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Appl
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(
I
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Vo
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14
,
No
.
4
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Dec
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20
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p
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s
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n
su
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IEE
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3
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it
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it
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tec
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s.
K
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w
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s
:
I
r
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Op
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m
al
ca
p
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p
lace
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P
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Salp
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T
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u
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e
CC B
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li
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C
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s
p
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A
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:
O
m
ar
Mu
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a
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Ned
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Dep
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m
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P
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c
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1.
I
NT
RO
D
UCT
I
O
N
T
h
e
in
cr
ea
s
i
n
g
d
e
m
a
n
d
f
o
r
elec
tr
icit
y
,
co
u
p
led
w
it
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li
m
ited
e
x
p
an
s
io
n
in
g
e
n
er
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a
n
d
tr
an
s
m
is
s
io
n
i
n
f
r
as
tr
u
ct
u
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e,
p
o
s
es
a
s
i
g
n
if
ican
t
ch
a
llen
g
e
to
m
o
d
er
n
elec
tr
ical
n
et
w
o
r
k
s
.
A
ls
o
,
th
e
c
o
n
ti
n
u
o
u
s
ad
v
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m
en
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o
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m
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o
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t
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ca
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s
i
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g
elec
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d
is
tr
ib
u
tio
n
n
et
w
o
r
k
s
to
g
r
o
w
[
1
]
.
T
h
is
u
lti
m
atel
y
r
es
u
lt
s
in
t
h
e
n
et
w
o
r
k
's
w
ea
k
n
ess
b
ec
au
s
e
p
o
w
er
lo
s
s
r
is
e,
th
e
v
o
ltag
e
p
r
o
f
ile
d
r
asti
ca
ll
y
d
r
o
p
s
,
an
d
t
h
e
c
u
r
r
en
t
s
f
lo
w
i
n
g
t
h
r
o
u
g
h
t
h
e
s
y
s
te
m
's
b
r
an
ch
es i
n
cr
ea
s
es o
v
er
w
h
at
i
s
estee
m
ed
[
2
]
.
E
n
h
a
n
ci
n
g
r
ad
ial
d
is
tr
ib
u
tio
n
n
et
w
o
r
k
(
R
D
N)
d
ep
en
d
ab
ilit
y
is
s
ig
n
i
f
ica
n
t
f
o
r
th
e
o
v
er
all
s
tab
ilit
y
o
f
th
e
elec
tr
ical
p
o
w
er
n
et
w
o
r
k
[
3
]
.
T
h
er
e
ar
e
v
ar
io
u
s
m
e
th
o
d
s
to
i
m
p
r
o
v
e
an
d
en
h
a
n
ce
th
e
e
f
f
icien
c
y
a
n
d
p
er
f
o
r
m
a
n
ce
o
f
R
DN
s
,
in
clu
d
in
g
o
p
ti
m
al
d
is
tr
ib
u
ted
g
en
er
atio
n
(
DG)
p
lace
m
en
t
[
4
]
,
n
et
w
o
r
k
r
ec
o
n
f
i
g
u
r
atio
n
,
an
d
o
p
ti
m
al
s
h
u
n
t
ca
p
ac
ito
r
s
(
SC
s
)
p
lace
m
en
t
[
5
]
.
E
ac
h
o
f
th
e
s
e
m
e
t
h
o
d
s
o
f
f
er
s
d
is
ti
n
ct
ad
v
an
ta
g
es a
n
d
ch
alle
n
g
es
[
6
]
.
Ho
w
e
v
er
,
DG
p
lace
m
en
t
h
elp
s
r
ed
u
ce
lo
s
s
e
s
b
y
g
e
n
er
atin
g
p
o
w
er
clo
s
er
to
d
e
m
an
d
[
7
]
.
A
d
d
itio
n
al
l
y
,
in
teg
r
ati
n
g
DG
in
to
e
x
i
s
ti
n
g
g
r
id
s
ca
n
b
e
c
o
m
p
le
x
a
n
d
co
s
t
l
y
,
m
a
k
i
n
g
i
t
les
s
co
s
t
-
e
f
f
ec
tiv
e
co
m
p
ar
ed
to
SC
s
[
8
]
,
[
9
]
.
R
ec
o
n
f
i
g
u
r
atio
n
o
p
ti
m
ize
s
p
o
w
e
r
f
lo
w
b
y
ch
a
n
g
i
n
g
t
h
e
n
et
w
o
r
k
to
p
o
lo
g
y
,
w
h
ic
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8792
A
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S
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tech
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iq
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fo
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p
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p
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tab
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[
1
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.
Ho
w
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v
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,
r
ec
o
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f
i
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u
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n
i
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lu
tio
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o
f
f
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ed
b
y
SC
s
[
1
1
]
,
[
1
2
]
.
Am
o
n
g
v
ar
io
u
s
av
a
ilab
le
m
et
h
o
d
s
,
o
p
tim
a
ll
y
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laci
n
g
a
n
d
s
i
zin
g
SC
s
is
w
id
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y
u
s
ed
f
o
r
it
s
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to
m
iti
g
ate
p
o
w
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s
s
es
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d
e
n
h
a
n
ce
v
o
lta
g
e
p
r
o
f
ile
s
t
h
r
o
u
g
h
r
ea
cti
v
e
p
o
w
er
co
m
p
e
n
s
atio
n
[
1
3
]
.
Sev
er
a
l
tech
n
iq
u
es
h
av
e
b
ee
n
p
r
o
p
o
s
ed
to
en
h
an
ce
R
DN
p
er
f
o
r
m
a
n
ce
,
w
ith
SC
p
lace
m
e
n
t
e
m
er
g
in
g
as
o
n
e
o
f
th
e
m
o
s
t
co
m
m
o
n
ap
p
r
o
ac
h
es
[
1
4
]
.
SC
s
ar
e
s
tr
ateg
ical
l
y
p
lace
d
in
R
DNs
to
r
ed
u
ce
lo
s
s
th
r
o
u
g
h
r
ea
cti
v
e
p
o
w
er
co
m
p
e
n
s
at
io
n
,
w
h
ic
h
b
ec
o
m
es
in
cr
ea
s
in
g
l
y
i
m
p
o
r
tan
t
a
s
en
er
g
y
co
n
s
er
v
at
io
n
i
s
p
r
io
r
itized
[
1
5
]
,
[
1
6
]
.
An
o
th
er
b
e
n
ef
i
t
o
f
ca
p
ac
ito
r
b
an
k
s
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n
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n
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a
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th
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p
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ile
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n
d
lib
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ate
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ee
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er
ca
p
ac
it
y
f
o
r
b
etter
s
y
s
te
m
u
tili
za
tio
n
[
1
7
]
.
Ho
w
e
v
er
,
i
m
p
r
o
p
er
SC
s
p
lace
m
en
t
ca
n
r
es
u
lt
in
s
u
b
o
p
ti
m
al
p
er
f
o
r
m
a
n
ce
,
s
u
c
h
as
h
ig
h
er
lo
s
s
es
an
d
v
o
ltag
e
d
r
o
p
s
,
th
er
eb
y
h
i
g
h
l
ig
h
ti
n
g
t
h
e
i
m
p
o
r
tan
ce
o
f
o
p
ti
m
izin
g
th
eir
lo
ca
tio
n
s
an
d
s
izes
[
1
8
]
.
T
h
e
is
s
u
e
o
f
id
en
ti
f
y
i
n
g
o
p
ti
m
al
p
o
s
itio
n
s
an
d
r
atin
g
s
o
f
SC
s
in
R
DN
s
k
n
o
w
n
as
o
p
ti
m
a
l
ca
p
ac
ito
r
p
lace
m
e
n
t
(
OC
P
)
p
r
o
b
lem
,
p
o
s
es
a
s
i
g
n
if
ican
t
ch
alle
n
g
e
f
o
r
R
DN
o
p
er
ato
r
s
.
T
h
is
ch
a
llen
g
e
ar
i
s
es
b
ec
a
u
s
e
th
i
s
p
r
o
b
le
m
i
s
a
co
m
b
i
n
ato
r
ial
in
n
a
tu
r
e
an
d
m
u
s
t
ad
d
r
ess
m
u
ltip
le
tech
n
i
ca
l
o
b
j
ec
tiv
es
(
p
o
w
er
lo
s
s
,
v
o
ltag
e
p
r
o
f
ile)
an
d
ec
o
n
o
m
ic
co
n
s
id
er
atio
n
(
co
s
t,
m
ai
n
ten
a
n
ce
)
[
1
9
]
.
As
a
r
es
u
lt,
i
n
latest
y
ea
r
s
,
a
v
ar
iet
y
o
f
o
p
ti
m
izatio
n
ap
p
r
o
ac
h
es
h
av
e
b
ee
n
r
ec
o
m
m
e
n
d
ed
f
o
r
f
in
d
i
n
g
th
e
b
etter
s
o
lu
tio
n
f
o
r
OC
P
p
r
o
b
lem
s
i
n
R
DN
s
i
n
o
r
d
er
to
m
ax
i
m
ize
t
h
eir
b
en
e
f
it
s
.
Ma
n
y
m
et
h
o
d
s
h
a
v
e
b
ee
n
d
ev
elo
p
ed
f
o
r
tack
lin
g
th
e
O
C
P
p
r
o
b
lem
,
w
h
ic
h
h
a
s
g
o
tte
n
m
o
r
e
atte
n
tio
n
f
r
o
m
r
esear
c
h
er
s
.
Fo
r
ad
d
r
ess
in
g
th
e
OC
P
p
r
o
b
lem
,
t
w
o
tec
h
n
i
q
u
es
d
ep
e
n
d
en
t
o
n
lo
s
s
s
e
n
s
i
t
iv
it
y
f
ac
to
r
s
(
L
SF
s
)
f
o
r
d
ec
id
in
g
o
p
ti
m
al
p
lace
s
an
d
th
e
p
la
n
t
g
r
o
w
th
s
i
m
u
lati
o
n
alg
o
r
it
h
m
(
P
GS
A
)
f
o
r
ca
lc
u
lati
n
g
o
p
ti
m
al
ca
p
ac
ito
r
ca
p
ac
ities
w
a
s
u
s
ed
i
n
[
2
0
]
.
Ho
w
e
v
er
,
s
u
ch
m
et
h
o
d
s
ar
e
li
m
ited
to
ca
p
ac
ito
r
s
i
zin
g
alo
n
e,
a
n
d
a
h
o
lis
tic
s
o
lu
tio
n
is
o
f
te
n
n
o
t
ac
h
iev
ed
.
Sev
er
al
o
t
h
er
m
e
th
o
d
s
,
s
u
c
h
a
s
d
ir
ec
t sear
ch
al
g
o
r
ith
m
(
DS
A
)
h
a
v
e
b
ee
n
ap
p
lied
to
id
en
ti
f
y
th
e
OC
P
f
o
r
m
ax
i
m
izin
g
n
et
s
a
v
in
g
s
an
d
d
i
m
i
n
is
h
i
n
g
ac
t
u
al
p
o
w
er
lo
s
s
[
2
1
]
.
T
h
e
m
i
n
e
b
last
al
g
o
r
ith
m
(
MB
A
)
w
a
s
ap
p
lied
to
OC
P
p
r
o
b
le
m
b
y
E
l
az
i
m
a
n
d
Ali
[
2
2
]
.
I
n
th
e
f
ir
s
t
p
h
ase,
L
SF
i
s
u
s
ed
to
lo
ca
te
s
u
ch
b
u
s
es,
f
o
llo
w
ed
b
y
MB
A
to
o
p
ti
m
ize
b
o
th
th
e
SC
's
ca
p
ac
ities
as
w
ell
as
th
eir
p
o
s
itio
n
s
.
Fo
r
s
o
lv
i
n
g
th
e
OC
P
p
r
o
b
lem
,
Yo
u
s
se
f
et
a
l.
[
2
3
]
c
o
m
b
i
n
ed
th
e
s
alp
s
w
ar
m
al
g
o
r
ith
m
(
S
S
A
)
w
i
th
L
S
F
f
o
r
o
p
ti
m
al
lo
ca
tio
n
s
an
d
s
ize
s
o
f
SC
s
.
I
n
a
d
if
f
er
en
t
ap
p
r
o
ac
h
,
a
h
y
b
r
id
s
tr
ateg
y
f
o
r
ca
p
ac
ito
r
p
o
s
itio
n
i
n
g
an
d
s
izi
n
g
i
n
R
D
Ns
w
a
s
p
r
o
p
o
s
ed
b
y
co
m
b
i
n
i
n
g
a
f
u
zz
y
e
x
p
er
t s
y
s
t
e
m
(
FES)
an
d
t
h
e
d
r
ag
o
n
f
l
y
al
g
o
r
ith
m
(
D
A
)
[
2
4
]
.
A
b
d
elsala
m
an
d
Ma
n
s
o
u
r
[
2
5
]
em
p
lo
y
ed
th
e
s
i
n
e
co
s
in
e
alg
o
r
ith
m
(
S
C
A
)
to
m
a
x
i
m
ize
p
r
o
f
it
th
r
o
u
g
h
eli
m
i
n
ati
n
g
e
n
er
g
y
lo
s
s
,
r
ed
u
cin
g
ca
p
ac
ito
r
in
v
es
t
m
en
t
co
s
ts
,
an
d
i
m
p
r
o
v
i
n
g
d
ep
en
d
ab
ilit
y
.
I
n
th
eir
ap
p
r
o
ac
h
,
L
SF
w
er
e
u
s
ed
f
o
r
f
i
n
d
in
g
th
e
m
o
s
t
s
e
n
s
iti
v
e
b
u
s
es
f
o
r
SC
s
p
lace
m
e
n
t,
en
s
u
r
i
n
g
o
p
ti
m
al
ca
p
ac
ito
r
p
lace
m
en
t
[
2
5
]
.
A
n
o
v
el
h
y
b
r
id
tech
n
iq
u
e
b
ased
o
n
co
m
b
i
n
ed
a
g
en
etic
a
lg
o
r
it
h
m
(
G
A
)
w
it
h
a
n
e
w
s
tab
ilit
y
in
d
ex
w
a
s
u
s
ed
to
s
o
l
v
e
t
h
e
OC
P
in
R
DN
s
,
ai
m
in
g
to
r
ed
u
ce
s
lo
s
s
a
n
d
i
m
p
r
o
v
e
s
v
o
lta
g
e
s
tab
ili
t
y
[
2
6
]
.
I
n
th
eir
h
y
b
r
id
m
e
th
o
d
,
th
e
o
p
tim
a
l
s
ite
s
o
f
th
e
ca
p
ac
ito
r
s
ar
e
id
en
tif
ied
b
y
th
e
b
u
s
v
o
lt
ag
e
s
tab
ilit
y
in
d
e
x
(
B
VSI
)
,
w
h
ile
t
h
e
G
A
i
s
e
m
p
l
o
y
ed
to
ca
lcu
la
te
t
h
e
o
p
ti
m
al
ca
p
ac
ito
r
s
s
ize.
R
ec
e
n
t
s
t
u
d
ie
s
b
y
[
2
7
]
-
[
2
9
]
h
a
v
e
ad
v
an
ce
d
th
e
o
p
ti
m
izatio
n
o
f
SC
allo
ca
tio
n
b
y
i
n
te
g
r
ati
n
g
t
h
e
m
s
i
m
u
l
tan
e
o
u
s
l
y
w
it
h
elec
t
r
ic
v
eh
icle
c
h
ar
g
i
n
g
s
tatio
n
s
f
o
r
en
h
an
c
in
g
d
is
tr
ib
u
tio
n
s
y
s
te
m
r
eliab
il
it
y
an
d
e
co
n
o
m
ic
p
er
f
o
r
m
an
ce
.
T
h
eir
h
y
b
r
id
o
p
ti
m
izatio
n
ap
p
r
o
ac
h
es
ad
d
r
ess
b
o
th
tech
n
ical
a
n
d
f
i
n
a
n
cial
o
b
j
ec
tiv
es,
f
o
cu
s
in
g
o
n
o
p
ti
m
al
S
C
s
a
s
s
ig
n
m
e
n
t
a
n
d
s
izi
n
g
elec
tr
ic
v
eh
ic
le
.
I
n
li
g
h
t
o
f
t
h
e
ab
o
v
e
d
is
cu
s
s
io
n
,
a
co
m
p
ar
ati
v
e
s
u
m
m
ar
y
o
f
t
h
e
m
o
s
t
n
o
tab
le
o
p
ti
m
izat
io
n
ap
p
r
o
ac
h
es u
s
ed
in
p
r
ev
io
u
s
OC
P
s
t
u
d
ies
is
p
r
esen
ted
i
n
T
ab
le
1
.
W
h
ile
t
h
e
s
e
m
et
h
o
d
s
o
f
f
er
p
ar
tial so
l
u
tio
n
s
,
m
o
s
t
s
t
u
d
ies
h
a
v
e
li
m
itatio
n
s
.
T
h
ese
lim
itati
o
n
s
in
cl
u
d
e
th
e
lack
o
f
co
n
s
id
er
atio
n
f
o
r
b
u
s
v
o
ltag
e
an
d
ca
p
ac
ito
r
ca
p
ac
ity
co
n
s
tr
ai
n
ts
,
alo
n
g
w
it
h
t
h
e
o
m
i
s
s
io
n
o
f
r
ep
air
an
d
o
p
er
atin
g
co
s
t
s
i
n
t
h
e
o
v
er
all
ca
p
ac
ito
r
co
s
t
esti
m
ates.
A
d
d
itio
n
all
y
,
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
i
n
p
r
io
r
s
tu
d
ies
ar
e
t
y
p
icall
y
tr
ea
ted
in
d
ep
en
d
en
tl
y
,
f
o
cu
s
in
g
o
n
lo
s
s
m
iti
g
atio
n
o
r
co
s
t
r
ed
u
ctio
n
as
s
ep
ar
ate
g
o
als,
r
ath
er
th
a
n
i
n
te
g
r
atin
g
b
o
th
in
to
a
u
n
i
f
ied
f
r
a
m
e
w
o
r
k
.
Mo
r
eo
v
er
,
ca
p
ac
ito
r
p
lace
m
e
n
t
in
m
an
y
p
r
io
r
s
tu
d
ies
i
s
o
f
te
n
b
ased
o
n
s
en
s
iti
v
it
y
f
ac
to
r
s
s
u
c
h
as
t
h
e
L
S
F.
W
h
ile
t
h
ese
f
ac
to
r
s
ca
n
p
r
o
v
id
e
a
p
r
elim
i
n
ar
y
e
s
ti
m
a
te,
th
e
y
h
a
v
e
b
ee
n
f
o
u
n
d
to
b
e
less
r
eliab
le
a
n
d
m
a
y
n
o
t
al
w
a
y
s
r
es
u
lt
i
n
o
p
ti
m
al
ca
p
ac
ito
r
p
o
s
itio
n
in
g
[
3
0
]
.
A
n
o
th
er
i
m
p
o
r
tan
t
li
m
i
tat
io
n
in
th
e
c
u
r
r
en
t
r
esear
ch
is
t
h
e
i
n
s
u
f
f
icien
t e
m
p
h
asis
o
n
r
ea
l
w
o
r
ld
o
r
p
r
ac
tic
al
R
DN
s
.
Mo
s
t st
u
d
ies ar
e
co
n
d
u
cted
o
n
s
ta
n
d
ar
d
test
R
D
Ns,
w
h
ich
o
f
ten
f
ail
to
r
ef
lect
th
e
co
m
p
lex
i
ties
a
n
d
o
p
er
atio
n
al
ch
alle
n
g
e
s
in
ac
t
u
al
R
DNs
[
3
1
]
.
T
o
a
d
d
r
ess
th
e
li
m
ita
tio
n
s
o
f
p
r
io
r
s
tu
d
ie
s
,
th
is
r
ese
ar
ch
p
r
o
p
o
s
es
a
n
o
v
el
ap
p
r
o
ac
h
th
a
t
s
i
m
u
lta
n
eo
u
s
l
y
ac
co
u
n
ts
f
o
r
tech
n
ical
an
d
ec
o
n
o
m
ic
b
en
ef
its
,
i
n
cl
u
d
in
g
p
o
w
er
lo
s
s
r
ed
u
ctio
n
an
d
co
s
t
-
ef
f
ec
tiv
e
n
e
s
s
.
I
n
t
h
is
co
n
tex
t,
th
e
SS
A
,
p
r
ese
n
ted
b
y
Mir
j
alili
et
a
l.
[
3
2
]
in
2
0
2
1
,
is
a
p
r
o
m
is
in
g
o
p
ti
m
iza
tio
n
ap
p
r
o
ac
h
en
co
u
r
ag
ed
b
y
th
e
f
o
r
ag
in
g
b
eh
av
io
r
o
f
s
a
lp
s
i
n
o
ce
an
s
.
SS
A
h
a
s
co
n
f
ir
m
ed
e
f
f
ec
tiv
e
n
ess
in
s
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l
v
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g
d
iv
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p
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s
b
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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l
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tia
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p
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s
s
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T
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th
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y
lev
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t
h
e
W
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A
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o
p
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m
et
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to
s
i
m
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s
l
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i
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th
e
o
p
ti
m
al
p
lace
m
en
t
a
n
d
r
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g
o
f
SC
s
.
T
h
e
p
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o
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ec
ted
ap
p
r
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ac
h
ai
m
s
f
o
r
d
i
m
in
is
h
i
n
g
p
o
w
er
lo
s
s
,
r
ed
u
c
in
g
t
h
e
o
v
er
al
l
co
s
t
s
as
s
o
ciate
d
w
i
th
S
C
s
(
p
u
r
ch
ase,
in
s
talla
tio
n
,
a
n
d
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p
er
atin
g
co
s
ts
)
,
an
d
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ein
f
o
r
cin
g
t
h
e
s
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s
te
m
v
o
lta
g
e
p
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o
f
ile,
th
u
s
m
ax
i
m
izi
n
g
t
h
e
an
n
u
al
co
s
t
s
av
i
n
g
(
AC
S)
s
u
b
j
ec
ted
to
m
ai
n
tai
n
in
g
all
t
h
e
co
n
s
tr
ain
t
s
w
it
h
i
n
its
p
er
m
is
s
ib
le
li
m
its
.
T
h
e
OC
P
is
p
r
esen
ted
a
s
a
m
u
lti
-
o
b
j
ec
tiv
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n
e
co
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s
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co
s
t
w
h
ile
s
at
is
f
y
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n
g
al
l
co
n
s
tr
ain
ts
.
T
h
e
p
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w
er
f
lo
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d
is
cu
s
s
ed
i
n
t
h
is
r
esear
ch
u
s
e
s
t
h
e
b
ac
k
w
ar
d
/
f
o
r
w
ar
d
s
w
ee
p
(
B
FS
)
tech
n
iq
u
e
,
w
h
ic
h
is
m
o
r
e
s
u
itab
le
f
o
r
R
DNs
t
h
an
o
th
er
co
n
v
e
n
tio
n
a
l
l
o
ad
f
lo
w
m
et
h
o
d
s
.
T
h
e
r
esu
lts
g
o
tten
u
tili
zi
n
g
th
e
W
SS
A
tec
h
n
iq
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e
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e
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m
p
ar
e
d
to
th
o
s
e
g
o
tten
u
ti
lizi
n
g
SS
A
a
n
d
o
th
er
co
n
te
m
p
o
r
ar
y
tech
n
iq
u
es p
u
b
lis
h
ed
i
n
th
e
liter
at
u
r
e
i
n
clu
d
i
n
g
n
o
v
el
an
al
y
tic
(
N
A
)
[
3
3
]
,
lo
cu
s
t
s
ea
r
ch
(
L
S)
[
3
4
]
,
g
re
y
w
o
lf
o
p
ti
m
i
za
tio
n
(
GW
O)
[
3
5
]
,
an
d
h
u
n
ter
-
p
r
e
y
o
p
ti
m
iza
tio
n
(
HP
O)
[
3
6
]
.
T
h
e
p
r
im
ar
y
co
n
tr
ib
u
tio
n
s
ca
n
b
e
o
u
tli
n
ed
as f
o
l
lo
w
s
:
i)
C
o
m
p
r
eh
e
n
s
i
v
e
m
u
lt
i
-
o
b
j
ec
tiv
e
ap
p
r
o
ac
h
: T
h
e
p
r
o
p
o
s
ed
w
o
r
k
s
i
m
u
l
tan
eo
u
s
l
y
ac
co
u
n
t
s
f
o
r
tech
n
ical
a
n
d
ec
o
n
o
m
ic
ad
v
a
n
ta
g
es,
i
n
cl
u
d
in
g
p
o
w
er
lo
s
s
r
ed
u
c
tio
n
an
d
co
s
t
-
e
f
f
ec
ti
v
e
n
ess
.
T
h
e
OC
P
is
s
u
e
i
s
co
n
s
id
er
ed
as
a
m
u
lti
-
o
b
j
ec
tiv
e
o
p
ti
m
izatio
n
tas
k
,
co
n
s
id
er
in
g
b
o
th
lo
s
s
m
in
i
m
izat
io
n
an
d
co
s
t
r
ed
u
ctio
n
,
in
te
g
r
ated
in
to
a
s
i
n
g
le
o
b
j
ec
tiv
e
f
u
n
ctio
n
u
s
in
g
a
w
ei
g
h
ti
n
g
f
ac
to
r
.
ii)
Dev
elo
p
m
e
n
t
o
f
W
SS
A
:
T
h
e
r
esear
ch
i
n
tr
o
d
u
ce
s
a
n
en
h
an
ce
d
o
p
ti
m
izatio
n
tech
n
iq
u
e,
W
SS
A
,
b
y
in
co
r
p
o
r
atin
g
an
i
n
er
tia
w
ei
g
h
t
p
ar
a
m
eter
i
n
to
th
e
s
ta
n
d
ar
d
SS
A
.
T
h
is
m
o
d
if
icatio
n
i
m
p
r
o
v
es
th
e
alg
o
r
ith
m
s
p
r
ec
is
io
n
,
co
n
s
is
te
n
c
y
,
an
d
s
p
ee
d
,
a
v
o
id
in
g
th
e
l
i
m
itat
io
n
s
o
f
th
e
b
asic
SS
A
i
n
co
m
p
lex
an
d
m
u
lti
-
m
o
d
al
s
ea
r
c
h
s
p
ac
es.
iii)
No
v
el
ap
p
licatio
n
o
f
W
SS
A
to
OC
P
in
R
DNs:
T
h
is
s
t
u
d
y
ap
p
lies
t
h
e
W
SS
A
f
o
r
tac
k
li
n
g
t
h
e
OC
P
p
r
o
b
lem
in
R
DNs
f
o
r
th
e
f
ir
s
t
ti
m
e.
T
h
e
m
et
h
o
d
d
eter
m
i
n
es
th
e
b
est
p
o
s
s
ib
le
SC
p
l
ac
e
m
en
t
an
d
r
atin
g
a
t
th
e
s
a
m
e
ti
m
e
ad
d
r
ess
in
g
g
ap
s
in
p
r
ev
io
u
s
s
t
u
d
ies t
h
at
r
elied
o
n
L
S
Fs
o
r
s
ep
ar
ated
o
b
j
ec
tiv
es.
iv
)
A
p
p
licatio
n
to
r
ea
l
a
n
d
s
ta
n
d
ar
d
R
DNs:
T
h
e
e
f
f
icie
n
c
y
o
f
th
e
p
r
ese
n
ted
m
eth
o
d
i
s
v
a
li
d
ated
o
n
b
o
th
r
ea
l
-
w
o
r
ld
a
n
d
b
en
c
h
m
ar
k
s
y
s
te
m
s
.
T
esti
n
g
th
e
s
u
g
g
ested
m
eth
o
d
o
lo
g
y
o
n
a
h
ea
v
il
y
lo
ad
e
d
I
r
aq
i
6
5
b
u
s
R
DN
an
d
s
tan
d
ar
d
I
E
E
E
3
3
b
u
s
R
DN.
v)
E
n
h
a
n
ce
d
en
er
g
y
a
n
d
ec
o
n
o
m
ic
p
er
f
o
r
m
a
n
ce
:
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
s
ig
n
i
f
ica
n
tl
y
en
h
a
n
ce
s
th
e
R
DN
’
s
p
er
f
o
r
m
a
n
ce
b
y
r
ed
u
ci
n
g
p
o
w
er
lo
s
s
a
n
d
a
m
elio
r
atin
g
v
o
lta
g
e
p
r
o
f
ile.
I
t
also
ac
h
ie
v
es
s
u
b
s
tan
tial
A
C
S
b
y
o
p
ti
m
izi
n
g
t
h
e
s
i
tin
g
a
n
d
r
atin
g
o
f
S
C
s
,
co
n
s
id
er
in
g
p
u
r
ch
ase,
in
s
ta
llatio
n
,
a
n
d
o
p
er
atin
g
co
s
ts
.
v
i)
Valid
atio
n
th
r
o
u
g
h
co
m
p
ar
ativ
e
an
al
y
s
i
s
:
T
h
e
r
esu
lts
attai
n
ed
u
s
in
g
th
e
W
SS
A
ar
e
b
en
ch
m
ar
k
ed
ag
ai
n
s
t
th
o
s
e
f
r
o
m
th
e
b
asic
SS
A
a
n
d
o
th
er
co
n
te
m
p
o
r
ar
y
o
p
ti
m
izat
i
o
n
tech
n
iq
u
e
s
d
escr
ib
ed
in
th
e
liter
atu
r
e.
v
ii)
P
r
ac
tical
co
n
s
id
er
atio
n
s
f
o
r
r
ea
l
-
w
o
r
ld
i
m
p
le
m
en
ta
tio
n
:
T
h
e
s
tu
d
y
e
x
p
licitl
y
ad
d
r
ess
es
th
e
li
m
itatio
n
s
o
f
p
r
io
r
r
esear
ch
b
y
f
o
cu
s
i
n
g
o
n
r
ea
l,
h
ea
v
i
l
y
lo
ad
ed
R
DN
s
with
s
i
g
n
i
f
ica
n
t
p
o
w
er
lo
s
s
es
a
n
d
o
p
er
atio
n
al
ch
alle
n
g
e
s
.
T
h
is
p
r
ac
tical
o
r
ie
n
tatio
n
g
u
ar
an
tees
t
h
e
ap
p
lica
b
ilit
y
o
f
t
h
e
p
r
o
p
o
s
ed
m
e
th
o
d
to
r
ea
l
-
w
o
r
ld
R
DNs.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
p
p
l P
o
w
er
E
n
g
I
SS
N:
2252
-
8792
A
n
o
ve
l W
S
S
A
tech
n
iq
u
e
fo
r
mu
lti
-
o
b
jective
o
p
tima
l c
a
p
a
cito
r
s
p
la
ce
men
t
…
(
Oma
r
Mu
h
a
mme
d
N
ed
a
)
937
T
h
e
p
ap
er
'
s
r
e
m
i
n
d
er
is
o
r
d
er
ed
as
f
o
llo
w
s
.
Sectio
n
2
d
escr
ib
es
th
e
O
C
P
p
r
o
b
lem
f
o
r
m
u
latio
n
,
i
n
cl
u
d
in
g
co
n
s
tr
ain
ts
,
lo
ad
f
lo
w
co
m
p
u
t
atio
n
u
til
izin
g
t
h
e
B
FS
m
et
h
o
d
,
an
d
o
b
j
ec
tiv
e
f
u
n
ct
io
n
s
.
T
h
e
f
u
n
d
a
m
e
n
tal
id
ea
s
o
f
th
e
SS
A
a
n
d
its
e
n
h
an
ce
m
en
ts
ar
e
d
escr
ib
ed
in
s
ec
tio
n
3
.
R
esu
l
ts
f
r
o
m
s
i
m
u
lat
io
n
s
a
n
d
ca
s
e
s
t
u
d
ies
ar
e
o
f
f
er
ed
in
s
ec
tio
n
4
.
L
a
s
tl
y
,
co
n
clu
s
io
n
s
an
d
f
u
t
u
r
e
w
o
r
k
ar
e
ex
p
lain
ed
i
n
s
ec
tio
n
5
.
2.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
d
escr
ib
es
a
n
d
ex
p
lain
s
t
h
e
lo
ad
f
lo
w
an
al
y
s
i
s
(
L
F
A
)
f
o
r
R
DN
s
,
m
u
lt
i
-
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
,
an
d
R
DNs c
o
n
s
tr
ai
n
ts
i
m
p
le
m
en
ted
in
t
h
is
w
o
r
k
an
d
d
escr
ib
ed
in
th
e
s
ec
tio
n
b
elo
w
.
2
.
1
.
L
o
a
d
f
l
o
w
a
na
ly
s
is
(
L
F
A)
T
h
is
s
ec
tio
n
p
r
esen
t
s
a
f
u
n
d
a
m
en
tal
to
o
l
f
o
r
ev
alu
at
in
g
th
e
s
tead
y
-
s
tate
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
R
DN
s
,
in
cl
u
d
in
g
b
u
s
v
o
lta
g
es,
p
o
w
er
f
lo
w
s
,
an
d
s
y
s
te
m
lo
s
s
es.
I
t
e
n
ab
les
ac
c
u
r
ate
a
s
s
es
s
m
en
t
o
f
n
et
w
o
r
k
o
p
er
atin
g
co
n
d
itio
n
s
,
w
h
ic
h
is
e
s
s
e
n
tial
f
o
r
p
lan
n
i
n
g
,
o
p
ti
m
izat
io
n
,
an
d
r
eliab
ilit
y
i
m
p
r
o
v
e
m
e
n
t.
I
n
t
h
is
s
t
u
d
y
,
th
e
L
F
A
is
p
er
f
o
r
m
ed
u
s
in
g
t
h
e
FB
S
m
eth
o
d
,
ch
o
s
e
n
f
o
r
its
s
u
itab
ilit
y
i
n
h
an
d
li
n
g
t
h
e
u
n
iq
u
e
c
h
ar
a
cter
is
tics
o
f
R
DN
s
,
as d
etailed
in
th
e
f
o
llo
w
i
n
g
s
u
b
s
ec
tio
n
.
2
.
1
.
1
.
B
F
S ba
s
ed
L
F
A
m
et
ho
d
B
ec
au
s
e
o
f
it
s
f
le
x
ib
ili
t
y
a
n
d
p
r
ec
is
io
n
,
B
FS
m
eth
o
d
is
w
id
el
y
u
s
ed
f
o
r
attain
in
g
t
h
e
L
F
A
c
alcu
latio
n
in
R
DN.
I
t
h
a
s
a
q
u
ick
co
n
v
er
g
en
ce
p
r
o
p
er
ty
a
n
d
is
co
m
p
u
t
atio
n
all
y
m
o
r
e
ef
f
icie
n
t.
W
h
il
e
Ne
w
to
n
-
R
ap
h
s
o
n
,
f
ast
d
ec
o
u
p
led
,
Gau
s
s
-
Seid
el
,
an
d
o
th
er
tr
ad
itio
n
al
L
F
A
tech
n
iq
u
es
ar
e
w
ell
ad
ap
ted
f
o
r
tr
an
s
m
is
s
io
n
s
y
s
te
m
s
,
th
e
y
ar
e
n
o
t
o
f
ten
u
s
ed
in
d
is
tr
ib
u
tio
n
n
e
t
w
o
r
k
s
d
u
e
to
its
lo
w
er
ef
f
icien
c
y
,
h
i
g
h
r
esis
ta
n
ce
/r
ea
ctan
c
e
(
R
/X)
r
atio
s
,
r
ad
ial
co
n
f
ig
u
r
ati
o
n
[
3
7
]
,
an
d
o
th
er
f
ac
to
r
s
.
T
h
e
B
FS
alg
o
r
ith
m
co
n
s
is
t
s
o
f
th
r
ee
s
i
m
p
le
iter
ati
v
e
s
tep
s
.
T
h
ese
s
tep
s
ar
e
d
escr
ib
ed
in
d
etail
in
[
3
8
]
.
2
.
1
.
2
.
L
F
A
ca
lcula
t
io
n
Fo
r
th
is
s
t
u
d
y
,
th
e
lo
ad
f
lo
w
ca
lcu
latio
n
s
ar
e
d
er
iv
ed
f
r
o
m
th
e
s
in
g
le
li
n
e
d
iag
r
a
m
(
S
L
D)
o
f
R
DN
d
is
p
la
y
i
n
Fi
g
u
r
e
1
.
T
h
e
SLD
s
er
v
es
th
e
s
tr
u
c
tu
r
al
r
ep
r
ese
n
t
atio
n
o
f
th
e
n
et
w
o
r
k
,
ill
u
s
tr
ati
n
g
th
e
ar
r
an
g
e
m
e
n
t
o
f
b
u
s
es,
b
r
an
ch
e
s
,
an
d
co
n
n
e
cted
lo
ad
s
.
Usi
n
g
th
is
s
c
h
e
m
a
tic
as
th
e
r
ef
er
en
ce
m
o
d
el,
t
h
e
B
FS
is
ap
p
lied
t
o
iter
ativ
el
y
co
m
p
u
te
b
u
s
v
o
ltag
e,
b
r
an
ch
,
cu
r
r
en
ts
,
a
n
d
p
o
w
er
f
lo
w
s
u
n
d
er
th
e
s
p
ec
if
ied
lo
a
d
in
g
co
n
d
itio
n
s
.
Fig
u
r
e
1
.
SL
D
o
f
t
h
e
R
DN
Fro
m
Fig
u
r
e
1
,
if
an
d
d
en
o
te
th
e
r
ec
eiv
in
g
en
d
n
o
d
es,
th
en
(
1
)
an
d
(
2
)
ca
n
b
e
u
tili
ze
d
f
o
r
co
m
p
u
t
in
g
th
e
ac
tiv
e
(
)
an
d
r
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ctiv
e
(
)
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o
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er
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o
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o
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=
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T
h
e
v
o
ltag
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o
f
li
n
e
at
b
u
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n
b
e
ca
lcu
lated
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o
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.
|
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+
2
,
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|
|
2
)
(
3
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8792
I
n
t J
A
p
p
l P
o
w
er
E
n
g
,
Vo
l.
14
,
No
.
4
,
Dec
em
b
er
20
25
:
9
3
4
-
950
938
I
n
(
4
)
an
d
(
5
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ar
e
p
r
esen
ted
to
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m
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u
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e
ac
tu
al
p
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er
lo
s
s
(
)
o
f
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e
b
r
an
ch
"
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=
2
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4
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(
,
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=
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(
5
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In
(
6
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an
d
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7
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ar
e
u
s
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eter
m
i
n
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i
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ed
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d
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g
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h
e
lo
s
s
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f
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m
p
letel
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l
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e
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h
o
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n
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d
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SC
s
i
n
cr
ea
s
e
p
o
w
er
ef
f
icie
n
c
y
an
d
r
ed
u
ce
t
h
e
o
v
er
all
co
s
ts
ass
o
ciate
d
w
ith
SC
s
an
d
p
o
w
er
lo
s
s
o
f
th
e
R
D
N
b
y
i
n
j
ec
tin
g
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ea
cti
v
e
p
o
w
er
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,
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.
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h
e
R
DN
in
Fig
u
r
e
1
b
ec
o
m
es
th
e
R
DN
in
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g
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r
e
2
af
ter
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lacin
g
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is
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n
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o
r
th
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c
o
m
p
e
n
s
ated
R
DN
to
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1
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u
r
e
2
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D
o
f
t
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m
p
e
n
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ated
s
i
m
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le
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D
N
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it
h
in
s
talle
d
SC
2
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2
.
O
bje
c
t
iv
e
f
un
ct
io
ns
Fig
u
r
e
3
d
is
p
lay
s
th
e
ad
v
a
n
t
ag
e
o
f
in
s
talli
n
g
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s
,
as
co
n
s
id
er
ed
in
th
is
w
o
r
k
.
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h
e
ch
i
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g
o
al
o
f
o
b
j
ec
tiv
e
f
u
n
ct
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n
i
n
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e
O
C
P
is
s
u
e
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to
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e
n
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er
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s
s
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d
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ed
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ce
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e
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ac
ito
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o
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er
atio
n
,
in
s
tallatio
n
,
an
d
p
u
r
ch
a
s
e
co
s
t
s
.
As
a
r
e
s
u
lt,
th
e
o
v
er
all
co
s
t
p
er
y
ea
r
is
r
ed
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ce
d
,
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d
t
h
u
s
i
n
cr
ea
s
i
n
g
AC
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o
f
t
h
e
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DN
s
.
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h
e
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r
o
b
lem
o
f
O
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P
is
v
ie
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e
d
as
a
co
llectio
n
o
f
m
u
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n
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n
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o
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s
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n
d
co
s
t,
w
h
ic
h
ar
e
d
ef
i
n
ed
b
elo
w
.
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h
o
u
g
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v
o
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e
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r
o
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ile
i
m
p
r
o
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e
m
e
n
t
is
n
o
t e
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p
lici
tl
y
in
cl
u
d
ed
in
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
;
i
n
s
tead
,
it
is
m
ai
n
ta
in
ed
t
h
r
o
u
g
h
s
y
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te
m
co
n
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tr
ai
n
ts
.
R
ed
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c
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o
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I
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
p
p
l P
o
w
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E
n
g
I
SS
N:
2252
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8792
A
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l W
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S
A
tech
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iq
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r
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o
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p
tima
l c
a
p
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cito
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s
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-
C
ap
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T
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ated
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e
p
o
w
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to
th
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R
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v
ia
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s
is
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a
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h
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co
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.
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.
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AC
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m
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s
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h
e
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n
b
e
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m
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g
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t
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k
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ed
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s
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s
ta
llatio
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,
o
p
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d
p
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r
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ase
co
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ts
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T
h
e
d
if
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er
e
n
ce
am
o
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g
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o
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th
e
to
tal
A
E
L
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o
f
th
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ase
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s
e
(
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b
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d
t
h
e
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co
s
t
a
f
ter
co
m
p
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n
s
a
tio
n
(
)
is
u
s
ed
to
ca
lcu
late
t
h
e
AC
S
(
)
ac
h
iev
ed
f
r
o
m
o
b
j
ec
tiv
e
f
u
n
cti
o
n
s
m
in
i
m
iza
tio
n
,
as s
h
o
w
n
in
th
e
g
i
v
e
n
eq
u
atio
n
s
:
=
×
×
(
2
6
)
=
+
(
2
7
)
=
−
(
2
8
)
w
h
er
e,
an
d
ar
e
d
escr
ib
ed
in
(
1
4
)
an
d
(
1
5
)
in
s
ec
tio
n
(
2
.
2
.
2
)
.
2
.
6
.
O
pti
m
iza
t
io
n
p
ro
ce
s
s
2
.
6
.
1
.
Co
nv
ent
io
na
l SSA
T
h
e
co
n
v
en
t
io
n
al
S
S
A
al
g
o
r
ith
m
w
as
p
r
o
p
o
s
ed
in
2
0
1
7
[
4
2
]
.
S
w
ar
m
s
a
lp
s
ca
n
b
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s
ca
v
e
n
g
ed
in
s
ea
s
,
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d
SS
A
s
i
m
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lates
t
h
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p
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s
s
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n
th
e
d
ee
p
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ce
an
s
,
th
e
s
al
p
at
th
e
h
ea
d
o
f
th
e
ch
ain
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il
l
b
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m
e
t
h
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w
it
h
t
h
e
r
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m
ai
n
i
n
g
s
a
lp
s
s
er
v
in
g
as
f
o
llo
w
er
s
.
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ec
au
s
e
o
f
th
i
s
s
p
ec
ial
b
eh
a
v
io
r
,
th
e
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g
o
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ith
m
h
a
s
a
h
i
g
h
d
eg
r
ee
o
f
ex
p
lo
itatio
n
p
o
ten
ti
al
in
t
h
e
lo
ca
l
r
an
g
e.
Ho
w
e
v
e
r
,
th
e
o
r
ig
in
a
l
SS
A
,
te
n
d
s
to
co
n
v
er
g
e
to
a
lo
ca
l
o
p
tim
u
m
w
h
e
n
t
h
e
p
o
p
u
latio
n
lead
er
is
u
n
ab
le
to
tr
av
el
t
o
th
e
p
r
o
m
is
i
n
g
ar
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s
.
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a
r
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lt,
w
e
s
u
g
g
e
s
t
a
m
et
h
o
d
in
th
i
s
p
ap
er
to
en
h
an
ce
th
e
ef
f
icie
n
c
y
o
f
th
e
S
S
A
.
T
h
e
lead
er
h
as
a
s
ig
n
i
f
ica
n
t
i
m
p
ac
t
o
n
th
e
e
n
tire
s
o
ciet
y
d
u
r
i
n
g
t
h
e
lead
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s
ta
g
e
[
4
3
]
.
T
h
e
q
u
est
is
d
ir
ec
ted
b
y
th
e
c
h
ie
f
,
w
h
o
k
ee
p
s
it
g
o
in
g
clo
s
er
to
th
e
f
o
o
d
.
In
(
2
9
)
in
d
icate
s
th
e
f
o
r
m
u
la
f
o
r
u
p
d
ati
n
g
lo
ca
tio
n
.
1
=
{
+
1
(
(
−
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2
+
)
,
3
≥
0
−
1
(
(
−
)
2
+
)
,
3
<
0
(
2
9
)
W
h
er
e,
1
an
d
r
ep
r
esen
t
th
e
lea
d
er
an
d
s
o
u
r
ce
o
f
f
o
o
d
lo
ca
tio
n
s
.
an
d
d
is
p
la
y
t
h
e
u
p
p
er
an
d
lo
w
e
r
b
o
u
n
d
ar
y
.
2
an
d
3
s
h
o
w
th
e
r
a
n
d
o
m
n
u
m
b
er
s
b
et
w
ee
n
[
0
,
1
]
co
n
tr
o
l
p
ar
a
m
eter
s
.
T
h
e
f
ac
to
r
1
is
i
m
p
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r
ta
n
t
in
o
r
g
an
izin
g
e
x
p
lo
r
atio
n
an
d
ex
tr
ac
tio
n
,
as it d
ec
r
ea
s
e
s
in
t
h
e
p
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io
d
[
2
~0
]
.
I
ts
m
ea
n
i
n
g
i
s
as
(
3
0
)
.
1
=
2
∗
−
(
4
)
2
(
3
0
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W
h
er
e,
an
d
d
en
o
te
cu
r
r
en
t
a
n
d
m
a
x
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m
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m
iter
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io
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s
n
u
m
b
er
.
T
h
e
f
o
llo
w
in
g
ter
m
is
u
til
i
ze
d
to
s
h
if
t
th
e
f
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llo
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ca
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=
1
2
(
+
−
1
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(
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h
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e,
d
en
o
tes th
e
f
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w
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p
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d
≥
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2
.
6
.
2
.
W
SSA
T
r
a
d
itio
n
al
SS
A
is
p
r
o
n
e
to
lo
ca
l
m
i
n
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m
a
s
ta
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atio
n
a
n
d
p
o
o
r
s
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r
ch
in
g
ac
cu
r
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y
.
Fo
r
o
v
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co
m
i
n
g
th
is
p
r
o
b
le
m
a
n
d
i
m
p
r
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v
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n
g
s
ea
r
ch
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g
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p
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t
h
r
o
u
g
h
b
o
th
e
x
p
lo
r
atio
n
an
d
e
x
p
lo
itati
o
n
,
an
in
er
tia
w
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g
h
t
m
ec
h
a
n
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s
m
€
[
0
,
1
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i
n
tr
o
d
u
ce
d
to
th
e
SS
A
,
a
n
d
th
e
p
r
ese
n
ted
al
g
o
r
ith
m
is
k
n
o
w
n
a
s
W
SS
A
[
4
4
]
.
T
h
is
n
e
w
f
ac
to
r
in
th
e
W
SS
A
h
as
th
e
b
en
ef
its
o
f
g
et
tin
g
a
b
etter
s
ea
r
ch
in
g
s
tr
ateg
y
,
ac
h
ie
v
i
n
g
p
r
ec
is
e
s
o
lu
tio
n
s
,
av
o
id
in
g
b
lin
d
n
e
s
s
o
f
t
h
e
s
ea
r
ch
m
et
h
o
d
,
an
d
ac
ce
ler
atin
g
s
p
ee
d
co
n
v
er
g
en
ce
.
A
ls
o
,
wh
en
d
ea
li
n
g
w
it
h
a
lar
g
e
n
u
m
b
er
o
f
lar
g
e
-
s
ca
le
p
r
o
b
lem
s
,
it
m
a
in
ta
in
s
a
g
o
o
d
b
alan
ce
a
m
o
n
g
e
x
p
lo
r
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n
a
n
d
ex
p
lo
itatio
n
w
h
ile
p
r
eser
v
in
g
lo
w
co
m
p
u
tatio
n
a
l
d
if
f
ic
u
lt
y
.
So
,
t
h
e
n
e
w
lead
er
'
s
p
o
s
itio
n
a
n
d
t
h
e
n
e
w
f
o
llo
we
r
s
'
p
o
s
itio
n
i
n
t
h
e
I
SS
A
ca
n
b
e
ch
a
n
g
ed
as
s
h
o
wn
in
(
3
2
)
an
d
(
3
3
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8792
I
n
t J
A
p
p
l P
o
w
er
E
n
g
,
Vo
l.
14
,
No
.
4
,
Dec
em
b
er
20
25
:
9
3
4
-
950
942
1
=
{
×
+
1
(
(
−
)
2
+
)
,
3
≥
0
×
−
1
(
(
−
)
2
+
)
,
3
<
0
(
3
2
)
=
1
2
(
+
×
−
1
)
(
3
3
)
2
.
7
.
T
he
i
m
p
le
m
ent
a
t
io
n o
f
WSS
A
t
o
O
CP
p
ro
ble
m
W
SS
A
co
n
s
id
er
s
t
w
o
O
C
P
p
r
o
b
lem
p
ar
a
m
eter
s
,
s
u
c
h
a
s
ca
p
ac
ito
r
p
o
s
itio
n
an
d
r
ati
n
g
,
to
b
e
co
n
tr
o
l
v
ar
iab
les.
A
s
a
r
esu
lt,
an
d
s
ea
r
ch
ag
e
n
t
'
s
n
u
m
b
er
o
f
v
ar
i
ab
les
is
ca
lcu
lated
b
y
t
h
e
n
u
m
b
er
o
f
SC
s
.
T
w
o
v
ar
iab
les
ar
e
co
n
s
id
er
ed
f
o
r
ea
ch
SC
:
th
e
f
ir
s
t
v
ar
iab
le
r
ep
r
esen
ts
t
h
e
p
o
s
itio
n
,
a
n
d
th
e
s
ec
o
n
d
v
ar
iab
l
e
r
ep
r
esen
ts
th
e
ca
p
ac
it
y
.
E
ac
h
s
ea
r
ch
ag
e
n
t
in
W
SS
A
,
f
o
r
ex
a
m
p
le,
is
m
ad
e
u
p
o
f
t
w
o
v
ar
i
ab
les
th
at
ar
e
s
p
lit
in
to
t
w
o
s
ec
t
io
n
s
f
o
r
ea
ch
SC
.
O
n
e
is
f
o
r
t
h
e
v
en
u
e,
w
h
ile
t
h
e
o
t
h
er
is
f
o
r
S
C
'
s
s
ca
le.
A
s
o
l
u
tio
n
co
r
r
esp
o
n
d
in
g
to
a
s
alp
is
r
ep
r
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ted
b
y
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ese
v
ar
iab
les.
T
h
e
s
o
lu
tio
n
v
ec
to
r
co
m
p
r
is
in
g
p
o
s
itio
n
an
d
r
atin
g
o
f
ca
p
ac
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r
ar
e
ex
p
r
ess
ed
i
n
(
3
4
)
.
Fig
u
r
e
4
d
ep
icts
t
h
e
p
r
o
ce
d
u
r
e
f
o
r
s
o
lv
in
g
t
h
e
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SS
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f
o
r
t
h
e
O
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p
r
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b
lem
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a
n
d
th
e
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SS
A
f
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w
c
h
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t
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s
s
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w
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i
n
t
h
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s
f
i
g
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r
e,
ill
u
s
tr
ati
n
g
t
h
e
s
tep
-
by
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s
tep
p
r
o
ce
s
s
o
f
t
h
e
alg
o
r
ith
m
.
=
[
,
̅
̅
̅
̅
̅
̅
̅
̅
̅
̅
]
(
3
4
)
W
h
er
e,
is
th
e
co
n
tr
o
l
v
ar
iab
le
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an
d
ar
e
th
e
lo
ca
tio
n
an
d
s
ize
o
f
ca
p
ac
ito
r
.
T
h
e
tr
ial
-
an
d
-
er
r
o
r
tech
n
iq
u
e
w
as
u
s
ed
f
o
r
in
itia
l
izin
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n
tr
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l
p
ar
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e
t
er
s
.
T
h
e
d
etails
o
f
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ar
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m
eter
s
b
ased
t
w
o
R
DNs a
r
e
m
en
t
io
n
ed
in
T
ab
le
3
.
T
ab
le
3
.
W
SS
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p
ar
am
e
ter
s
P
a
r
a
me
t
e
r
s
I
EEE
3
3
b
u
s
I
r
a
q
i
6
5
b
u
s
P
o
p
u
l
a
t
i
o
n
s
i
z
e
(
)
10
10
I
t
e
r
a
t
i
o
n
s
(
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