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[
1
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T
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Activ
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PC
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to
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
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52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
1
,
J
an
u
ar
y
20
25
:
35
-
47
36
h
a
n
d
l
e
f
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w
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s
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4
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m
s
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r
en
t c
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l a
lg
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ith
m
s
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y
n
ch
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r
en
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eth
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d
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th
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s
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n
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s
p
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(
PQ)
th
eo
r
y
[
5
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-
[
7
]
.
Alth
o
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g
h
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tech
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m
p
l
ex
ity
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d
s
en
s
itiv
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to
n
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[
8
]
.
R
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t
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v
an
ce
m
e
n
ts
in
ar
tific
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in
tellig
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p
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m
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.
Ar
tific
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n
well
-
s
u
ited
to
th
i
s
task
.
T
h
an
k
s
to
its
L
M
S
l
ea
r
n
in
g
alg
o
r
ith
m
,
ADAL
I
NE
ad
ap
ts
in
r
ea
l
tim
e
to
v
ar
iatio
n
s
in
th
e
elec
tr
ical
n
etwo
r
k
,
p
r
o
v
id
in
g
ac
cu
r
ate
a
n
d
r
ap
id
esti
m
ates
o
f
r
ef
er
e
n
ce
cu
r
r
en
ts
.
I
ts
s
im
p
licity
an
d
ef
f
ec
tiv
e
n
ess
m
ak
e
it
a
p
r
ef
er
r
e
d
to
o
l
f
o
r
an
al
y
zin
g
th
r
ee
-
p
h
ase
elec
tr
ical
s
y
s
tem
s
[
9
]
,
[
1
0
]
.
N
u
m
er
o
u
s
s
tu
d
ies co
n
f
ir
m
th
e
s
u
p
er
io
r
ity
o
f
ADAL
I
NE
o
v
er
tr
ad
itio
n
al
m
eth
o
d
s
,
esp
ec
ially
f
o
r
ex
tr
ac
tin
g
f
u
n
d
a
m
en
tal
an
d
h
ar
m
o
n
ic
co
m
p
o
n
en
ts
.
E
ar
ly
im
p
lem
en
tatio
n
s
o
f
ADAL
I
NE
in
s
in
g
le
-
p
h
ase
s
y
s
te
m
s
d
em
o
n
s
tr
ated
its
ef
f
ec
tiv
en
ess
u
n
d
er
id
ea
l
v
o
ltag
e
co
n
d
itio
n
s
a
n
d
s
tead
y
-
s
tate
o
p
e
r
atio
n
[
1
1
]
.
Su
b
s
eq
u
en
t
s
tu
d
ies,
s
u
ch
as
th
e
f
u
n
d
am
e
n
tal
ac
tiv
e
cu
r
r
en
t
(
FAC
)
-
ADAL
I
NE
,
ex
ten
d
ed
its
ap
p
licatio
n
t
o
d
y
n
a
m
ic
co
n
d
itio
n
s
[
1
2
]
.
Fu
r
th
er
r
esear
ch
ex
p
a
n
d
ed
th
e
u
s
e
o
f
ADAL
I
NE
t
o
th
r
e
e
-
ph
ase,
th
r
ee
-
wir
e
s
y
s
tem
s
[
1
3
]
,
[
1
4
]
.
T
h
e
s
in
g
le
-
p
h
ase
A
DAL
I
NE
tech
n
iq
u
e
was
ad
ap
ted
to
th
r
ee
-
p
h
ase
s
y
s
tem
s
b
y
ap
p
ly
in
g
it
to
ea
c
h
p
h
ase
i
n
d
iv
id
u
ally
,
th
e
r
eb
y
s
im
p
lify
in
g
d
esig
n
th
r
o
u
g
h
its
m
o
d
u
lar
s
tr
u
ctu
r
e.
T
h
ese
ADAL
I
NE
-
b
ased
ap
p
r
o
ac
h
es
h
av
e
b
ee
n
v
alid
ate
d
t
h
r
o
u
g
h
s
im
u
latio
n
s
an
d
ex
p
e
r
im
en
ts
u
n
d
er
v
a
r
io
u
s
co
n
d
itio
n
s
,
in
clu
d
i
n
g
s
in
u
s
o
id
al
s
o
u
r
ce
s
,
b
alan
ce
d
lo
ad
s
,
an
d
d
y
n
am
ic
s
tates
[
1
5
]
,
[
1
6
]
.
H
o
wev
er
,
th
e
in
c
r
ea
s
in
g
co
m
p
le
x
ity
o
f
wid
e
-
b
a
n
d
ADAL
I
NE
h
as
m
o
tiv
ated
th
e
ex
p
lo
r
atio
n
o
f
s
elec
tiv
e
co
m
p
en
s
atio
n
s
tr
ateg
ies.
B
y
tar
g
etin
g
t
h
e
m
o
s
t
p
r
o
b
lem
atic
h
a
r
m
o
n
ics
(
5
th
an
d
7
th
)
,
it
is
p
o
s
s
ib
le
t
o
s
ig
n
if
ican
tly
r
ed
u
ce
f
ilter
s
ize,
im
p
r
o
v
e
d
y
n
am
ics,
an
d
o
p
tim
ize
co
s
t [
1
7
]
,
[
1
8
]
.
T
o
en
h
a
n
ce
th
e
p
e
r
f
o
r
m
an
ce
o
f
ADAL
I
NE
-
b
ased
h
a
r
m
o
n
i
c
ex
tr
ac
tio
n
,
s
y
n
c
h
r
o
n
izer
s
s
u
ch
as
z
er
o
-
cr
o
s
s
in
g
d
etec
to
r
s
(
Z
C
Ds)
an
d
p
h
ase
-
lo
ck
ed
lo
o
p
s
(
PLL
s
)
ar
e
o
f
ten
r
eq
u
i
r
ed
[
1
9
]
,
[
2
0
]
.
T
h
ese
co
m
p
o
n
e
n
ts
alig
n
th
e
r
ef
er
e
n
ce
s
ig
n
al
p
h
a
s
e
with
th
at
o
f
th
e
elec
tr
ical
n
etwo
r
k
,
en
s
u
r
in
g
ac
cu
r
ate
f
u
n
d
am
en
tal
cu
r
r
en
t
esti
m
atio
n
.
Ho
wev
er
,
in
teg
r
ati
n
g
Z
C
Ds
an
d
PLL
s
ca
n
co
m
p
l
icate
s
y
s
tem
s
tr
u
ctu
r
e.
An
in
n
o
v
ativ
e
alter
n
ativ
e
is
th
e
u
n
if
ie
d
ADAL
I
NE
tech
n
iq
u
e,
w
h
ich
u
s
es
d
o
u
b
le
AD
AL
I
NE
s
tr
u
ctu
r
es
to
ex
tr
ac
t
f
u
n
d
am
e
n
tal
v
o
ltag
e
an
d
c
u
r
r
en
t
s
ig
n
als,
th
u
s
elim
in
atin
g
th
e
n
ee
d
f
o
r
a
d
d
itio
n
a
l
s
y
n
ch
r
o
n
izer
s
[
2
1
]
.
Alth
o
u
g
h
th
is
ap
p
r
o
ac
h
is
ef
f
ec
tiv
e
u
n
d
er
id
ea
l
co
n
d
itio
n
s
,
it
en
c
o
u
n
ter
s
d
if
f
icu
lties
wh
en
s
o
u
r
ce
v
o
ltag
es
ar
e
d
is
to
r
ted
o
r
u
n
b
ala
n
ce
d
.
T
o
ad
d
r
ess
th
is
,
tech
n
iq
u
es
s
u
ch
as
h
ig
h
l
y
s
elec
tiv
e
f
ilter
s
(
HSF)
an
d
s
elf
-
tu
n
in
g
f
ilter
s
(
STFs
)
h
av
e
b
ee
n
d
ev
elo
p
e
d
[
2
2
]
,
[
2
3
]
.
T
h
e
A
DAL
I
NE
tech
n
iq
u
e
b
ased
o
n
HSF
o
r
STFs
h
as
s
h
o
wn
p
r
o
m
is
in
g
r
esu
lts
u
n
d
er
v
ar
io
u
s
d
is
to
r
tio
n
an
d
im
b
alan
ce
co
n
d
itio
n
s
,
im
p
r
o
v
in
g
th
e
r
o
b
u
s
tn
ess
an
d
ac
cu
r
a
cy
o
f
h
ar
m
o
n
ic
ex
tr
ac
tio
n
.
R
ec
en
t
ad
v
an
ce
m
e
n
ts
h
av
e
f
o
cu
s
ed
o
n
m
o
d
if
y
in
g
th
e
ADAL
I
NE
lear
n
in
g
alg
o
r
ith
m
to
e
n
h
an
ce
its
p
er
f
o
r
m
an
ce
.
An
im
p
r
o
v
ed
v
ar
ian
t
o
f
th
e
latest,
th
e
v
ar
iab
le
s
tep
s
ize
leak
y
least
m
ea
n
s
q
u
ar
e
(
VSSLM
S)
alg
o
r
ith
m
,
was
in
tr
o
d
u
ce
d
in
[
2
4
]
.
T
h
is
alg
o
r
ith
m
d
y
n
a
m
ically
ad
ju
s
ts
th
e
s
tep
s
ize,
p
ar
ticu
lar
ly
d
u
r
in
g
co
n
v
er
g
en
ce
p
er
io
d
s
.
Ho
wev
er
,
ch
allen
g
es
r
em
ain
in
th
e
p
r
esen
ce
o
f
d
is
to
r
tio
n
s
o
r
v
o
ltag
e
im
b
alan
ce
s
.
T
o
ad
d
r
ess
th
ese
is
s
u
es,
L
y
ap
u
n
o
v
-
b
ased
ap
p
r
o
ac
h
es
h
av
e
b
ee
n
ex
p
lo
r
e
d
to
e
n
s
u
r
e
s
tab
ilit
y
a
n
d
ac
ce
ler
ate
L
MS
alg
o
r
ith
m
co
n
v
er
g
e
n
ce
[
2
5
]
.
Ad
d
itio
n
ally
,
s
o
m
e
wo
r
k
s
h
av
e
p
r
o
p
o
s
ed
co
m
p
letely
r
ep
lacin
g
th
e
L
MS
alg
o
r
ith
m
with
s
u
p
p
o
r
t
v
ec
t
o
r
m
ac
h
i
n
es
(
SVMs),
o
f
f
er
i
n
g
s
ig
n
if
ican
t
p
er
f
o
r
m
an
ce
g
ain
s
[
2
6
]
.
Hy
b
r
id
m
eth
o
d
s
h
a
v
e
also
b
ee
n
ex
p
l
o
r
ed
,
c
o
m
b
in
in
g
class
ical
tech
n
iq
u
es
s
u
ch
as
PQ
a
n
d
SR
F
th
eo
r
y
with
ad
ap
tiv
e
ADAL
I
NE
f
ilter
s
[
1
7
]
,
[
2
7
]
,
[
2
8
]
.
T
h
is
in
n
o
v
ativ
e
ap
p
r
o
ac
h
to
f
u
n
d
am
en
tal
co
m
p
o
n
en
t
ex
t
r
ac
tio
n
co
n
s
id
er
ab
ly
r
ed
u
ce
s
th
e
n
u
m
b
er
o
f
n
eu
r
al
n
etwo
r
k
s
an
d
p
a
r
am
eter
s
to
b
e
esti
m
ated
,
o
p
ti
m
izes
th
e
lear
n
in
g
tim
e
an
d
co
m
p
u
tatio
n
f
r
eq
u
en
cy
in
r
ea
l
-
tim
e
im
p
lem
en
tatio
n
s
,
an
d
in
cr
ea
s
es th
e
r
o
b
u
s
tn
e
s
s
o
f
th
ese
m
eth
o
d
s
.
Ho
wev
er
,
d
esp
ite
th
ese
ad
v
an
ce
s
,
th
e
ap
p
licatio
n
o
f
ADAL
I
NE
-
b
ased
alg
o
r
ith
m
s
in
f
o
u
r
-
wir
e
th
r
ee
-
p
h
ase
s
y
s
tem
s
r
em
ain
s
in
s
u
f
f
icien
tly
ex
p
lo
r
e
d
.
C
o
n
v
en
tio
n
al
m
eth
o
d
s
co
n
tin
u
e
to
b
e
th
e
m
o
s
t
co
m
m
o
n
ly
u
s
ed
,
d
esp
ite
th
eir
lim
itatio
n
s
an
d
d
is
ad
v
an
tag
es
[
2
9
]
.
T
h
e
r
ef
o
r
e,
d
e
v
elo
p
in
g
ADAL
I
NE
-
b
ased
tech
n
iq
u
es
an
d
ex
p
lo
r
in
g
th
eir
p
o
te
n
tial
in
f
o
u
r
-
wir
e
th
r
ee
-
p
h
ase
s
y
s
tem
s
,
co
u
ld
o
f
f
e
r
a
r
o
b
u
s
t
alter
n
ativ
e
to
ex
is
tin
g
m
eth
o
d
s
.
T
h
is
wo
u
l
d
im
p
r
o
v
e
th
e
co
n
tr
o
l
o
f
APFs
ac
r
o
s
s
a
wid
e
r
an
g
e
o
f
n
e
two
r
k
c
o
n
d
itio
n
s
.
T
h
is
ar
ticle
p
r
o
p
o
s
es
a
n
ew
ADAL
I
NE
-
b
ased
h
y
b
r
id
ap
p
r
o
ac
h
f
o
r
h
ar
m
o
n
ic
id
en
tific
ati
o
n
in
f
o
u
r
-
wir
e
n
etwo
r
k
s
,
aim
i
n
g
to
ad
d
r
ess
th
r
ee
m
ajo
r
ch
allen
g
es
:
ca
s
e
s
tu
d
y
:
ev
al
u
ate
th
e
im
p
ac
t
o
f
in
tr
o
d
u
cin
g
ADAL
I
NE
in
to
th
e
p
r
o
p
o
s
ed
m
eth
o
d
b
y
co
m
p
ar
in
g
its
ef
f
e
ctiv
en
ess
with
th
e
PQ
th
eo
r
y
,
wh
ich
is
r
en
o
wn
ed
f
o
r
its
s
u
p
er
io
r
p
e
r
f
o
r
m
an
ce
.
Op
tim
al
h
ar
m
o
n
ic
d
etec
tio
n
:
d
ev
elo
p
a
n
id
en
tific
atio
n
te
ch
n
iq
u
e
ca
p
ab
le
o
f
ef
f
ec
tiv
ely
d
etec
tin
g
a
ll
ty
p
es
o
f
h
ar
m
o
n
ics,
r
eg
ar
d
less
o
f
n
etwo
r
k
d
is
to
r
tio
n
an
d
/o
r
im
b
alan
ce
co
n
d
itio
n
s
.
Pre
cise
id
en
tific
atio
n
en
ab
les
m
o
r
e
ef
f
ec
tiv
e
co
m
p
en
s
atio
n
,
b
r
in
g
i
n
g
th
e
to
tal
h
ar
m
o
n
i
c
d
is
to
r
tio
n
(
T
HD)
clo
s
er
to
ze
r
o
.
Op
tim
al
p
o
wer
q
u
ality
: e
x
tr
ac
t
p
r
ec
is
e
r
ef
er
en
ce
c
u
r
r
en
ts
to
co
m
p
en
s
ate
f
o
r
h
ar
m
o
n
ic
cu
r
r
en
ts
,
n
eu
tr
al
cu
r
r
e
n
ts
,
an
d
r
ea
ctiv
e
p
o
wer
,
th
er
e
b
y
en
s
u
r
i
n
g
s
m
o
o
th
an
d
s
tab
le
p
o
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r
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co
m
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a
d
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d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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52
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DA
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37
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n
s
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s
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s
tr
u
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to
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etails
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1
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ates
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at
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n
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as
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n
t
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m
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ts
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h
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r
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3
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.
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ase
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)
an
d
lo
ad
cu
r
r
en
ts
(
,
,
an
d
)
,
as illu
s
tr
ated
b
y
(
1
)
.
(
)
=
[
]
[
]
(
1
)
Nex
t,
th
e
av
er
ag
e
v
alu
e
is
d
et
er
m
in
ed
b
y
ap
p
ly
in
g
(
)
to
a
lo
w
-
p
ass
f
ilter
.
T
h
u
s
,
th
e
r
ea
l p
o
wer
is
d
is
tr
ib
u
ted
am
o
n
g
th
e
th
r
ee
p
h
ases
ac
co
r
d
in
g
to
th
e
(
2
)
:
=
(
+
+
)
,
=
(
+
+
)
,
=
(
+
+
)
(
2
)
wh
er
e
,
,
an
d
ar
e
th
e
a
m
p
li
tu
d
es
o
f
,
,
a
n
d
r
esp
ec
tiv
ely
.
Fro
m
th
ese
v
alu
es,
t
h
e
b
alan
ce
d
lin
e
c
u
r
r
en
ts
ca
n
b
e
d
eter
m
in
ed
as
(
3
)
:
(
)
=
2
(
2
)
,
(
)
=
2
(
2
)
,
(
)
=
2
(
2
)
(
3
)
f
in
ally
,
th
e
r
e
f
er
en
ce
c
u
r
r
e
n
ts
ar
e
d
ef
in
e
d
b
y
(
4
)
-
(
6
)
.
(
)
=
(
)
−
(
)
(
4
)
(
)
=
(
)
−
(
)
(
5
)
(
)
=
(
)
−
(
)
(
6
)
Desp
ite
its
s
im
p
licity
an
d
r
o
b
u
s
tn
ess
,
th
e
ad
o
p
tio
n
o
f
SD
M
in
APFs
r
em
ain
s
lim
ited
d
u
e
to
its
p
er
ce
iv
ed
lo
wer
ac
cu
r
ac
y
c
o
m
p
ar
ed
to
o
th
e
r
tr
a
d
itio
n
al
m
eth
o
d
s
.
Stu
d
ies
[
3
1
]
,
[
3
2
]
h
a
v
e
p
ar
ticu
lar
ly
h
ig
h
lig
h
ted
lim
itatio
n
s
in
ter
m
s
o
f
T
HD
r
ed
u
ctio
n
co
m
p
ar
ed
to
tech
n
iq
u
es
s
u
ch
as
PQ
t
h
eo
r
y
.
Ad
d
itio
n
ally
,
SDM
h
as
co
n
s
tr
ain
ts
r
elate
d
t
o
r
e
s
p
o
n
s
e
tim
e
an
d
s
tab
ilit
y
.
W
h
ile
PQ
th
eo
r
y
allo
ws
f
o
r
co
m
p
en
s
atio
n
in
a
s
in
g
le
cy
cle,
SDM
ca
lcu
lat
i
o
n
s
ca
n
ex
ten
d
o
v
er
s
ev
er
al
cy
cles
(
u
p
to
f
o
u
r
teen
at
5
0
Hz)
[
3
0
]
.
T
h
ese
lim
itatio
n
s
p
ar
tly
ex
p
lain
th
e
r
elativ
e
lack
o
f
in
ter
est
f
r
o
m
th
e
s
cien
ti
f
ic
co
m
m
u
n
it
y
in
th
is
m
eth
o
d
.
T
o
ad
d
r
ess
th
ese
lim
itatio
n
s
wh
ile
r
etain
in
g
th
e
ad
v
a
n
tag
e
s
o
f
SDM,
we
p
r
o
p
o
s
e
r
ep
laci
n
g
th
e
co
n
v
en
tio
n
al
lo
w
-
p
ass
f
ilter
,
k
n
o
wn
f
o
r
its
s
en
s
itiv
ity
to
n
o
is
e,
with
an
ADAL
I
NE
f
ilter
.
T
h
e
lear
n
in
g
an
d
a
d
ap
tatio
n
ca
p
ab
ilit
ies
o
f
ADAL
I
NE
s
h
o
u
ld
s
ig
n
if
ican
tly
im
p
r
o
v
e
t
h
e
p
r
ec
is
io
n
an
d
c
o
n
v
e
r
g
en
c
e
s
p
ee
d
o
f
SDM.
A
d
etailed
d
escr
ip
tio
n
o
f
A
DAL
I
NE
will
b
e
p
r
esen
ted
i
n
th
e
f
o
llo
win
g
s
ec
tio
n
.
Fig
u
r
e
2
illu
s
tr
ates
th
e
s
tr
u
ctu
r
e
o
f
t
h
e
SDM
in
co
r
p
o
r
atin
g
ADAL
I
NE
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
A
DA
LINE
-
b
a
s
ed
s
yn
ch
r
o
n
o
u
s
d
etec
tio
n
fo
r
en
h
a
n
ce
d
s
h
u
n
t
A
P
F
p
erfo
r
ma
n
ce
(
A
b
d
ess
la
m
R
ya
d
Meb
a
r
ek
)
39
Fig
u
r
e
2
.
Pro
p
o
s
ed
ADAL
I
NE
-
b
ased
SDM
t
o
p
o
lo
g
y
2
.
3
.
Ada
ptiv
e
lin
ea
r
neuro
n
e
(
ADALI
N
E
)
T
h
e
ADAL
I
NE
is
a
s
im
p
le
y
et
p
o
wer
f
u
l
n
eu
r
al
n
etwo
r
k
i
n
tr
o
d
u
ce
d
b
y
B
er
n
ar
d
W
id
r
o
w
an
d
T
ed
Ho
f
f
in
1
9
6
0
.
T
h
is
n
etwo
r
k
is
wid
ely
u
s
ed
in
s
ig
n
al
p
r
o
ce
s
s
in
g
a
n
d
a
d
ap
tiv
e
f
i
lter
in
g
ap
p
licatio
n
s
,
p
ar
ticu
lar
ly
f
o
r
th
e
esti
m
atio
n
o
f
f
r
eq
u
en
cy
,
f
u
n
d
am
en
tal
,
an
d
h
ar
m
o
n
ic
co
m
p
o
n
e
n
ts
.
I
t
is
ca
p
ab
le
o
f
h
an
d
lin
g
b
o
th
lin
ea
r
an
d
n
o
n
l
in
ea
r
s
ig
n
als
[
1
8
]
,
[
3
3
]
.
T
h
e
ADAL
I
NE
co
n
s
is
ts
o
f
a
s
in
g
le
lay
er
o
f
n
eu
r
o
n
s
u
s
in
g
a
lin
ea
r
ac
tiv
atio
n
f
u
n
ctio
n
.
I
ts
o
p
er
atio
n
is
b
ased
o
n
th
e
weig
h
tin
g
an
d
s
u
m
m
atio
n
o
f
in
p
u
ts
.
E
ac
h
in
p
u
t
co
m
p
o
n
en
t
«
»
is
m
u
ltip
lied
b
y
a
co
r
r
esp
o
n
d
i
n
g
s
y
n
ap
tic
weig
h
t
«
»
,
f
o
r
m
in
g
a
weig
h
ted
p
r
o
d
u
ct
«
»
.
T
h
e
s
u
m
o
f
th
ese
weig
h
ted
p
r
o
d
u
cts
co
n
s
titu
tes
th
e
esti
m
ated
o
u
tp
u
t
«
»
.
T
h
e
weig
h
ts
ar
e
ad
ju
s
ted
d
u
r
in
g
th
e
lear
n
in
g
p
r
o
ce
s
s
,
to
m
in
im
ize
th
e
er
r
o
r
«
»
b
etwe
en
th
e
d
esire
d
o
u
tp
u
t «
»
an
d
t
h
e
esti
m
ated
o
u
tp
u
t
«
»
.
T
h
e
s
tr
u
ctu
r
e
o
f
th
e
p
r
o
p
o
s
ed
ADAL
I
NE
is
p
r
esen
ted
in
F
ig
u
r
e
3
.
I
n
o
u
r
s
tu
d
y
,
we
in
teg
r
a
ted
an
ADAL
I
NE
with
th
e
SDM
to
im
p
r
o
v
e
t
h
e
ac
cu
r
ac
y
o
f
r
ea
l
p
o
wer
f
ilter
in
g
an
d
e
f
f
icien
tly
ex
t
r
a
ct
th
e
f
u
n
d
am
en
tal
co
m
p
o
n
en
t.
I
t
was
im
p
lem
en
ted
in
MA
T
L
AB
/Si
m
u
lin
k
u
s
in
g
th
e
"S
-
Fu
n
ctio
n
B
u
ild
er
"
b
l
o
ck
a
n
d
p
r
o
g
r
a
m
m
ed
i
n
th
e
C
lan
g
u
a
g
e.
T
o
ad
a
p
t
th
e
ADAL
I
NE
t
o
th
e
n
o
n
-
lin
ea
r
ity
o
f
o
u
r
s
y
s
tem
a
n
d
d
eter
m
in
e
its
p
ar
am
ete
r
s
,
we
u
s
ed
t
h
e
Fo
u
r
ier
s
er
ies
d
ec
o
m
p
o
s
itio
n
o
f
th
e
s
tu
d
ied
s
ig
n
al.
T
h
is
d
ec
o
m
p
o
s
itio
n
allo
ws
ex
p
r
ess
in
g
th
e
s
ig
n
al
as
a
s
u
m
o
f
s
in
u
s
o
id
s
at
m
u
ltip
les
o
f
th
e
f
u
n
d
am
en
tal
f
r
eq
u
en
cy
.
T
h
e
f
o
u
r
ier
c
o
ef
f
icien
ts
co
r
r
esp
o
n
d
i
n
g
to
th
e
f
r
eq
u
e
n
cies
o
f
in
ter
est
c
os
(
2
)
an
d
s
in
(
2
)
ar
e
u
s
ed
as
in
p
u
ts
to
th
e
ADAL
I
NE
.
T
h
e
am
p
litu
d
es
o
f
th
ese
co
m
p
o
n
e
n
ts
an
d
,
wh
ich
we
aim
to
esti
m
ate,
ar
e
co
n
s
id
er
ed
as th
e
s
y
n
ap
tic
weig
h
ts
t
o
b
e
ad
ju
s
ted
.
T
h
e
f
o
u
r
ier
s
er
ies d
ec
o
m
p
o
s
itio
n
eq
u
atio
n
is
p
r
esen
ted
as
(
7
)
:
=
∑
[
(
2
)
+
(
2
)
]
∞
=
1
,
2
(
7
)
T
h
e
esti
m
ated
o
u
tp
u
t «
»
ca
n
b
e
ex
p
r
ess
ed
b
y
(
8
)
:
=
∑
(
.
)
∞
=
1
,
2
=
.
(
8
)
T
h
e
in
p
u
t
v
ec
to
r
«
»
is
d
ef
in
ed
as (
9
)
:
=
[
1
s
in
(
2
)
c
os
(
2
)
s
in
(
4
)
c
os
(
4
)
…
s
in
(
)
c
os
(
)
]
(
9
)
W
h
ile
th
e
weig
h
t v
ec
to
r
«
»
is
g
iv
en
as
(
1
0
)
:
=
[
0
1
2
3
4
…
−
1
]
(
10
)
T
o
ad
ju
s
t
th
e
s
y
n
ap
tic
weig
h
ts
,
we
u
s
e
th
e
least
m
ea
n
s
q
u
ar
es
(
LMS
)
lear
n
in
g
alg
o
r
ith
m
.
T
h
is
alg
o
r
ith
m
u
p
d
ates
th
e
weig
h
ts
at
ea
ch
iter
atio
n
b
ased
o
n
th
e
q
u
ad
r
atic
er
r
o
r
b
etwe
en
th
e
d
esire
d
/r
ea
l
o
u
tp
u
t
a
n
d
th
e
esti
m
ated
o
u
tp
u
t.
T
h
e
u
p
d
ate
r
u
le
is
g
iv
en
b
y
(
1
1
)
.
(
+
1
)
=
(
)
+
.
(
)
.
(
)
(
11
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
1
,
J
an
u
ar
y
20
25
:
35
-
47
40
W
h
er
e:
(
+
1
)
is
th
e
u
p
d
ated
weig
h
t
v
ec
to
r
at
iter
atio
n
k
+
1
,
(
)
i
s
th
e
weig
h
t
v
ec
to
r
at
iter
atio
n
k
,
is
th
e
lear
n
in
g
r
at
e,
(
)
is
th
e
er
r
o
r
b
etwe
en
t
h
e
d
e
s
ir
ed
o
u
tp
u
t
a
n
d
th
e
esti
m
ated
o
u
tp
u
t.
I
t
is
eq
u
al
to
(
−
)
.
Fin
aly
,
(
)
is
th
e
in
p
u
t
v
ec
to
r
at
iter
atio
n
k
.
T
h
e
le
ar
n
in
g
p
r
o
ce
s
s
co
n
tin
u
es
u
n
til
th
e
er
r
o
r
c
o
n
v
e
r
g
es
to
a
m
in
im
u
m
,
in
d
icatin
g
th
at
th
e
m
o
d
el
h
as
lear
n
ed
t
h
e
o
p
tim
al
weig
h
ts
f
o
r
th
e
g
iv
en
task
.
T
h
e
esti
m
ated
f
u
n
d
am
en
tal
p
o
wer
is
th
en
ev
alu
ated
as
(
1
2
)
:
(
)
=
1
s
in
+
1
c
os
(
12
)
Fig
u
r
e
3
.
Pro
p
o
s
ed
ADAL
I
NE
to
p
o
lo
g
y
2
.
4
.
I
ns
t
a
nta
neo
us
po
wer
t
heo
ry
On
e
wid
ely
u
s
ed
m
eth
o
d
f
o
r
h
ar
m
o
n
ic
id
e
n
tific
atio
n
i
s
th
e
I
PT
,
also
k
n
o
wn
as
p
-
q
th
eo
r
y
.
T
h
is
tech
n
iq
u
e
is
ex
ten
s
iv
ely
ap
p
lied
f
o
r
an
aly
zi
n
g
a
n
d
c
o
n
tr
o
llin
g
p
o
wer
s
y
s
tem
s
,
p
a
r
ticu
lar
ly
in
APFs
[
5
]
,
[
3
4
]
.
Dev
elo
p
e
d
b
y
Ak
ag
i
,
Kan
az
awa
,
an
d
Nab
ae
in
1
9
8
3
[
3
5
]
,
I
PT
h
as b
ec
o
m
e
a
co
r
n
er
s
to
n
e
in
th
e
f
ield
o
f
p
o
wer
q
u
ality
im
p
r
o
v
em
e
n
t.
T
h
e
co
r
e
co
n
ce
p
t
o
f
I
PT
in
v
o
lv
es
tr
an
s
f
o
r
m
in
g
th
r
ee
-
p
h
ase
v
o
ltag
es
an
d
cu
r
r
en
ts
f
r
o
m
th
e
tim
e
d
o
m
ain
to
th
e
in
s
tan
tan
eo
u
s
p
o
wer
d
o
m
ain
u
s
in
g
th
e
C
l
ar
k
e
tr
an
s
f
o
r
m
atio
n
.
T
h
is
tr
an
s
f
o
r
m
atio
n
,
as sh
o
wn
in
(
1
3
)
,
co
n
v
er
ts
v
o
lta
g
es a
n
d
cu
r
r
en
ts
f
r
o
m
th
e
a
b
c
f
r
a
m
e
t
o
th
e
α
β f
r
a
m
e.
[
]
=
√
2
3
[
1
−
1
2
−
1
2
0
√
3
2
−
√
3
2
]
[
]
a
n
d
[
]
=
√
2
3
[
1
−
1
2
−
1
2
0
√
3
2
−
√
3
2
]
[
]
(1
3
)
T
h
is
tr
an
s
f
o
r
m
atio
n
allo
ws
th
e
s
ep
ar
atio
n
o
f
th
e
p
o
wer
co
m
p
o
n
e
n
ts
in
to
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
wer
,
wh
ich
ca
n
th
en
b
e
f
u
r
t
h
er
an
aly
ze
d
t
o
id
e
n
tify
h
ar
m
o
n
ic
d
is
to
r
tio
n
s
an
d
im
b
alan
ce
s
in
th
e
s
y
s
tem
.
T
h
e
in
s
tan
tan
eo
u
s
ac
tiv
e
p
o
w
er
(
)
an
d
i
n
s
tan
tan
eo
u
s
r
ea
ctiv
e
p
o
wer
(
)
ar
e
g
iv
en
in
(
1
4
)
:
[
]
=
[
−
]
[
]
(
14
)
a
f
ter
ap
p
ly
in
g
lo
w
-
p
ass
f
ilter
i
n
g
to
th
e
p
r
e
v
io
u
s
ly
o
b
tain
ed
p
o
wer
s
,
th
e
r
ef
e
r
en
ce
c
u
r
r
e
n
ts
to
b
e
s
u
p
p
lied
b
y
th
e
SAPF
in
th
e
two
-
p
h
ase
(
αβ
)
f
r
am
e
ar
e
g
iv
en
b
y
(
1
5
)
:
[
]
=
1
2
+
2
[
−
]
[
]
(
15
)
t
o
elim
in
ate
h
ar
m
o
n
ics an
d
r
e
ac
tiv
e
p
o
wer
,
th
e
u
n
wan
te
d
p
o
wer
s
ch
o
s
en
to
b
e
elim
in
ate
d
ar
e
g
iv
en
i
n
(
4
)
:
{
=
̃
=
̅
+
̃
(
16
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
A
DA
LINE
-
b
a
s
ed
s
yn
ch
r
o
n
o
u
s
d
etec
tio
n
fo
r
en
h
a
n
ce
d
s
h
u
n
t
A
P
F
p
erfo
r
ma
n
ce
(
A
b
d
ess
la
m
R
ya
d
Meb
a
r
ek
)
41
w
h
er
e
̃
is
th
e
h
ar
m
o
n
ic
ac
ti
v
e
p
o
wer
,
a
n
d
̅
an
d
̃
a
r
e
th
e
co
n
tin
u
o
u
s
an
d
h
ar
m
o
n
ic
r
ea
ctiv
e
p
o
wer
,
r
esp
ec
tiv
ely
.
T
h
u
s
,
f
r
o
m
(
1
6
)
,
th
e
cu
r
r
e
n
t r
ef
er
e
n
ce
s
in
(
1
5
)
will b
e
as sh
o
wn
in
(
1
7
)
:
[
]
=
1
2
+
2
[
−
]
[
̃
̅
+
̃
]
(
1
7
)
f
in
ally
,
th
e
in
v
er
s
e
C
lar
k
e
tr
an
s
f
o
r
m
atio
n
will b
e
ap
p
lied
to
(
1
7
)
to
o
b
tain
th
e
cu
r
r
e
n
t r
ef
e
r
e
n
ce
s
in
th
e
ab
c
f
r
am
e.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
p
r
esen
ts
an
d
an
aly
ze
s
th
e
r
esu
lts
o
f
th
e
n
u
m
er
i
ca
l
s
im
u
latio
n
s
o
f
th
e
elec
tr
ical
s
y
s
tem
,
d
etailed
in
s
ec
tio
n
2
.
T
h
ese
s
im
u
latio
n
s
wer
e
co
n
d
u
cted
b
y
co
m
p
ar
in
g
two
id
en
tific
atio
n
m
eth
o
d
s
:
th
e
SDM
b
ased
o
n
an
ADAL
I
NE
alg
o
r
i
th
m
an
d
th
e
tr
ad
itio
n
al
PQ
m
e
th
o
d
u
s
in
g
a
3
0
Hz
lo
w
-
p
ass
f
ilter
.
T
h
e
o
b
jectiv
e
was
to
v
alid
ate
th
e
h
y
p
o
t
h
eses
f
o
r
m
u
lated
in
t
h
e
in
tr
o
d
u
ctio
n
.
T
h
e
e
v
alu
atio
n
f
o
c
u
s
es
o
n
s
ev
er
al
p
er
f
o
r
m
a
n
ce
in
d
icato
r
s
:
th
e
T
HD
r
ate,
th
e
am
p
litu
d
es
o
f
th
e
3
rd
,
5
th
,
a
n
d
7
th
o
r
d
er
h
ar
m
o
n
ics,
th
e
s
o
u
r
ce
an
d
n
e
u
tr
a
l
cu
r
r
en
ts
,
as we
ll a
s
th
e
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
wer
d
eliv
e
r
ed
b
y
ea
ch
m
et
h
o
d
.
T
h
is
co
m
p
a
r
ativ
e
an
aly
s
is
allo
ws
f
o
r
a
n
ap
p
r
ec
iatio
n
o
f
th
e
ad
v
an
tag
es
an
d
lim
itatio
n
s
o
f
e
ac
h
ap
p
r
o
ac
h
with
in
th
e
co
n
tex
t
o
f
o
u
r
s
tu
d
y
.
Fig
u
r
e
4
illu
s
tr
ates
th
e
s
im
u
latio
n
r
esu
lts
o
f
th
e
lo
ad
c
u
r
r
e
n
ts
,
n
eu
tr
al
cu
r
r
e
n
t,
an
d
ac
tiv
e
a
n
d
r
ea
ctiv
e
p
o
wer
,
u
n
d
er
t
h
e
lo
ad
c
o
n
d
itio
n
s
o
f
s
ce
n
ar
io
(
1
)
,
m
ain
g
r
id
,
b
ef
o
r
e
co
m
p
en
s
atio
n
.
T
h
ese
r
esu
lts
clea
r
ly
s
h
o
w
th
at
th
e
lo
ad
cu
r
r
en
ts
ar
e
h
ig
h
ly
d
is
to
r
ted
as
s
h
o
wn
in
Fig
u
r
e
4
(
a)
,
d
u
e
to
t
h
e
p
r
esen
ce
o
f
a
th
r
ee
-
p
h
ase
n
o
n
lin
ea
r
lo
ad
.
Ad
d
itio
n
ally
,
t
h
e
in
tr
o
d
u
ctio
n
o
f
an
a
d
d
itio
n
al
s
in
g
le
-
p
h
ase
lo
ad
co
n
n
ec
ted
to
p
h
a
s
e
(
b
)
r
esu
lts
in
a
s
ig
n
if
ican
t
in
cr
ea
s
e
in
th
e
cu
r
r
en
t
am
p
litu
d
e
in
th
is
p
h
ase,
ca
u
s
in
g
a
s
u
b
s
tan
tial
im
b
al
an
ce
b
etwe
en
th
e
p
h
ases
.
Mo
r
eo
v
e
r
,
th
e
n
e
u
tr
a
l
cu
r
r
en
t
r
ea
c
h
es
h
ig
h
v
alu
e
s
(
2
0
A)
an
d
ex
h
ib
its
s
ig
n
if
ic
an
t
o
s
cillatio
n
s
as
s
h
o
wn
in
Fig
u
r
e
4
(
b
)
.
T
h
ese
d
is
tu
r
b
an
ce
s
h
av
e
a
d
ir
ec
t im
p
a
ct
o
n
p
o
wer
,
b
o
th
ac
tiv
e
an
d
r
ea
ctiv
e
as sh
o
wn
in
Fig
u
r
e
4
(
c)
,
wh
ich
s
h
o
ws
f
lu
c
tu
atio
n
s
an
d
a
lack
o
f
s
tab
ilit
y
.
T
h
e
p
o
wer
f
ac
t
o
r
,
r
e
d
u
ce
d
to
0
.
8
4
8
0
,
h
ig
h
lig
h
ts
th
e
im
p
o
r
tan
ce
o
f
h
ar
m
o
n
ics in
th
is
ca
s
e.
(
a)
(
b
)
(
c)
Fig
u
r
e
4
.
B
ef
o
r
e
co
m
p
en
s
atio
n
s
ig
n
als in
ca
s
e
(
1
)
: (
a
)
lo
ad
c
u
r
r
en
ts
,
(
b
)
n
e
u
tr
al
cu
r
r
en
t,
an
d
(
c)
ac
tiv
e
p
o
wer
(
b
l
u
e)
an
d
r
e
ac
tiv
e
p
o
wer
(
r
ed
)
Fig
u
r
e
5
p
r
esen
ts
a
d
etailed
c
o
m
p
ar
is
o
n
o
f
th
e
r
esu
lts
o
b
tai
n
ed
af
ter
co
m
p
e
n
s
atin
g
th
e
d
is
tu
r
b
an
ce
s
u
s
in
g
th
e
two
m
eth
o
d
s
s
tu
d
ied
:
th
e
class
ical
PQ
th
eo
r
y
(
lef
t c
o
lu
m
n
)
an
d
th
e
n
e
u
r
al
SDM
(
r
ig
h
t
c
o
lu
m
n
)
.
T
h
e
q
u
an
titi
es
co
m
p
ar
ed
in
cl
u
d
e
t
h
e
s
o
u
r
ce
cu
r
r
en
ts
,
th
e
p
h
ase
s
h
if
t
b
etwe
en
th
e
s
o
u
r
ce
v
o
ltag
e
an
d
cu
r
r
e
n
t,
th
e
n
eu
tr
al
cu
r
r
en
t,
as
well
as
th
e
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
wer
.
T
h
is
co
m
p
ar
ativ
e
an
aly
s
is
ev
alu
ates
th
e
ad
v
a
n
tag
es
an
d
lim
itatio
n
s
o
f
ea
ch
ap
p
r
o
ac
h
in
ter
m
s
o
f
co
m
p
en
s
atio
n
q
u
a
lity
an
d
en
e
r
g
y
ef
f
icie
n
cy
.
B
o
th
m
eth
o
d
s
en
s
u
r
e
ef
f
ec
tiv
e
a
n
d
alm
o
s
t
id
en
tical
co
m
p
en
s
atio
n
,
p
r
o
d
u
cin
g
s
in
u
s
o
id
al
a
n
d
b
ala
n
ce
d
s
o
u
r
ce
cu
r
r
en
t
wav
ef
o
r
m
s
with
a
1
2
0
-
d
e
g
r
ee
p
h
ase
s
h
if
t
b
etwe
en
th
e
th
r
ee
p
h
ases
as
s
h
o
wn
in
Fig
u
r
es
5
(
a)
an
d
5
(
a
’
)
,
an
d
in
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
1
,
J
an
u
ar
y
20
25
:
35
-
47
42
p
h
ase
with
th
e
v
o
ltag
e
as
s
h
o
wn
in
Fig
u
r
es
5
(
b
)
an
d
5
(
b
’
)
,
r
esu
ltin
g
i
n
a
p
o
wer
f
a
cto
r
clo
s
e
to
u
n
ity
.
Ho
wev
er
,
th
e
SDM
s
tan
d
s
o
u
t
f
r
o
m
th
e
PQ
th
eo
r
y
in
two
as
p
ec
ts
:
f
ir
s
t,
b
y
a
h
i
g
h
er
s
o
u
r
ce
cu
r
r
e
n
t
am
p
litu
d
e,
r
ea
ch
in
g
4
0
A
c
o
m
p
a
r
ed
to
2
0
A
f
o
r
th
e
PQ
th
eo
r
y
;
s
ec
o
n
d
,
b
y
a
p
er
f
ec
t
b
alan
ce
o
f
th
ese
c
u
r
r
en
t
am
p
litu
d
es,
u
n
lik
e
a
s
lig
h
t
p
er
s
is
ten
t
im
b
a
lan
ce
in
th
e
am
p
litu
d
e
o
f
p
h
ase
(
b
)
o
f
a
b
o
u
t
4
%
c
o
m
p
a
r
ed
to
th
e
o
th
e
r
p
h
ases
.
B
o
th
m
eth
o
d
s
e
n
s
u
r
e
a
n
eg
li
g
ib
le
n
eu
tr
al
cu
r
r
e
n
t
as
s
h
o
wn
in
Fig
u
r
es
5
(
c)
a
n
d
5
(
c
’
)
.
R
e
g
ar
d
in
g
ac
tiv
e
a
n
d
r
ea
ctiv
e
p
o
wer
as
s
h
o
w
n
in
Fi
g
u
r
es
5
(
d
)
an
d
5
(
d
’
)
,
b
o
t
h
m
e
th
o
d
s
s
u
cc
ee
d
in
f
u
lly
c
o
m
p
e
n
s
atin
g
th
e
r
ea
ctiv
e
p
o
wer
.
Fo
r
ac
tiv
e
p
o
wer
,
t
h
e
PQ
th
eo
r
y
s
h
o
ws
b
etter
s
tab
ilit
y
d
u
r
in
g
th
e
tr
an
s
itio
n
p
er
io
d
b
u
t
ex
h
i
b
its
r
esid
u
al
f
lu
ctu
atio
n
s
o
f
a
b
o
u
t
2
.
2
%
ar
o
u
n
d
th
e
a
v
er
ag
e
v
a
lu
e
in
s
tead
y
-
s
tate.
I
n
co
n
t
r
ast,
th
e
n
eu
r
al
SDM
m
eth
o
d
,
alth
o
u
g
h
d
is
tu
r
b
e
d
d
u
r
in
g
th
e
t
r
an
s
ien
t
p
h
ase,
o
f
f
e
r
s
p
er
f
ec
t
s
tab
ilit
y
an
d
s
m
o
o
t
h
ac
tiv
e
p
o
wer
i
n
a
s
tead
y
s
tate,
with
an
a
v
er
ag
e
am
p
litu
d
e
o
f
1
5
k
W
.
T
h
e
r
e
s
p
o
n
s
e
tim
e
o
f
b
o
th
m
eth
o
d
s
is
s
im
ilar
,
ar
o
u
n
d
0
.
0
4
s
,
wh
ich
m
ar
k
s
an
im
p
r
o
v
em
en
t
in
SDM
p
er
f
o
r
m
an
c
e
co
m
p
a
r
ed
to
t
h
e
r
esu
lts
r
e
p
o
r
ted
i
n
[
3
0
]
.
T
h
is
s
u
p
er
io
r
s
tab
ilit
y
o
f
t
h
e
SDM
is
cr
u
cial
f
o
r
a
p
p
licatio
n
s
r
eq
u
ir
in
g
a
h
i
g
h
-
q
u
ality
elec
tr
ical
p
o
wer
s
u
p
p
ly
.
Fig
u
r
e
5
.
Po
s
t
-
co
m
p
en
s
atio
n
s
ig
n
al
in
ca
s
e
(
1
)
: so
u
r
ce
cu
r
r
e
n
t
(
a)
PQ th
eo
r
y
,
(a
’
)
SDM
; Ph
a
s
e
b
etwe
en
v
o
ltag
e
an
d
cu
r
r
en
t
,
(
b
)
PQ th
eo
r
y
,
(b
’
)
SDM
;
n
e
u
tr
al
cu
r
r
e
n
t
,
(
c
)
PQ th
eo
r
y
,
(c
’
)
SDM
,
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
w
er
,
an
d
(
d
)
PQ
th
eo
r
y
,
(d
’
)
SDM
Ho
wev
er
,
alth
o
u
g
h
th
ese
g
r
ap
h
ical
r
esu
lts
s
ee
m
to
f
av
o
r
th
e
p
r
o
p
o
s
ed
SDM,
th
ey
ar
e
n
o
t
s
u
f
f
icien
t
to
p
r
o
v
e
its
s
u
p
er
io
r
ity
o
v
e
r
t
h
e
PQ
th
eo
r
y
.
A
d
etailed
s
tu
d
y
o
f
t
h
e
h
a
r
m
o
n
ic
s
p
ec
tr
u
m
,
p
ar
ticu
lar
ly
th
e
T
HD
r
ate
as
well
as
th
e
3
rd
,
5
th
,
an
d
7
th
o
r
d
er
h
ar
m
o
n
ics,
is
th
er
ef
o
r
e
n
ec
ess
ar
y
.
T
ab
le
3
s
u
m
m
ar
izes
th
ese
r
esu
lt
s
b
ef
o
r
e
a
n
d
af
te
r
co
m
p
en
s
atio
n
,
allo
win
g
f
o
r
m
o
r
e
r
o
b
u
s
t
c
o
n
clu
s
io
n
s
r
eg
a
r
d
in
g
th
e
o
v
er
all
p
er
f
o
r
m
a
n
ce
o
f
ea
ch
m
eth
o
d
.
T
h
e
two
id
en
tific
atio
n
m
eth
o
d
s
ex
p
lo
r
ed
in
th
is
s
tu
d
y
ac
h
iev
e
T
HD
v
alu
es
co
m
p
lian
t
with
th
e
IEEE
-
5
1
9
s
tan
d
ar
d
.
Ho
wev
er
,
th
eir
ef
f
e
ctiv
en
ess
v
ar
ies,
as
s
h
o
wn
in
T
ab
le
3
.
W
h
ile
th
e
PQ
th
eo
r
y
s
h
o
ws
a
p
r
o
m
is
in
g
r
e
d
u
ctio
n
in
T
HD,
th
e
n
eu
r
al
SDM
co
n
s
is
ten
tly
o
f
f
er
s
th
e
m
o
s
t
im
p
r
ess
iv
e
r
esu
lts
,
ac
h
iev
in
g
a
s
ig
n
if
ican
tly
lo
wer
T
HD
th
an
th
e
PQ
th
eo
r
y
.
T
h
is
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
also
ex
ten
d
s
t
o
t
h
e
r
ed
u
ctio
n
o
f
3
rd
,
5
th
,
an
d
7
th
o
r
d
e
r
h
ar
m
o
n
ics.
T
h
e
n
eu
r
al
SDM
co
n
s
is
ten
tly
ac
h
iev
es
T
HD
p
er
ce
n
tag
es
b
elo
w
0
.
5
%
f
o
r
all
th
r
ee
p
h
ases
.
T
h
e
T
HD
is
r
ed
u
ce
d
f
r
o
m
2
6
.
9
0
%
t
o
ju
s
t
0
.
3
6
%
f
o
r
p
h
ase
(
a)
,
f
r
o
m
1
4
.
3
0
%
to
0
.
3
7
%
f
o
r
p
h
ase
(
b
)
,
an
d
f
r
o
m
2
6
.
7
2
%
to
0
.
3
6
%
f
o
r
p
h
ase
(
c)
.
T
h
e
5
th
an
d
7
th
o
r
d
er
o
d
d
h
ar
m
o
n
ics,
wh
ich
ar
e
th
e
m
o
s
t
h
ar
m
f
u
l
in
th
is
ty
p
e
o
f
s
y
s
te
m
,
co
m
p
ly
with
r
elev
an
t
s
tan
d
ar
d
s
,
with
v
alu
es
b
elo
w
0
.
1
%.
Similar
ly
,
th
e
3
rd
o
r
d
er
h
ar
m
o
n
ic,
wh
ich
ap
p
ea
r
s
o
n
ly
in
th
e
u
n
b
alan
ce
d
p
h
ase
(
b
)
,
is
r
ed
u
ce
d
f
r
o
m
6
.
1
6
%
to
a
r
em
ar
k
ab
le
0
.
0
3
%.
T
h
ese
r
esu
lts
h
ig
h
lig
h
t
th
e
ef
f
ec
tiv
en
ess
o
f
ADAL
I
NE
in
im
p
r
o
v
in
g
th
e
ac
cu
r
ac
y
an
d
r
o
b
u
s
tn
ess
o
f
th
e
SDM,
also
d
e
m
o
n
s
tr
atin
g
th
e
s
u
p
er
io
r
ab
ilit
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o
f
th
e
n
eu
r
al
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to
m
itig
ate
h
a
r
m
o
n
ics
an
d
im
p
r
o
v
e
cu
r
r
en
t w
av
e
f
o
r
m
q
u
alit
y.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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J
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&
C
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p
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2
5
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2
-
4
7
52
A
DA
LINE
-
b
a
s
ed
s
yn
ch
r
o
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tio
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r
en
h
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d
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p
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r
ma
n
ce
(
A
b
d
ess
la
m
R
ya
d
Meb
a
r
ek
)
43
T
ab
le
3
.
C
ase
(
1
)
r
esu
lts
N
o
r
me
B
e
f
o
r
e
c
o
m
p
e
n
sa
t
i
o
n
P
Q
t
h
e
o
r
y
N
e
u
r
a
l
S
D
M
P
h
a
se
(
a
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TH
D
%
<
5%
2
6
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0
1
.
0
6
0
.
3
6
h
3
/
h
1
(
%
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2
8
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h5
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3
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3
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7
P
h
a
se
(
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TH
D
%
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1
4
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3
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1
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1
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3
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P
h
a
se
(
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D
%
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2
6
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1
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3
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Alth
o
u
g
h
s
in
g
le
-
p
h
ase
n
o
n
lin
ea
r
lo
ad
s
ten
d
to
ca
u
s
e
im
b
alan
ce
s
in
co
n
n
ec
ted
n
etwo
r
k
s
(
o
n
lin
e
n
etwo
r
k
s
)
,
th
e
p
r
esen
ce
o
f
s
y
m
m
etr
ical
th
r
ee
-
p
h
ase
lo
ad
s
p
lay
s
a
s
ig
n
if
ican
t
m
itig
atin
g
r
o
le.
I
n
d
ee
d
,
as
illu
s
tr
ated
in
th
e
p
r
ev
io
u
s
s
ce
n
ar
io
,
t
h
e
p
e
r
ce
n
tag
e
o
f
3
r
d
o
r
d
er
h
ar
m
o
n
ic,
in
d
icativ
e
o
f
i
m
b
alan
ce
s
,
d
o
es
n
o
t
ex
ce
ed
7
%,
a
v
alu
e
clo
s
e
to
th
e
r
eg
u
lato
r
y
lim
it
o
f
4
%.
Acc
o
r
d
i
n
g
t
o
th
e
d
ata
f
r
o
m
[
3
6
]
,
t
h
is
ty
p
e
o
f
h
ar
m
o
n
ic
in
th
ese
n
etwo
r
k
s
ca
n
r
ea
ch
a
lim
it
o
f
1
5
%.
T
h
is
r
esu
lt
h
ig
h
lig
h
ts
th
e
s
tab
ilizin
g
r
o
le
o
f
th
r
ee
-
p
h
ase
lo
ad
s
.
I
n
co
n
tr
ast,
o
f
f
lin
e
n
etwo
r
k
s
,
ch
ar
ac
ter
ized
b
y
a
p
r
ed
o
m
in
an
ce
o
f
s
in
g
le
-
p
h
a
s
e
lo
ad
s
,
ar
e
m
o
r
e
v
u
ln
er
ab
le
to
h
ar
m
o
n
ic
d
is
to
r
tio
n
s
,
p
ar
ticu
lar
ly
th
e
3
r
d
o
r
d
er
h
ar
m
o
n
ic
wh
ich
ca
n
r
ea
c
h
alar
m
in
g
lev
els.
T
h
e
ab
s
en
ce
o
f
a
n
atu
r
al
atten
u
atin
g
ef
f
ec
t
in
th
ese
n
etwo
r
k
s
n
ec
ess
itates
a
th
o
r
o
u
g
h
ev
alu
atio
n
o
f
o
u
r
m
eth
o
d
i
n
th
is
s
p
ec
if
ic
co
n
t
ex
t.
T
h
e
s
im
u
latio
n
r
esu
lts
o
f
s
ce
n
ar
io
(
2
)
,
p
r
esen
ted
in
Fig
u
r
e
6
,
r
ev
ea
l
a
s
ig
n
if
ican
t
d
eg
r
a
d
atio
n
in
s
i
g
n
al
q
u
ality
b
ef
o
r
e
co
m
p
en
s
atio
n
co
m
p
ar
e
d
to
s
ce
n
ar
io
(
1
)
.
Sin
g
le
-
p
h
ase
n
o
n
lin
ea
r
lo
ad
s
in
tr
o
d
u
ce
s
ig
n
if
ican
t
an
d
u
n
ev
en
d
is
to
r
tio
n
s
an
d
i
m
b
alan
ce
s
in
th
e
l
o
ad
cu
r
r
en
ts
as
s
h
o
wn
in
Fig
u
r
e
6
(
a)
,
th
ese
ef
f
ec
ts
d
e
p
en
d
o
n
th
e
ty
p
e
o
f
s
in
g
le
-
p
h
a
s
e
lo
ad
co
n
n
ec
ted
to
ea
c
h
p
h
ase
an
d
th
e
cu
r
r
en
t
d
em
an
d
.
Mo
r
e
o
v
er
,
th
e
n
e
u
tr
a
l
cu
r
r
e
n
t
ex
h
ib
its
co
n
s
id
er
ab
le
f
lu
ctu
atio
n
s
,
wit
h
p
ea
k
v
alu
e
s
r
ea
ch
in
g
1
0
A
as
s
h
o
wn
in
Fig
u
r
e
6
(
b
)
.
Alth
o
u
g
h
th
ese
lo
ad
s
c
o
n
s
u
m
e
less
p
o
wer
t
h
an
th
r
ee
-
p
h
ase
lo
a
d
s
,
r
esu
ltin
g
in
l
o
wer
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
wer
a
m
p
litu
d
es
co
m
p
ar
e
d
to
s
ce
n
a
r
io
(
1
)
,
th
ey
f
l
u
ctu
ate
co
n
s
id
er
ab
ly
as
s
h
o
wn
in
Fig
u
r
e
6
(
c
)
.
T
h
ese
d
is
tu
r
b
an
c
es
lead
to
a
d
r
asti
c
r
ed
u
ctio
n
i
n
th
e
p
o
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ac
to
r
,
r
ea
ch
in
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0
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5
3
5
3
,
an
d
d
ec
r
ea
s
e
th
e
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f
icien
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o
f
p
o
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an
s
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r
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Fig
u
r
e
6
.
B
ef
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co
m
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en
s
atio
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i
g
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als in
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2
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a
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lo
ad
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r
r
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(
b
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e
u
tr
al
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r
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an
d
(
c)
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tiv
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(
b
lu
e)
,
a
n
d
r
ea
ctiv
e
p
o
wer
(
r
e
d
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Fig
u
r
e
7
p
r
esen
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th
e
s
ig
n
al
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o
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m
eth
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s
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d
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atio
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t
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o
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e
wa
v
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o
r
m
r
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lts
o
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r
ce
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r
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d
n
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tr
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l
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r
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o
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tain
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is
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ce
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io
ar
e
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o
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t
id
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tical
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o
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e
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r
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s
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ce
n
ar
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o
.
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h
e
s
o
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r
ce
cu
r
r
en
ts
ar
e
s
in
u
s
o
id
al,
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ce
d
,
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d
p
h
ase
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s
h
if
ted
b
y
1
2
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d
eg
r
ee
s
as
s
h
o
wn
in
Fig
u
r
es
7
(
a)
an
d
7
(
a
’
)
,
wh
ile
b
ein
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
1
,
J
an
u
ar
y
20
25
:
35
-
47
44
p
er
f
ec
tly
s
y
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ch
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ized
with
t
h
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v
o
ltag
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as
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h
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wn
in
Fig
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r
es
7
(
b
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an
d
7
(
b
’
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,
r
esu
ltin
g
in
a
u
n
it
p
o
wer
f
ac
to
r
.
T
h
e
am
p
litu
d
es
o
f
th
ese
cu
r
r
e
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ts
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ir
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t
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ce
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h
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A,
wh
ile
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5
A.
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is
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b
ec
au
s
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in
g
le
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ase
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ad
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u
m
e
less
cu
r
r
e
n
t th
an
th
r
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ase
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ad
s
.
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wev
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a
s
lig
h
t
asy
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m
etr
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th
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p
h
ase
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litu
d
es,
ar
o
u
n
d
0
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2
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o
b
s
er
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e
d
f
o
r
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eo
r
y
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u
n
lik
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h
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n
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r
al
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ich
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f
f
e
r
s
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e
r
f
ec
t
s
y
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m
etr
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in
d
i
ca
tin
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p
r
ec
is
e
id
en
tific
atio
n
b
y
th
is
m
et
h
o
d
.
T
h
e
n
eu
tr
al
cu
r
r
en
t
is
alm
o
s
t
ze
r
o
,
l
ess
th
an
0
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5
A
in
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m
eth
o
d
s
as
s
h
o
wn
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r
es
7
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c)
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n
d
7
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c
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n
d
icatin
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tiv
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m
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.
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eg
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d
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g
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ct
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o
wer
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o
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eth
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d
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en
s
u
r
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t
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m
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s
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ap
p
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r
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ased
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er
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ec
tly
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le
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m
o
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a
m
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f
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ilit
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itical
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r
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itals
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Fig
u
r
e
7
.
Po
s
t
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co
m
p
en
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atio
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ig
n
als in
ca
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2
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: so
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r
ce
cu
r
r
e
n
t
(
a)
PQ th
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(a
’
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ase
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r
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e
u
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al
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r
r
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n
t
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r
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m
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aly
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is
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f
h
ar
m
o
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ic
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ter
r
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lts
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ef
o
r
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d
af
ter
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m
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h
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e
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eth
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e
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ase
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t
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ase
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n
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l
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d
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Un
d
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n
d
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th
e
th
ir
d
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m
o
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ic
p
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m
in
ates,
f
u
r
th
er
ag
g
r
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atin
g
th
e
im
b
alan
ce
an
d
th
r
ea
ten
in
g
n
etwo
r
k
s
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.
Af
ter
co
m
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n
,
it
is
clea
r
th
at
th
e
p
r
o
p
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s
ed
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m
eth
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d
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n
s
is
ten
tly
o
u
tp
e
r
f
o
r
m
s
th
e
P
Q
t
h
e
o
r
y
.
T
h
e
T
H
D
i
s
s
i
g
n
i
f
ic
a
n
t
l
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r
e
d
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d
:
f
r
o
m
3
0
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9
6
%
to
0
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8
1
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p
h
a
s
e
a
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,
f
r
o
m
1
5
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0
4
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o
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5
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h
as
e
b
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a
n
d
f
r
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m
1
1
9
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Evaluation Warning : The document was created with Spire.PDF for Python.