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[
1
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,
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2
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ith
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[
5
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h
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ed
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I
n
th
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ies
[
2
-
4
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,
th
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itab
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p
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ile
th
e
o
p
tim
al
s
izes
o
f
ca
p
ac
ito
r
s
h
a
v
e
b
ee
n
d
eter
m
in
e
d
b
y
PSO
-
T
VI
W
,
AC
O
an
d
GA,
r
e
s
p
ec
tiv
ely
.
I
n
[
5
]
,
th
e
m
is
s
io
n
o
f
d
eter
m
in
in
g
ca
p
ac
ito
r
p
lace
m
en
ts
at
ca
n
d
i
d
ate
b
u
s
es is
ass
ig
n
ed
to
a
f
u
zz
y
te
ch
n
iq
u
e
w
h
ile
th
e
o
p
tim
al
ca
p
ac
ito
r
s
ize
d
eter
m
in
a
tio
n
is
in
ch
ar
g
e
o
f
I
HM
.
Fo
r
th
e
s
ec
o
n
d
test
s
y
s
tem
,
m
an
y
a
lg
o
r
ith
m
s
s
u
ch
as
an
aly
tical
m
eth
o
d
(
AM
)
[
6
]
,
g
r
id
s
ea
r
ch
al
g
o
r
ith
m
(
GSA)
[
6
]
,
g
o
ld
en
s
ec
tio
n
s
ea
r
ch
alg
o
r
it
h
m
GSSA
[
6
]
an
d
m
in
im
izatio
n
o
f
p
o
wer
lo
s
s
es
(
ML
P
)
[
6
]
,
g
r
ass
h
o
p
p
er
h
eu
r
is
tic
o
p
tim
izat
io
n
al
g
o
r
ith
m
(
GOA)
[
7
]
,
p
la
n
t
g
r
o
wth
s
im
u
latio
n
alg
o
r
ith
m
(
PGSA)
[
8
]
,
two
-
s
tag
e
m
eth
o
d
(
T
SM)
[
9
]
,
in
ter
i
o
r
p
o
in
t
(
I
P]
[
1
0
]
,
s
im
u
lated
an
n
ea
lin
g
(
SA)
[
1
0
]
an
d
g
r
a
v
itatio
n
al
s
ea
r
ch
m
eth
o
d
(
GSM)
[
1
0
]
,
f
lo
wer
p
o
llin
atio
n
alg
o
r
ith
m
(
FP
A)
[
1
1
]
an
d
an
t li
o
n
o
p
ti
m
izer
(
AL
O)
[
1
2
]
h
av
e
b
ee
n
s
u
c
ce
s
s
f
u
lly
ap
p
lied
f
o
r
im
p
lem
en
tin
g
th
e
OC
PS
D
p
r
o
b
lem
.
I
n
p
r
e
v
io
u
s
m
eth
o
d
g
r
o
u
p
,
th
e
wo
r
k
in
[
1
1
,
1
2
]
h
a
s
th
e
s
am
e
m
an
n
er
b
ec
au
s
e
th
e
au
th
o
r
s
h
av
e
em
p
lo
y
ed
FP
A
an
d
AL
O
m
eth
o
d
s
to
d
is
co
v
er
b
o
th
th
e
c
o
r
r
ec
t
lo
c
atio
n
s
an
d
th
e
s
izi
ng
o
f
ca
p
ac
ito
r
.
T
h
e
co
m
b
in
atio
n
o
f
K
-
Me
an
C
lu
s
ter
in
g
an
d
E
lb
o
w
T
ec
h
n
iq
u
e
h
as
b
ee
n
ap
p
lied
f
o
r
a
r
ea
l
d
is
tr
ib
u
tio
n
n
etwo
r
k
in
Vietn
am
[
1
3
]
.
A
d
o
lp
h
in
alg
o
r
it
h
m
was
s
u
g
g
ested
in
[
1
4
]
f
o
r
1
6
an
d
3
3
-
b
u
s
es
s
y
s
tem
s
b
u
t
th
er
e
h
as
b
ee
n
co
m
p
ar
is
o
n
with
p
r
ev
io
u
s
m
eth
o
d
s
.
Dis
tr
ib
u
ted
g
e
n
er
ato
r
s
[
1
5
]
as
well
as
r
ec
o
n
f
ig
u
r
atio
n
[
1
6
-
1
9
]
ar
e
t
wo
s
o
lu
tio
n
s
f
o
r
r
e
d
u
cin
g
p
o
wer
lo
s
s
o
f
d
is
tr
ib
u
tio
n
.
B
o
th
ca
p
ac
ito
r
p
lace
m
en
t
an
d
r
ec
o
n
f
ig
u
r
atio
n
wer
e
c
o
m
b
in
ed
t
o
r
e
d
u
ce
t
o
tal
lo
s
s
[
2
0
]
.
I
n
ad
d
itio
n
,
s
tatic
s
y
n
c
h
r
o
n
o
u
s
co
n
d
en
s
e
r
(
STAT
C
OM
)
was
also
p
r
o
p
o
s
ed
to
im
p
r
o
v
e
v
o
ltag
e
p
r
o
f
il
e
o
f
d
is
tr
ib
u
tio
n
s
y
s
tem
s
[
2
1
]
.
I
n
g
en
er
al,
t
h
ese
p
r
o
p
o
s
ed
s
o
lu
tio
n
s
co
u
ld
r
ed
u
ce
lo
s
s
an
d
im
p
r
o
v
e
v
o
ltag
e
;
h
o
wev
er
,
th
e
co
s
t
o
f
o
th
er
c
o
m
p
o
n
en
ts
is
m
u
ch
h
ig
h
er
th
a
n
ca
p
ac
ito
r
s
.
I
n
th
is
p
ap
er
,
th
e
p
r
o
c
ess
f
o
r
d
eter
m
in
in
g
b
o
th
th
e
s
u
itab
le
p
o
s
itio
n
s
an
d
th
e
r
atin
g
s
o
f
ca
p
ac
ito
r
h
as
b
ee
n
r
eso
lv
e
d
b
y
d
if
f
u
s
io
n
an
d
u
p
d
ate
tec
h
n
iq
u
es
-
b
ased
alg
o
r
ith
m
(
DUT
A
)
.
DUT
A
was
f
o
r
m
ed
b
y
th
r
ee
p
h
ase
in
clu
d
in
g
d
if
f
u
s
io
n
p
h
ase
an
d
two
o
th
er
u
p
d
ate
p
h
ases
f
o
r
cr
ea
tin
g
s
o
lu
tio
n
s
[
2
2
]
.
T
h
e
f
ir
s
t
p
h
ase’
s
m
is
s
io
n
is
to
ex
p
lo
r
e
s
ea
r
ch
s
p
ac
es
b
y
u
s
in
g
m
an
y
n
ew
s
o
lu
tio
n
s
wh
e
r
ea
s
two
o
th
er
p
h
ases
ar
e
in
c
h
ar
g
e
o
f
ex
p
lo
it
th
e
s
ea
r
ch
s
p
ac
e.
T
h
e
ex
p
er
ien
ce
d
r
esu
lts
o
f
DUT
A
m
eth
o
d
ar
e
v
er
y
p
r
o
m
is
in
g
v
ia
ex
ec
u
t
in
g
o
n
1
5
-
b
u
s
an
d
33
-
b
u
s
s
tan
d
ar
d
d
is
tr
ib
u
tio
n
s
y
s
tem
s
.
Su
b
s
eq
u
en
tly
,
th
is
p
a
p
er
o
f
f
er
s
s
o
m
e
co
n
t
r
ib
u
tio
n
s
as f
o
llo
ws:
−
C
lear
ly
an
aly
s
is
th
e
s
tr
u
ctu
r
e
o
f
DUT
A
−
So
lv
e
OC
PS
D
p
r
o
b
lem
with
m
an
y
s
tu
d
y
ca
s
es b
y
u
s
in
g
D
UT
A
−
DUT
A
ca
n
o
f
f
er
f
av
o
r
ab
le
s
o
l
u
tio
n
s
2.
O
CP
SD P
RO
B
L
E
M
F
O
RM
UL
A
T
I
O
N
2
.
1
.
L
o
a
d f
l
o
w
Po
wer
f
lo
w
ca
lc
u
latio
n
in
th
e
d
is
tr
ib
u
tio
n
n
etwo
r
k
is
v
er
y
es
s
en
tial to
an
aly
ze
o
r
ass
ess
th
e
o
p
er
atin
g
s
tate
o
f
th
e
d
is
tr
ib
u
tio
n
s
y
s
tem
.
Fro
m
h
er
e,
we
ca
n
d
eter
m
in
e
v
o
ltag
e
at
b
u
s
es,
ca
lcu
late
th
e
cu
r
r
en
t
r
u
n
n
in
g
o
n
b
r
an
ch
es
a
n
d
co
m
p
u
te
ac
tiv
e
p
o
wer
lo
s
s
e
s
b
etwe
en
two
n
o
d
es.
Fo
r
t
h
is
wo
r
k
,
a
s
in
g
le
li
n
e
d
iag
r
am
o
f
th
e
s
im
p
le
d
is
tr
ib
u
tio
n
s
y
s
tem
as
d
is
p
lay
ed
in
Fig
u
r
e
1
is
co
n
s
id
er
ed
,
wh
er
e
g
is
th
e
s
en
d
in
g
b
u
s
an
d
g
+
1
is
th
e
r
ec
eiv
in
g
b
u
s
.
Fro
m
Fig
u
r
e
1
,
e
q
u
atio
n
s
f
o
r
th
e
p
o
wer
f
lo
ws
ca
n
b
e
estab
lis
h
ed
a
s
f
o
llo
ws:
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Op
timiz
in
g
lo
ca
tio
n
a
n
d
s
iz
e
o
f c
a
p
a
cito
r
s
fo
r
p
o
w
er lo
s
s
r
e
d
u
ctio
n
in
r
a
d
ia
l..
.
(
Th
u
a
n
Th
a
n
h
N
g
u
ye
n
)
295
Fig
u
r
e
1
.
Simp
le
d
is
tr
ib
u
tio
n
s
y
s
tem
,
+
1
=
+
1
+
,
+
1
+
,
+
1
(
,
+
1
2
+
,
+
1
2
)
/
|
|
2
(
1
)
,
+
1
=
+
1
+
,
+
1
+
,
+
1
(
,
+
1
2
+
,
+
1
2
)
/
|
|
2
(
2
)
+
1
2
=
2
−
2
(
,
+
1
.
,
+
1
+
,
+
1
.
,
+
1
)
+
(
,
+
1
2
+
,
+
1
2
)
.
(
,
+
1
2
+
,
+
1
2
)
/
|
|
2
(3
)
Fro
m
s
h
o
wn
in
(
1
)
an
d
(
2
)
,
we
ca
n
ea
s
ily
d
ed
u
ce
th
e
l
o
s
s
o
f
th
e
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
wer
o
f
th
e
kt
h
lin
e
b
etwe
en
b
u
s
g
a
n
d
g
+1
as th
e
f
o
llo
win
g
eq
u
atio
n
s
:
L
os
s
(
g,
g+
1
)
=
,
+
1
(
,
+
1
2
+
,
+
1
2
)
/
|
|
2
(
4
)
L
os
s
(
,
+
1
)
=
,
+
1
(
,
+
1
2
+
,
+
1
2
)
/
|
|
2
(
5
)
T
h
e
to
tal
k
W
lo
s
s
es o
f
th
e
d
is
tr
ib
u
tio
n
s
y
s
tem
(
P
∑
)
ar
e
s
p
ec
if
ied
b
y
th
e
f
o
llo
win
g
eq
u
atio
n
s
:
∑
=
∑
L
o
s
s
(
g,
g+
1
)
=
1
(
6
)
2
.
2
.
O
b
je
c
tiv
e
fu
n
c
tio
n
T
h
e
m
ain
tar
g
et
o
f
th
e
ca
p
ac
it
o
r
p
lace
m
en
t
in
d
is
tr
ib
u
tio
n
s
y
s
tem
s
is
p
o
wer
lo
s
s
r
ed
u
ctio
n
an
d
v
o
ltag
e
q
u
ality
im
p
r
o
v
em
en
t.
T
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
(
OF)
o
f
OC
PS
D
p
r
o
b
lem
ca
n
b
e
g
iv
en
b
y
:
∑
=
∑
L
os
s
(
g,
g+
1
)
=
1
(
7
)
2
.
3
.
Co
ns
t
ra
ints
-
Vo
ltag
e
C
o
n
s
tr
ain
ts
:
th
e
v
o
ltag
e
m
ag
n
itu
d
e
at
b
u
s
es is
p
er
m
itted
to
b
e
b
etwe
en
t
h
e
p
r
e
d
ete
r
m
in
ed
lim
its
:
g
(
8
)
-
T
o
tal
I
n
jecte
d
r
ea
ctiv
e
p
o
wer
:
th
e
s
u
m
o
f
t
h
e
co
m
p
en
s
ated
r
ea
ctiv
e
p
o
wer
m
u
s
t
b
e
less
o
r
eq
u
al
th
an
th
at
o
f
lo
ad
s
.
∑
(
)
=
1
≤
∑
(
)
=
1
(
9
)
-
C
ap
ac
ito
r
s
ize
an
d
b
r
an
ch
cu
r
r
en
t lim
its
:
ea
ch
in
s
ta
lled
ca
p
ac
ito
r
an
d
cu
r
r
e
n
t f
lo
win
g
ea
ch
b
r
an
ch
m
u
s
t b
e
co
n
s
tr
ain
ed
b
y
:
(
1
0
)
|
|
≤
(
1
1
)
3.
T
H
E
AP
P
L
I
E
D
DUT
A
M
E
T
H
O
D
DUT
A
was
r
ec
o
m
m
en
d
ed
a
n
d
d
ev
elo
p
ed
b
y
ad
d
in
g
two
u
p
d
ate
p
h
ases
to
f
r
ac
tal
s
e
ar
ch
-
b
ased
alg
o
r
ith
m
(
FS
A)
[
2
2
,
2
3
]
.
T
h
e
au
th
o
r
p
r
o
v
e
d
th
at
DUT
A
was
ca
p
ab
le
f
o
r
s
o
lv
in
g
o
p
tim
iz
atio
n
p
r
o
b
lem
s
v
ia
im
p
lem
en
tin
g
o
n
s
o
m
e
tr
ad
itio
n
al
b
en
ch
m
ar
k
test
f
u
n
ctio
n
s
.
I
n
ad
d
itio
n
,
DUT
A
was
also
ex
te
n
d
ed
to
s
o
lv
e
co
m
p
lex
o
p
tim
izatio
n
p
r
o
b
lem
s
s
u
ch
as
ec
o
n
o
m
ic
lo
ad
d
is
p
atch
[
2
4
]
,
o
p
tim
al
r
ea
cti
v
e
p
o
wer
d
is
p
atch
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
1
:
2
9
3
-
3
0
0
296
p
r
o
b
lem
[
2
5
]
an
d
o
p
tim
izin
g
d
is
tr
ib
u
ted
d
atab
ase
q
u
er
ies
[
2
6
]
.
T
h
e
s
ea
r
ch
in
g
p
o
wer
o
f
DUT
A
is
b
ased
o
n
th
r
ee
p
h
ases
in
clu
d
in
g
d
if
f
u
s
i
o
n
th
e
f
i
r
s
t
u
p
d
ate
a
n
d
th
e
s
ec
o
n
d
u
p
d
ate
p
h
ases
.
T
h
e
m
is
s
io
n
o
f
s
u
ch
p
h
ases
is
d
escr
ib
ed
as f
o
llo
ws:
Dif
f
u
s
io
n
m
ec
h
a
n
is
m
-
b
ased
s
ea
r
ch
alg
o
r
ith
m
(
DM
B
SA)
is
im
p
lem
en
ted
b
y
:
=
Gau
s
s
ian
(
be
s
,
)
+
(
be
s
−
)
;
d
=
1
,
.
.
.
,
(
1
2
)
=
Gau
s
s
ian
(
,
)
(
1
3
)
=
|
(
be
s
−
)
.
(
)
|
/
(
1
4
)
T
o
u
s
e
s
h
o
wn
in
(
1
2
)
o
r
(
1
3
)
,
a
co
m
p
a
r
is
o
n
b
etwe
en
a
r
a
n
d
o
m
n
u
m
b
er
(
θ)
an
d
a
walk
f
ac
to
r
(
ω
)
is
d
eter
m
in
ed
.
I
f
θ
<
ω
,
s
h
o
wn
in
(
1
2
)
is
ass
i
g
n
ed
an
d
o
th
e
r
wis
e,
s
h
o
wn
in
(
1
3
)
is
g
iv
e
n
.
C
lear
ly
,
th
e
v
al
u
e
o
f
ω
p
lay
s
a
k
ey
r
o
le
in
s
elec
tin
g
two
p
r
ev
io
u
s
eq
u
atio
n
s
.
I
f
ω
is
f
i
x
ed
to
1
,
n
ew
s
o
lu
tio
n
s
ar
e
cr
ea
ted
b
y
(
1
2
)
.
I
f
ω
is
f
ix
ed
to
0
,
n
ew
s
o
lu
tio
n
s
ar
e
g
e
n
er
ated
b
y
(
1
2
)
.
Oth
er
wis
e,
b
o
th
as
s
h
o
wn
in
(
1
2
)
an
d
(
1
3
)
ar
e
u
s
ed
f
o
r
p
r
o
d
u
cin
g
n
ew
o
n
es.
Af
ter
p
er
f
o
r
m
in
g
DM
B
SA,
th
e
f
ir
s
t
an
d
s
ec
o
n
d
u
p
d
ates
ar
e
im
p
lem
e
n
ted
b
y
t
h
e
two
f
o
llo
win
g
eq
u
atio
n
s
:
=
1
+
(
2
−
)
(
1
5
)
=
{
+
(
3
−
4
)
if
>
0
.
5
−
(
5
−
be
s
)
(
1
6
)
4.
T
H
E
I
M
P
L
E
M
E
N
T
A
T
I
O
N
O
F
DUTA T
O
O
CP
SD P
RO
B
L
E
M
I
n
OC
PS
D
p
r
o
b
lem
,
th
e
lo
ca
tio
n
an
d
s
ize
o
f
ca
p
ac
ito
r
s
ar
e
al
s
o
co
n
tr
o
lled
b
y
v
ar
iab
les
co
r
r
esp
o
n
d
in
g
to
a
s
o
lu
tio
n
o
f
DUT
A
.
Su
ch
v
ar
iab
les
ar
e
d
e
p
en
d
e
n
t
o
n
th
e
n
u
m
b
er
o
f
ca
p
ac
ito
r
s
ad
d
ed
to
th
e
s
y
s
tem
.
Fo
r
ca
lcu
latin
g
th
e
p
o
wer
l
o
s
s
an
d
th
e
v
o
ltag
e
at
ea
ch
b
u
s
,
we
r
u
n
th
e
p
o
wer
f
l
o
w
p
r
o
g
r
am
u
s
in
g
f
o
r
war
d
–
b
ac
k
war
d
s
wee
p
tech
n
iq
u
e
[
2
7
]
.
T
h
e
f
itn
ess
f
u
n
ctio
n
f
o
r
ass
ess
in
g
s
o
lu
tio
n
s
is
s
p
ec
if
ied
b
y
u
s
in
g
as
s
h
wo
n
in
(
1
7
)
;
=
∑
+
(
+
)
(
1
7
)
Step
s
to
im
p
lem
en
t th
e
DUT
A
f
o
r
OC
PS
D
p
r
o
b
lem
ar
e
p
r
es
en
ted
b
y
th
e
f
lo
wch
a
r
t p
r
o
v
id
e
d
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
T
h
e
f
lo
wch
a
r
t o
f
DU
T
A
f
o
r
im
p
lem
e
n
tin
g
OC
PS
D
p
r
o
b
lem
5.
CO
M
P
ARI
SO
N
AND
D
I
SC
USSI
O
N
I
n
th
is
p
ar
a
g
r
ap
h
,
an
ac
tu
al
p
er
f
o
r
m
a
n
ce
o
f
DUT
A
is
s
tu
d
ied
b
y
m
ak
in
g
th
e
r
esu
lt
co
m
p
ar
is
o
n
s
o
f
s
u
ch
m
eth
o
d
to
o
th
er
p
r
ev
io
u
s
r
ep
o
r
ted
m
eth
o
d
s
.
T
wo
d
is
tr
ib
u
tio
n
test
s
y
s
tem
s
ar
e
em
p
lo
y
ed
f
o
r
s
o
lv
in
g
th
e
o
p
tim
al
ca
p
ac
ito
r
p
lace
m
en
t p
r
o
b
lem
with
two
th
e
f
o
ll
o
win
g
in
ten
tio
n
s
:
−
Dis
tr
ib
u
tio
n
s
y
s
tem
o
f
1
5
b
u
s
es
an
d
3
3
b
u
s
es
ar
e
u
s
ed
as
th
e
b
asic
m
o
d
els
f
o
r
ac
ce
s
s
i
n
g
th
e
ab
ilit
y
o
f
DUT
A.
−
Su
ch
test
s
y
s
tem
s
ar
e
r
eg
ar
d
e
d
as
th
e
ex
p
ed
ien
cy
f
o
r
s
u
r
v
ey
in
g
d
if
f
e
r
en
t
co
m
p
en
s
ated
ca
p
ac
ito
r
p
lace
m
en
ts
.
5
.
1
.
T
he
15
-
bu
s
net
wo
rk
T
h
e
I
E
E
E
1
5
-
b
u
s
test
d
is
tr
ib
u
tio
n
s
y
s
tem
h
as
a
to
tal
lo
ad
o
f
1
.
2
2
6
4
MW
an
d
1
2
5
1
.
2
MV
Ar
.
T
h
e
in
v
esti
g
ated
s
y
s
tem
is
s
u
f
f
er
ed
an
ac
tiv
e
p
o
wer
l
o
s
s
o
f
6
1
.
8
k
W
[
1
]
.
C
lear
ly
,
s
u
ch
p
o
wer
lo
s
s
is
s
ig
n
if
ican
t
th
at
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Op
timiz
in
g
lo
ca
tio
n
a
n
d
s
iz
e
o
f c
a
p
a
cito
r
s
fo
r
p
o
w
er lo
s
s
r
e
d
u
ctio
n
in
r
a
d
ia
l..
.
(
Th
u
a
n
Th
a
n
h
N
g
u
ye
n
)
297
n
ee
d
s
to
b
e
r
ed
u
ce
d
b
y
a
d
d
i
n
g
ca
p
ac
i
to
r
s
.
Ho
wev
e
r
,
a
d
e
ter
m
in
atio
n
o
f
l
o
ca
tio
n
,
th
e
n
u
m
b
er
an
d
s
ize
o
f
ca
p
ac
ito
r
s
h
as
to
b
e
s
elec
ted
ca
r
ef
u
lly
b
ec
a
u
s
e
it
ca
n
lead
to
o
v
er
co
m
p
en
s
atio
n
.
I
t
is
f
o
r
th
at
r
ea
s
o
n
th
at
DUT
A
is
ap
p
lied
f
o
r
t
h
is
task
with
two
ca
s
es
f
o
r
ca
p
ac
ito
r
p
lace
m
en
ts
.
Nam
ely
,
C
ase
1
in
s
tall
two
ca
p
ac
ito
r
s
wh
ile
C
ase
2
ad
d
s
th
r
ee
ca
p
ac
ito
r
s
to
th
e
s
y
s
tem
.
W
ith
ea
ch
o
p
tim
izatio
n
p
r
o
b
lem
,
in
v
esti
g
atin
g
p
ar
am
eter
s
to
ac
ce
s
s
th
e
ef
f
icac
y
,
s
tu
r
d
in
ess
an
d
s
tab
ilit
y
o
f
th
e
s
ea
r
ch
p
r
o
ce
s
s
o
f
DUT
A
ar
e
ex
tr
em
ely
im
p
o
r
tan
t.
W
h
er
ein
p
o
p
u
lati
o
n
s
izes (
P
s
)
an
d
t
h
e
m
ax
im
u
m
n
u
m
b
e
r
o
f
iter
atio
n
s
(
C
max
)
ar
e
two
p
ar
am
eter
s
to
b
e
in
v
esti
g
ated
.
Fo
r
ca
s
e
1
,
T
ab
le
1
s
h
o
ws
th
at
th
e
b
est
p
o
wer
lo
s
s
is
3
2
.
3
0
6
k
W
co
r
r
esp
o
n
d
in
g
with
P
s
=
1
0
an
d
C
max
=
3
0
.
C
lear
ly
,
th
e
P
s
v
alu
e
is
im
p
o
s
s
ib
le
to
d
ec
r
ea
s
e
alth
o
u
g
h
C
max
is
in
cr
ea
s
ed
.
Ho
wev
er
,
th
e
s
tan
d
ar
d
d
ev
iatio
n
(
STD
)
v
alu
e
o
f
s
u
b
c
ase
1
.
1
D
is
s
m
aller
th
an
th
at
o
f
s
u
b
ca
s
e
1
.
1
C
.
Fig
u
r
e
3
p
r
o
v
i
d
es
th
e
p
o
wer
lo
s
s
r
esu
lts
o
f
5
0
r
u
n
s
f
r
o
m
s
u
b
ca
s
e
1
.
1
A
to
s
u
b
ca
s
e
1
.
1
C
wh
ils
t
Fig
u
r
e
4
s
h
o
ws
th
e
b
est
p
o
wer
lo
s
s
an
d
ST
D
o
f
th
ese
s
u
b
ca
s
es.
T
ab
le
1
.
T
h
e
p
o
wer
lo
s
s
(
k
W
)
o
b
tain
ed
f
r
o
m
d
if
f
er
e
n
t v
alu
es
o
f
P
s
a
n
d
C
max
o
v
e
r
5
0
r
u
n
s
No
S
u
b
c
a
s
e
1
.
1
A
S
u
b
c
a
s
e
1
.
1
B
S
u
b
c
a
s
e
1
.
1
C
S
u
b
c
a
s
e
1
.
1
D
P
s
5
5
10
10
C
m
ax
10
15
30
50
M
i
n
l
o
ss
3
2
.
5
2
4
3
2
.
3
1
2
3
2
.
3
0
6
3
2
.
3
0
6
A
v
e
r
l
o
ss
3
4
.
6
6
8
3
4
.
2
0
4
3
2
.
7
1
9
3
2
.
5
1
5
M
a
x
l
o
ss
3
7
.
5
6
3
4
1
.
2
9
5
3
7
.
0
9
5
3
4
.
6
7
8
S
T
D
1
.
5
0
8
1
.
8
4
1
0
.
8
9
8
0
.
4
7
7
Fig
u
r
e
3
.
T
h
e
b
est p
o
wer
lo
s
s
o
f
5
0
r
u
n
s
f
r
o
m
s
u
b
-
ca
s
e
1
.
1
A
to
s
u
b
-
ca
s
e
1
.
1
D
Fig
u
r
e
4
.
T
h
e
b
est p
o
wer
lo
s
s
an
d
STD
f
o
r
ca
s
e
1
f
r
o
m
s
u
b
-
ca
s
e
1
.
1
A
to
s
u
b
-
ca
s
e
1
.
1
D
T
h
e
r
esu
lts
o
f
DUT
A
ar
e
co
m
p
ar
ed
t
o
o
th
er
m
eth
o
d
s
f
o
r
C
ase
1
as
s
h
o
wn
i
n
T
a
b
le
2
.
Seein
g
th
e
tab
le
ca
n
r
ec
o
g
n
ize
th
at
DUT
A
an
d
AC
O
[
3
]
f
in
d
th
e
s
am
e
p
o
s
itio
n
s
o
f
ca
p
ac
ito
r
s
b
u
t
d
if
f
e
r
en
t
p
o
s
itio
n
s
with
HE
M
[
1
]
an
d
PS
O
-
T
VI
W
[
2
]
.
Ho
wev
er
,
p
o
wer
lo
s
s
o
f
DUT
A
is
th
e
s
m
allest
wh
ile
th
at
o
f
AC
O
[
3
]
is
th
e
h
ig
h
est.
Fo
r
C
ase
2
,
th
e
p
r
o
ce
s
s
f
o
r
in
v
esti
g
atin
g
th
ese
m
en
t
io
n
ed
p
ar
am
eter
s
o
f
DUT
A
is
ag
ain
im
p
lem
en
ted
with
th
e
r
esu
lts
o
b
tain
ed
as
d
is
p
lay
ed
in
Fig
u
r
e
5
.
Fr
o
m
th
e
f
ig
u
r
e,
t
h
e
p
o
wer
lo
s
s
o
f
3
1
.
2
8
0
k
W
is
co
r
r
esp
o
n
d
in
g
to
P
s
=
5
an
d
C
max
=
1
0
,
th
at
o
f
3
0
.
3
7
8
k
W
is
co
r
r
esp
o
n
d
in
g
to
P
s
=
5
an
d
C
max
=
2
0
.
T
h
at
o
f
3
0
.
3
3
8
k
W
,
wh
ich
is
co
n
s
id
er
ed
as
th
e
b
est
p
o
wer
l
o
s
s
,
is
co
r
r
esp
o
n
d
in
g
t
o
P
s
=
1
0
an
d
C
max
=
4
0
.
I
f
C
max
is
co
n
tin
u
o
u
s
ly
in
c
r
ea
s
ed
to
6
0
,
th
e
b
est
p
o
wer
lo
s
s
d
o
es
n
o
t
s
till
ch
an
g
e
b
u
t
STD
is
b
etter
.
T
ab
le
3
s
h
o
ws
th
e
o
p
tim
al
r
esu
lts
o
b
tain
ed
f
r
o
m
DUT
A
an
d
I
HA
[
5
]
in
ter
m
o
f
ca
p
ac
ito
r
p
o
s
itio
n
s
an
d
s
izes,
to
tal
co
m
p
e
n
s
atio
n
,
p
o
wer
lo
s
s
an
d
m
in
im
u
m
v
o
lta
g
e.
I
t
ca
n
b
e
p
r
o
v
en
th
at
DUT
A
is
s
u
p
er
io
r
to
I
HA.
Fig
u
r
e
6
i
s
p
lo
tted
to
illu
s
tr
a
te
im
p
r
o
v
em
e
n
t o
f
th
e
f
ir
s
t sy
s
tem
v
o
ltag
es d
u
e
to
co
n
n
ec
ted
c
ap
ac
ito
r
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
1
:
2
9
3
-
3
0
0
298
T
ab
le
2
.
R
esu
lt c
o
m
p
ar
is
o
n
b
e
twee
n
DUT
A
an
d
o
th
er
m
eth
o
d
s
f
o
r
ca
s
e
1
M
e
t
h
o
d
s
C
a
p
a
c
i
t
o
r
n
o
d
e
S
i
z
e
(
k
V
A
R
)
To
t
a
l
K
V
A
R
To
t
a
l
l
o
ss
(
k
W
)
M
i
n
i
m
u
m
V
o
l
t
a
g
e
(
p
.
u
)
H
EM
[
1
]
3
,
6
8
0
5
,
3
8
8
1
1
9
3
3
2
.
6
-
PSO
-
TV
I
W
[
2
]
3
,
6
8
7
1
,
3
2
1
1
1
9
2
3
2
.
7
-
A
C
O
[
3
]
4
,
6
6
3
0
,
4
1
0
1
0
4
0
3
6
.
8
1
0
.
9
5
D
U
TA
4
,
6
4
3
8
.
4
6
,
7
0
2
.
6
4
1
1
4
1
.
1
3
2
.
3
0
6
0
.
9
6
5
0
4
Fig
u
r
e
5
.
T
h
e
b
est p
o
wer
lo
s
s
an
d
STD
f
o
r
ca
s
e
1
f
r
o
m
s
u
b
-
ca
s
e
1
.
2
A
to
s
u
b
-
ca
s
e
1
.
2
D
T
ab
le
3
.
R
esu
lt
co
m
p
ar
is
o
n
b
e
twee
n
DUT
A
an
d
o
th
er
m
eth
o
d
s
f
o
r
ca
s
e
2
M
e
t
h
o
d
s
C
a
p
a
c
i
t
o
r
n
o
d
e
S
i
z
e
(
k
V
A
R
)
To
t
a
l
K
V
A
R
To
t
a
l
l
o
ss
(
k
W
)
M
i
n
i
m
u
m
V
o
l
t
a
g
e
(
p
.
u
)
I
H
A
[
5
]
6
,
1
1
,
1
5
3
5
0
,
3
0
0
,
3
0
0
9
5
0
3
1
.
1
2
0
.
9
6
5
8
D
U
TA
4
,
6
,
1
1
4
8
8
.
2
4
,
4
0
8
.
0
8
,
3
0
0
.
1
1
1
9
7
.
1
4
3
0
.
3
4
0
.
9
6
9
5
5
Fig
u
r
e
6
.
T
h
e
in
f
lu
e
n
ce
o
f
co
m
p
en
s
ated
ca
p
ac
ito
r
s
o
n
th
e
v
o
ltag
e
o
f
1
5
b
u
s
s
y
s
tem
5
.
2
.
T
he
3
3
-
no
de
net
wo
r
k
T
h
is
s
y
s
tem
w
ith
o
u
t
co
m
p
en
s
atio
n
h
as
th
e
m
in
im
u
m
b
u
s
v
o
ltag
e
o
f
0
.
9
0
3
8
p
.
u
at
b
u
s
1
8
an
d
to
tal
ac
tiv
e
p
o
wer
lo
s
s
o
f
2
1
0
.
9
7
KW
[
1
3
]
.
Fo
r
p
u
r
p
o
s
in
g
l
o
s
s
r
ed
u
ctio
n
o
f
s
u
ch
s
y
s
tem
,
DUT
A
is
u
tili
ze
d
to
s
ee
k
th
e
co
r
r
ec
t
p
o
s
itio
n
s
an
d
o
p
ti
m
al
s
izes
o
f
ca
p
ac
ito
r
s
w
ith
t
wo
s
u
r
v
ey
ca
s
es.
Sin
g
le
an
d
t
h
r
ee
ca
p
ac
ito
r
s
ar
e
co
n
s
id
er
ed
,
wh
e
r
e
two
ca
s
es
ar
e
r
ep
o
r
te
d
in
T
ab
le
4
a
n
d
T
a
b
le
5
.
Fo
r
ea
ch
s
tu
d
y
ca
s
e
o
f
t
h
e
s
y
s
tem
,
we
also
an
aly
ze
P
s
an
d
C
max
to
f
in
d
th
e
b
est
o
p
tim
al
r
esu
lts
ac
h
iev
e
d
b
y
DUT
A
f
o
r
c
o
m
p
ar
is
o
n
s
.
As
a
r
esu
lt,
P
s
=
1
0
an
d
C
max
=
5
0
ar
e
s
elec
ted
f
o
r
th
e
ca
s
e
with
o
n
e
ca
p
ac
ito
r
an
d
P
s
=
1
0
an
d
C
max
=
7
0
ar
e
s
et
f
o
r
th
e
ca
s
e
with
th
r
ee
ca
p
ac
ito
r
s
.
I
t is seen
f
r
o
m
T
ab
le
4
th
at
DUT
A
g
iv
es c
ap
ac
ito
r
p
lace
m
en
t,
t
o
tal
p
o
we
r
lo
s
s
an
d
m
in
im
u
m
v
o
ltag
e
lik
e
o
th
er
f
iv
e
m
eth
o
d
s
.
T
h
is
d
em
o
n
s
tr
ates
th
at
all
m
eth
o
d
s
ca
n
r
esu
lt
in
th
e
s
am
e
as
a
s
o
lu
tio
n
q
u
ality
.
I
n
ca
s
e
o
f
lo
ca
tin
g
th
r
ee
ca
p
a
cito
r
s
,
DUT
A
id
en
tifie
s
th
e
o
p
tim
u
m
p
o
s
itio
n
s
as
b
u
s
n
u
m
b
er
s
1
3
,
2
4
,
3
0
an
d
o
p
tim
al
s
izes
as
3
8
7
.
9
2
,
5
4
4
.
2
1
an
d
1
0
3
7
.
0
3
k
VAR
,
r
esp
ec
tiv
ely
.
T
o
tal
lo
s
s
is
le
s
s
e
n
ed
to
1
3
8
.
3
7
2
k
W
f
r
o
m
th
e
b
ase
ca
s
e
o
f
2
1
0
.
9
7
k
W
co
r
r
esp
o
n
d
in
g
3
4
.
4
1
% o
f
a
ctiv
e
p
o
wer
lo
s
s
r
ate
an
d
th
e
m
in
im
u
m
v
o
ltag
e
is
im
p
r
o
v
e
d
to
b
e
0
.
9
5
6
7
p
u
.
As
s
h
o
wn
in
T
ab
le
5
,
to
tal
lo
s
s
f
r
o
m
GSA
[
1
0
]
is
t
h
e
s
m
al
lest
b
u
t
it
is
ea
s
ily
r
ec
o
g
n
ized
th
at
GSA
u
s
es
P
s
=
2
0
0
0
an
d
C
max
=
4
0
0
,
lead
i
n
g
to
n
u
m
b
e
r
o
f
s
o
lu
tio
n
g
e
n
er
atio
n
s
(
P
s
x
C
max
)
is
8
0
0
.
0
0
0
.
C
lear
ly
,
t
h
is
v
alu
e
is
v
er
y
h
ig
h
.
Fr
o
m
th
is
v
iew,
th
at
o
f
DUT
A
o
f
1
3
8
.
2
6
6
k
W
is
co
n
s
id
er
ed
as
th
e
b
est
r
esu
lt
as
co
m
p
ar
ed
to
o
th
er
m
eth
o
d
s
.
T
h
is
illu
s
tr
ates
th
e
p
er
f
o
r
m
an
ce
an
d
ef
f
ec
ti
v
en
ess
o
f
DUT
A.
Fu
r
th
er
m
o
r
e
,
th
e
v
o
ltag
e
p
r
o
f
ile
s
ig
n
if
ican
tly
h
as
b
ee
n
en
h
an
ce
d
with
in
s
tallatio
n
c
ap
ac
ito
r
s
as
s
h
o
wn
Fig
u
r
e
7
.
T
h
e
Fig
u
r
e
s
h
o
ws
th
e
in
f
lu
en
ce
o
f
c
o
m
p
en
s
ated
ca
p
ac
ito
r
s
o
n
th
e
v
o
ltag
e
o
f
3
3
b
u
s
s
y
s
tem
b
ef
o
r
e
an
d
af
ter
c
o
m
p
e
n
s
atio
n
ca
s
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Op
timiz
in
g
lo
ca
tio
n
a
n
d
s
iz
e
o
f c
a
p
a
cito
r
s
fo
r
p
o
w
er lo
s
s
r
e
d
u
ctio
n
in
r
a
d
ia
l..
.
(
Th
u
a
n
Th
a
n
h
N
g
u
ye
n
)
299
T
ab
le
4
.
R
esu
lt c
o
m
p
ar
is
o
n
b
e
twee
n
DUT
A
an
d
o
th
er
m
eth
o
d
s
with
s
in
g
le
ca
p
ac
ito
r
M
e
t
h
o
d
s
C
a
p
a
c
i
t
o
r
n
o
d
e
S
i
z
e
(
k
V
A
R
)
To
t
a
l
l
o
ss
kW
M
i
n
i
m
u
m
V
o
l
t
a
g
e
(
p
.
u
)
A
M
[
6
]
30
1
2
2
9
.
8
1
5
1
.
4
0
0
.
9
1
6
2
G
S
A
[
6
]
30
1
2
6
5
1
5
1
.
3
8
0
.
9
1
6
5
G
S
S
A
[
6
]
30
1
2
5
8
1
5
1
.
3
8
0
.
9
1
6
5
M
P
L
[
6
]
30
1
2
5
8
1
5
1
.
3
8
0
.
9
1
6
5
G
O
A
[
7
]
30
1
2
5
0
1
5
1
.
3
8
0
.
9
1
6
D
U
TA
30
1
2
5
8
.
0
1
1
5
1
.
3
7
9
0
.
9
1
6
4
8
T
ab
le
5
.
R
esu
lt c
o
m
p
ar
is
o
n
b
e
twee
n
DUT
A
an
d
o
th
er
m
eth
o
d
s
with
th
r
ee
ca
p
ac
ito
r
s
M
e
t
h
o
d
s
C
a
p
a
c
i
t
o
r
n
o
d
e
S
i
z
e
(
k
V
A
R
)
To
t
a
l
K
V
A
R
To
t
a
l
l
o
ss
(
k
W
)
M
i
n
i
m
u
m
V
o
l
t
a
g
e
(
p
.
u
)
P
s
C
m
ax
G
O
A
[
7
]
1
3
,
2
4
,
3
0
3
7
5
,
5
5
0
,
1
0
5
0
1
9
7
5
1
3
8
.
2
7
0
.
9
3
1
40
1
0
0
P
S
G
A
[
8
]
6
,
2
8
,
2
9
1
2
0
0
,
7
6
0
,
2
0
0
2
1
6
0
1
5
1
.
9
8
0
.
9
4
6
-
-
TSM
[
9
]
7
,
2
9
,
3
0
8
5
0
,
2
5
,
9
0
0
1
7
7
5
1
4
4
.
0
4
2
0
.
9
2
5
1
-
-
I
P
[
1
0
]
9
,
2
9
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3
0
4
5
0
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8
0
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9
0
0
2
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5
0
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1
.
7
8
0
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9
5
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S
A
[
1
0
]
1
0
,
3
0
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1
4
4
5
0
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3
5
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9
0
0
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7
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5
1
.
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5
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S
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[
1
0
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1
3
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2
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8
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3
5
0
1
6
0
0
1
3
4
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5
0
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9
6
7
2
2
0
0
0
4
0
0
F
P
A
[
1
1
]
1
3
,
2
4
,
3
0
4
5
0
,
4
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u
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e
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.
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h
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in
f
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n
ce
o
f
co
m
p
en
s
ated
ca
p
ac
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r
s
o
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th
e
v
o
ltag
e
o
f
th
e
3
3
b
u
s
s
y
s
tem
6.
CO
NCLU
SI
O
N
T
h
is
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r
k
is
to
u
s
e
DUT
A
f
o
r
th
e
m
o
s
t
ap
p
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o
p
r
iate
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tio
n
an
d
s
izin
g
o
f
f
ix
ed
ca
p
ac
ito
r
b
an
k
s
in
o
r
d
er
to
r
ed
u
ce
to
tal
lo
s
s
as we
ll a
s
im
p
r
o
v
e
th
e
v
o
ltag
e
p
r
o
f
ile
o
f
th
e
r
ad
ial
d
is
tr
ib
u
tio
n
n
e
two
r
k
.
T
h
e
ap
p
lied
m
eth
o
d
is
v
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if
ied
o
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s
tan
d
a
r
d
1
5
-
b
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s
a
n
d
3
3
-
b
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n
etwo
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s
with
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t
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y
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s
es.
R
esu
lts
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h
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ed
f
r
o
m
DUT
A
s
h
o
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at
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e
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o
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lo
s
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r
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ile
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e
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e
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r
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h
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.
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o
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e
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m
p
ar
is
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b
y
DUT
A
with
th
o
s
e
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y
r
ec
en
tly
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e
p
o
r
ted
m
eth
o
d
s
in
d
i
ca
te
th
at
th
e
m
eth
o
d
ca
n
attain
ap
p
r
o
x
im
ate
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r
b
etter
p
o
wer
l
o
s
s
f
o
r
all
test
ca
s
es.
T
h
er
ef
o
r
e,
it is
p
o
s
s
ib
le
to
im
p
ly
t
h
at
th
e
m
eth
o
d
s
h
o
u
l
d
b
e
r
eg
ar
d
e
d
as a
n
ef
f
ec
tiv
e
m
eth
o
d
f
o
r
s
o
l
v
in
g
OC
PS
D
p
r
o
b
lem
.
RE
F
E
R
E
NC
E
S
[1
]
M
.
H.
Ha
q
u
e
, “
Ca
p
a
c
it
o
r
p
lac
e
m
e
n
t
in
ra
d
ial
d
istri
b
u
ti
o
n
sy
ste
m
s fo
r
lo
ss
re
d
u
c
ti
o
n
,”
IEE
Pr
o
c
e
e
d
in
g
s
-
Ge
n
e
ra
ti
o
n
,
T
ra
n
sm
issio
n
a
n
d
Distrib
u
ti
o
n
,
v
o
l.
1
4
6
,
n
o
.
5
,
p
p
.
5
0
1
-
5
0
5
,
S
e
p
te
m
b
e
r
1
9
9
9
.
[2
]
K.
P
ra
k
a
sh
,
M
.
S
y
d
u
l
u
,
“
P
a
rti
c
le
s
wa
rm
o
p
ti
m
iza
ti
o
n
-
b
a
se
d
c
a
p
a
c
it
o
r
p
lac
e
m
e
n
t
o
n
ra
d
ial
d
istri
b
u
ti
o
n
sy
ste
m
s
,”
2
0
0
7
IEE
E
P
o
we
r E
n
g
in
e
e
rin
g
S
o
c
iety
Ge
n
e
ra
l
M
e
e
ti
n
g
,
J
u
n
e
2
0
0
7
.
[3
]
El
-
El
a
A.
A.,
Kin
a
wy
,
A.
M
.
,
M
o
u
wa
fi,
M
.
T.
,
&
El
-
S
e
h
iem
y
,
R.
A.
,
“
Op
ti
m
a
l
sitt
i
n
g
a
n
d
siz
in
g
o
f
c
a
p
a
c
it
o
rs
fo
r
v
o
lt
a
g
e
e
n
h
a
n
c
e
m
e
n
t
o
f
d
istri
b
u
ti
o
n
s
y
ste
m
s
,”
2
0
1
5
5
0
th
I
n
ter
n
a
ti
o
n
a
l
Un
ive
rs
it
ies
Po
we
r
En
g
in
e
e
rin
g
C
o
n
fer
e
n
c
e
(UPE
C)
,
S
e
p
tem
b
e
r
2
0
1
5
.
[4
]
Re
d
d
y
V.
V.
K.,
&
S
y
d
u
lu
,
M
.
,
“
2
In
d
e
x
a
n
d
G
A
b
a
se
d
o
p
ti
m
a
l
l
o
c
a
ti
o
n
a
n
d
siz
in
g
o
f
d
istri
b
u
ti
o
n
sy
st
e
m
c
a
p
a
c
it
o
rs
,
”
2
0
0
7
IE
EE
P
o
we
r E
n
g
in
e
e
rin
g
S
o
c
iety
Ge
n
e
ra
l
M
e
e
ti
n
g
,
J
u
n
e
2
0
0
7
.
[5
]
Ali
E.
S
.
,
El
a
z
im,
S
.
A.,
&
Ab
d
e
laz
iz,
A.
Y
.
,
“
Im
p
ro
v
e
d
h
a
rm
o
n
y
a
l
g
o
rit
h
m
a
n
d
p
o
we
r
lo
ss
in
d
e
x
fo
r
o
p
ti
m
a
l
lo
c
a
ti
o
n
s
a
n
d
siz
in
g
o
f
c
a
p
a
c
it
o
rs
in
ra
d
ial
d
istr
ib
u
ti
o
n
sy
ste
m
s
,”
In
t
e
rn
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
P
o
we
r
&
En
e
rg
y
S
y
ste
ms
,
v
o
l.
8
0
,
p
p
.
25
2
-
2
6
3
,
S
e
p
tem
b
e
r
2
0
1
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
1
6
9
3
-
6
9
3
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T
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KOM
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KA
T
elec
o
m
m
u
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C
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m
p
u
t E
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n
tr
o
l
,
Vo
l.
19
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
1
:
2
9
3
-
3
0
0
300
[6
]
Am
a
n
M
.
M
.
,
e
t
a
l
.
,
“
Op
ti
m
u
m
sh
u
n
t
c
a
p
a
c
it
o
r
p
lac
e
m
e
n
t
in
d
istri
b
u
ti
o
n
sy
ste
m
—
A
re
v
iew
a
n
d
c
o
m
p
a
ra
ti
v
e
stu
d
y
,
”
Ren
e
wa
b
le
a
n
d
S
u
sta
in
a
b
le E
n
e
rg
y
Rev
iews
,
v
o
l
.
3
0
,
p
p
.
4
2
9
-
4
3
9
,
2
0
1
4
.
[7
]
El
sa
y
e
d
A.
M
.
,
e
t
a
l
.,
“
Op
ti
m
a
l
a
l
lo
c
a
ti
o
n
a
n
d
c
o
n
tro
l
o
f
fix
e
d
a
n
d
sw
it
c
h
e
d
c
a
p
a
c
it
o
r
b
a
n
k
s o
n
d
istri
b
u
ti
o
n
s
y
ste
m
s
u
sin
g
g
ra
ss
h
o
p
p
e
r
o
p
ti
m
isa
ti
o
n
a
lg
o
rit
h
m
wit
h
p
o
we
r
l
o
ss
se
n
s
it
iv
it
y
a
n
d
ro
u
g
h
se
t
t
h
e
o
,
”
IE
T
Ge
n
e
ra
ti
o
n
,
T
ra
n
sm
issio
n
&
Distrib
u
ti
o
n
,
v
o
l.
13
,
n
o
.
1
7
,
p
p
.
3
8
6
3
-
38
78
,
2
0
1
9
.
[8
]
S
a
rm
a
A.
K.,
Ra
fi
K.
M
.
,
“
Op
t
ima
l
se
lec
ti
o
n
o
f
c
a
p
a
c
it
o
rs
fo
r
ra
d
ial
d
istri
b
u
ti
o
n
sy
ste
m
s
u
sin
g
p
lan
t
g
r
o
wt
h
sim
u
latio
n
a
lg
o
r
it
h
m
,”
I
n
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
A
d
v
a
n
c
e
d
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
p
p
.
43
-
54
,
Ja
n
u
a
ry
2
0
1
1
.
[9
]
S
a
rm
a
A.
K.,
&
Ra
fi
K.
M
.
,
“
Op
t
ima
l
se
lec
ti
o
n
o
f
c
a
p
a
c
it
o
rs
f
o
r
ra
d
ial
d
istri
b
u
ti
o
n
sy
ste
m
s
u
si
n
g
p
lan
t
g
ro
wt
h
sim
u
latio
n
a
lg
o
r
it
h
m
,”
I
n
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
A
d
v
a
n
c
e
d
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
pp
43
-
54
,
Ja
n
u
a
ry
2
0
1
1
.
[1
0
]
S
h
u
a
i
b
Y.
M
.
,
Ka
lav
a
t
h
i
M
.
S
.
,
&
Ra
jan
C.
C.
A
.
,
“
Op
ti
m
a
l
c
a
p
a
c
it
o
r
p
la
c
e
m
e
n
t
in
ra
d
ial
d
istri
b
u
t
io
n
s
y
ste
m
u
sin
g
g
ra
v
it
a
ti
o
n
a
l
se
a
rc
h
a
lg
o
rit
h
m
,
”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
Po
we
r
&
E
n
e
rg
y
S
y
ste
ms
,
v
o
l.
6
4
,
p
p
.
3
8
4
-
3
9
7
,
Ja
n
u
a
ry
2
0
1
5
.
[1
1
]
Tam
il
se
lv
a
n
V.,
Ja
y
a
b
a
ra
th
i
T
.
,
R
a
g
h
u
n
a
th
a
n
,
T
.
,
&
Ya
n
g
X.
S
.
,
“
Op
ti
m
a
l
c
a
p
a
c
it
o
r
p
lac
e
m
e
n
t
i
n
ra
d
ial
d
istri
b
u
ti
o
n
sy
ste
m
s u
sin
g
fl
o
we
r
p
o
ll
i
n
a
ti
o
n
a
lg
o
rit
h
m
,
”
Al
e
x
a
n
d
ria
e
n
g
in
e
e
ri
n
g
jo
u
rn
a
l
,
v
o
l
.
57
,
n
o
.
4
,
p
p
.
2
7
7
5
-
2
7
8
6
,
Ja
n
u
a
ry
2
0
1
5
.
[1
2
]
G
e
o
rg
e
T.
,
Y
o
u
ss
e
f
A.
R.
,
E
b
e
e
d
M
.
,
&
Ka
m
e
l
S
.
,
“
An
t
li
o
n
o
p
ti
m
i
z
a
ti
o
n
tec
h
n
i
q
u
e
f
o
r
o
p
ti
m
a
l
c
a
p
a
c
it
o
r
p
lac
e
m
e
n
t
b
a
se
d
o
n
t
o
tal
c
o
st
a
n
d
p
o
we
r
lo
ss
m
in
imiz
a
ti
o
n
,”
2
0
1
8
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
In
n
o
v
a
ti
v
e
T
re
n
d
s
in
Co
m
p
u
ter
En
g
i
n
e
e
rin
g
(
IT
CE)
,
p
p
.
3
5
0
-
3
5
8
,
F
e
b
ru
a
ry
2
0
1
8
.
[1
3
]
Du
o
n
g
M
.
Q.,
e
t
a
l.
,
“
Co
m
b
in
a
ti
o
n
o
f
K
-
M
e
a
n
c
lu
ste
ri
n
g
a
n
d
e
lb
o
w
tec
h
n
i
q
u
e
i
n
m
it
ig
a
ti
n
g
lo
ss
e
s
o
f
d
istri
b
u
t
io
n
n
e
two
rk
,”
G
M
S
A
RN
In
ter
n
a
ti
o
n
a
l
Jo
u
r
n
a
l
,
v
o
l
.
13
,
p
p
.
1
5
3
-
1
5
8
,
2
0
1
9
.
[1
4
]
Wale
e
d
Kh
a
li
d
S
h
a
k
ir
Al
-
Ju
b
o
ri,
Ali
Na
ss
e
r
Hu
ss
a
in
,
“
Op
ti
m
u
m
re
a
c
ti
v
e
p
o
we
r
c
o
m
p
e
n
sa
ti
o
n
f
o
r
d
is
tri
b
u
t
io
n
sy
ste
m
u
sin
g
d
o
l
p
h
i
n
a
lg
o
rit
h
m
c
o
n
sid
e
rin
g
d
iffere
n
t
lo
a
d
m
o
d
e
ls
,”
I
n
t
e
rn
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
En
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
5
,
p
p
.
5
0
3
2
-
5
0
4
7
,
Oc
to
b
e
r
2
0
2
0
.
[1
5
]
An
g
S
.
,
e
t
a
l
.
,
“
S
in
e
c
o
si
n
e
a
lg
o
ri
t
h
m
fo
r
o
p
ti
m
a
l
p
lac
e
m
e
n
t
a
n
d
siz
i
n
g
o
f
d
istri
b
u
te
d
g
e
n
e
ra
ti
o
n
i
n
ra
d
ial
d
istri
b
u
ti
o
n
n
e
two
rk
,”
G
M
S
A
RN
In
ter
n
a
ti
o
n
a
l
Jo
u
r
n
a
l
,
v
o
l
.
12
,
p
p
.
2
0
2
-
2
1
2
,
De
c
e
m
b
e
r
2
0
1
8
.
[1
6
]
M
o
h
a
m
m
e
d
i
R.
D.,
e
t
a
l
.,
“
Op
ti
m
u
m
n
e
two
rk
re
c
o
n
fi
g
u
ra
ti
o
n
u
si
n
g
g
re
y
wo
lf
o
p
ti
m
ize
r
,
”
T
EL
KO
M
NIKA
T
e
lec
o
mm
u
n
ica
ti
o
n
Co
mp
u
ti
n
g
E
lec
tro
n
ics
a
n
d
C
o
n
tr
o
l
,
v
o
l
.
16
,
n
o
.
5
,
p
p
.
2
4
2
8
-
24
,
S
e
p
tem
b
e
r
2
0
1
8
.
[1
7
]
M
m
a
ry
E.
R.
,
M
a
ru
n
g
sri,
B.
,
“
I
n
teg
ra
ti
o
n
o
f
m
u
lt
i
-
re
n
e
wa
b
le
e
n
e
rg
y
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
a
n
d
b
a
tt
e
ry
i
n
ra
d
ial
d
istri
b
u
ti
o
n
n
e
two
r
k
s
,”
GM
S
AR
N
In
ter
n
a
ti
o
n
a
l
Jo
u
r
n
a
l
,
v
o
l.
12
,
p
p
.
1
9
4
-
2
0
1
,
2
0
1
8
.
[1
8
]
M
m
a
ry
E.
R.
,
&
M
a
ru
n
g
sri,
B.
,
"
M
u
lt
i
o
b
jec
ti
v
e
o
p
ti
m
iza
ti
o
n
o
f
re
n
e
wa
b
le
d
istr
ib
u
ted
g
e
n
e
ra
ti
o
n
a
n
d
sh
u
n
t
c
a
p
a
c
it
o
r
fo
r
tec
h
n
o
-
e
c
o
n
o
m
ic
a
n
a
ly
sis
u
si
n
g
h
y
b
ri
d
i
n
v
a
siv
e
we
e
d
s
o
p
ti
m
i
z
a
ti
o
n
,
”
GM
S
AR
N
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
,
v
o
l
.
12
,
p
p
.
24
-
33
,
Ja
n
u
a
ry
2
0
1
8
.
[1
9
]
S
a
fit
ri
N
.,
“
No
n
-
u
n
if
o
rm
ro
o
ft
o
p
P
Vs
d
istri
b
u
t
io
n
e
ffe
c
t
to
imp
ro
v
e
v
o
lt
a
g
e
p
r
o
fil
e
in
re
si
d
e
n
ti
a
l
fe
e
d
e
r
,
”
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ica
t
i
o
n
Co
m
p
u
t
in
g
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l
,
v
o
l
.
1
6
,
n
o
.
4
,
p
p
.
1
3
8
8
-
1
3
9
5
,
Au
g
u
st
2
0
1
8
.
[2
0
]
Ka
d
o
m
H.
F
.
,
e
t
a
l
.
,
“
Du
a
l
te
c
h
n
iq
u
e
o
f
re
c
o
n
fig
u
ra
ti
o
n
a
n
d
c
a
p
a
c
it
o
r
p
lac
e
m
e
n
t
fo
r
d
istri
b
u
ti
o
n
sy
ste
m
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
&
Co
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l
.
1
0
,
n
o
.
1
,
p
p
.
8
0
-
9
0
,
F
e
b
ru
a
ry
2
0
2
0
.
[2
1
]
Alh
a
m
ro
u
n
i
M
.
I.
,
e
t
a
l
.
,
“
Lo
a
d
fl
o
w
-
b
a
se
d
v
o
lt
a
g
e
sta
b
il
it
y
in
d
ice
s
fo
r
v
o
l
tag
e
sta
b
il
it
y
a
n
d
c
o
n
ti
n
g
e
n
c
y
a
n
a
ly
sis
fo
r
o
p
ti
m
a
l
lo
c
a
ti
o
n
o
f
sta
tco
m
i
n
d
istri
b
u
t
io
n
n
e
two
r
k
with
i
n
teg
ra
t
e
d
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
u
n
it
,
”
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ica
ti
o
n
Co
mp
u
ti
n
g
E
lec
tro
n
ics
a
n
d
C
o
n
tr
o
l
,
v
o
l
.
1
6
,
n
o
.
5
,
p
p
.
2
3
0
2
-
2
3
1
5
,
Oc
to
b
e
r
2
0
1
8
.
[2
2
]
T.
P
h
a
n
Va
n
Ho
n
g
,
T.
Tran
T
h
e
,
“
Eco
n
o
m
ic
d
isp
a
tch
i
n
m
icro
g
rid
u
sin
g
st
o
c
h
a
stic
fra
c
tal
se
a
rc
h
a
lg
o
ri
th
m
,
”
GM
S
AR
N
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
,
p
p
.
1
0
2
-
1
1
5
,
S
e
p
tem
b
e
r
2
0
1
7
.
[2
3
]
S
a
li
m
i
H.
,
“
S
to
c
h
a
stic
fra
c
tal
se
a
rc
h
:
a
p
o
we
rfu
l
m
e
tah
e
u
risti
c
a
lg
o
rit
h
m
,
”
K
n
o
wled
g
e
-
Ba
se
d
S
y
s
tem
s
,
v
o
l
.
7
5
,
p
p
.
1
-
1
8
,
F
e
b
r
u
a
ry
2
0
1
5
.
[2
4
]
P
h
a
m
L.
H.,
e
t
a
l
.
,
“
S
t
o
c
h
a
sti
c
fra
c
tal
se
a
rc
h
-
b
a
se
d
m
e
th
o
d
fo
r
e
c
o
n
o
m
ic
l
o
a
d
d
is
p
a
tch
,
”
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ica
ti
o
n
Co
mp
u
ti
n
g
E
lec
tro
n
ics
a
n
d
C
o
n
tr
o
l
,
v
o
l
.
1
7
,
n
o
.
5
,
p
p
.
2
5
3
5
-
2
5
4
6
,
Oc
to
b
e
r
2
0
1
9
.
[2
5
]
Va
n
Tran
H.,
e
t
a
l
.
,
“
F
i
n
d
i
n
g
o
p
t
i
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
so
l
u
t
io
n
s b
y
u
sin
g
a
n
o
v
e
l
imp
ro
v
e
d
st
o
c
h
a
stic frac
tal
se
a
rc
h
o
p
ti
m
iza
ti
o
n
a
lg
o
r
it
h
m
,
”
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ica
ti
o
n
Co
mp
u
ti
n
g
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l
,
v
o
l.
1
7
,
n
o
.
5
,
p
p
.
2
5
1
7
-
2
5
2
6
,
Oc
t
o
b
e
r
2
0
1
9
.
[2
6
]
S
o
h
a
l
M
.
,
e
t
a
l
.
,
“
A
fra
m
e
wo
rk
f
o
r
o
p
ti
m
izi
n
g
d
istri
b
u
ted
d
a
tab
a
se
q
u
e
ries
b
a
se
d
o
n
sto
c
h
a
stic
fra
c
tal
se
a
rc
h
,
”
In
t.
J
.
Co
mp
.
S
c
.
a
n
d
M
o
b
.
Co
m
p
u
ti
n
g
(
J
CS
M
C)
,
v
o
l
.
4
,
n
o
.
6
,
p
p
.
5
4
4
-
55
,
Ja
n
u
a
ry
2
0
1
5
.
[2
7
]
Au
g
u
g
li
a
ro
A
.
,
e
t
a
l
.
,
“
A
b
a
c
k
wa
rd
sw
e
e
p
m
e
th
o
d
f
o
r
p
o
we
r
flo
w
so
lu
ti
o
n
in
d
istri
b
u
ti
o
n
n
e
two
r
k
s
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
P
o
we
r &
E
n
e
rg
y
S
y
ste
ms
,
v
o
l.
32
,
n
o
.
4
,
p
p
.
2
7
1
-
2
8
0
,
M
a
y
2
0
1
0
.
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