig
n
al
an
aly
s
is
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
d
ee
p
lea
r
n
in
g
-
d
r
iven
tr
a
ve
lin
g
w
a
ve
meth
o
d
f
o
r
…
(
A
s
ma
Ta
lb
i
)
1713
Fig
u
r
e
7
.
W
av
elet
d
etail
co
ef
f
i
cien
ts
(
L
ev
el
1
-
d
b
4
)
As
s
h
o
wn
in
Fig
u
r
e
7
,
th
e
d
e
tail
co
ef
f
icien
ts
o
b
tain
ed
f
r
o
m
th
e
DW
T
ar
e
u
s
ed
as
in
p
u
t
v
ec
to
r
s
to
tr
ain
a
L
STM
r
ec
u
r
r
en
t
n
e
u
r
a
l
n
etwo
r
k
.
T
h
is
ty
p
e
o
f
n
etw
o
r
k
is
p
ar
ticu
lar
ly
well
-
s
u
ited
to
an
aly
zin
g
tim
e
s
er
ies
d
ata
d
u
e
to
its
ab
ilit
y
to
ca
p
tu
r
e
lo
n
g
-
ter
m
tem
p
o
r
a
l
d
ep
en
d
e
n
cies.
I
n
th
is
s
tu
d
y
,
th
e
L
STM
m
o
d
el
u
tili
ze
s
th
e
tem
p
o
r
al
p
atter
n
s
i
n
th
e
wav
elet
co
e
f
f
icien
ts
to
lear
n
th
e
d
is
tin
g
u
is
h
in
g
f
ea
t
u
r
e
s
o
f
d
if
f
er
e
n
t
f
a
u
lt
ty
p
es
an
d
esti
m
ate
th
eir
lo
ca
ti
o
n
s
alo
n
g
th
e
tr
an
s
m
is
s
io
n
lin
e.
Sp
ec
if
ically
,
th
e
n
etwo
r
k
is
tr
ain
ed
to
p
r
ed
ict
two
k
ey
m
o
m
en
ts
in
th
e
s
ig
n
al:
₁
,
th
e
ar
r
iv
al
tim
e
o
f
th
e
f
i
r
s
t
wav
ef
r
o
n
t
p
ea
k
(
ty
p
ically
at
m
ea
s
u
r
em
en
t
s
tatio
n
A)
,
an
d
₂
,
th
e
ar
r
iv
al
tim
e
o
f
th
e
s
ec
o
n
d
p
ea
k
(
d
u
e
t
o
eith
er
r
ef
lectio
n
o
r
ar
r
iv
al
at
s
tatio
n
B
)
.
On
ce
th
ese
two
in
s
tan
ts
h
av
e
b
ee
n
id
en
tifie
d
,
th
e
f
a
u
lt
lo
ca
tio
n
c
an
b
e
d
eter
m
i
n
ed
u
s
in
g
th
e
cl
ass
ical
tr
av
el
-
tim
e
f
o
r
m
u
la.
=
.
(
2
−
1
)
2
(
2
3
)
W
h
er
e
: is th
e
v
elo
city
at
wh
i
ch
th
e
tr
av
ellin
g
wav
e
p
r
o
p
a
g
ates in
th
e
tr
an
s
m
is
s
io
n
lin
e.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
was
im
p
lem
en
ted
in
MA
T
L
AB
as
a
s
im
u
lated
L
STM
s
tr
u
ctu
r
e
b
ased
o
n
a
f
ee
d
f
o
r
war
d
n
eu
r
al
n
etwo
r
k
.
T
h
e
in
p
u
t
c
o
n
s
is
ts
o
f
th
e
f
ir
s
t
-
lev
el
d
etail
c
o
ef
f
icien
ts
.
D1
,
t
ak
en
f
r
o
m
th
e
DW
T
,
is
n
o
r
m
alize
d
b
ef
o
r
e
tr
ain
in
g
.
A
s
lid
in
g
win
d
o
w
is
ap
p
lied
,
allo
win
g
ea
ch
in
p
u
t
v
ec
to
r
to
r
ep
r
esen
t
co
n
s
ec
u
tiv
e
s
am
p
les
o
f
D1
,
th
er
eb
y
en
a
b
lin
g
th
e
n
etwo
r
k
to
ca
p
tu
r
e
th
e
tem
p
o
r
al
d
ep
en
d
e
n
ce
o
f
th
e
tr
av
elin
g
wav
e
s
ig
n
al.
T
h
e
n
etwo
r
k
is
co
m
p
o
s
ed
o
f
1
0
h
i
d
d
en
n
eu
r
o
n
s
an
d
h
as
b
ee
n
t
r
ain
ed
u
s
in
g
th
e
L
e
v
en
b
e
r
g
-
Ma
r
q
u
ar
d
t
(
tr
ain
lm
)
b
ac
k
p
r
o
p
ag
atio
n
alg
o
r
ith
m
o
v
er
1
0
0
ite
r
atio
n
s
(
ep
o
ch
s
)
.
T
h
e
co
s
t
f
u
n
ctio
n
ch
o
s
en
is
th
e
m
ea
n
s
q
u
a
r
e
e
r
r
o
r
(
MSE
)
,
an
d
th
e
d
ata
is
n
o
r
m
alize
d
to
s
p
ee
d
u
p
c
o
n
v
e
r
g
en
ce
.
At
t
h
e
e
n
d
o
f
th
e
tr
ain
i
n
g
,
th
e
n
etwo
r
k
'
s
o
u
tp
u
t
c
o
r
r
esp
o
n
d
s
to
a
p
r
e
d
icted
v
e
r
s
io
n
o
f
th
e
co
ef
f
icien
ts
D1
,
f
r
o
m
w
h
ich
th
e
m
o
s
t
r
elev
a
n
t
p
ea
k
s
ar
e
a
u
to
m
atica
lly
d
ete
cted
.
T
h
e
f
ir
s
t
two
h
ig
h
-
am
p
litu
d
e
p
ea
k
s
,
c
o
r
r
esp
o
n
d
in
g
r
esp
ec
tiv
ely
to
th
e
in
cid
en
t
wav
e
an
d
its
r
ef
lecte
d
ec
h
o
,
ar
e
r
etain
ed
to
lo
ca
te
th
e
f
au
lt.
T
h
e
tim
e
in
ter
v
al
(
Δ
t)
b
etwe
en
th
ese
two
p
ea
k
s
is
th
en
en
te
r
ed
in
to
th
e
tr
av
ellin
g
wav
e
eq
u
atio
n
to
esti
m
ate
th
e
d
is
tan
ce
o
f
t
h
e
f
a
u
lt
alo
n
g
t
h
e
tr
an
s
m
is
s
io
n
lin
e.
Fig
u
r
e
8
s
h
o
ws th
e
ar
ch
itectu
r
e
o
f
th
e
p
r
o
p
o
s
ed
L
STM
.
Fig
u
r
e
8
.
Ar
c
h
itectu
r
e
o
f
th
e
p
r
o
p
o
s
ed
L
STM
-
b
ased
f
au
lt lo
c
atio
n
m
o
d
el
Fig
u
r
e
8
p
r
esen
ts
th
e
p
r
o
p
o
s
e
d
h
y
b
r
id
f
r
am
ewo
r
k
,
wh
er
e
DW
T
is
u
s
ed
to
ex
tr
ac
t
th
e
h
i
g
h
-
f
r
eq
u
e
n
cy
d
etail
co
ef
f
icien
ts
(
D1
)
,
wh
ic
h
ar
e
th
e
n
p
r
o
ce
s
s
ed
b
y
th
e
L
STM
n
etwo
r
k
u
s
in
g
a
s
lid
in
g
win
d
o
w
to
ca
p
t
u
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.