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i
n
g
(
L
P
)
w
i
th
a
F
AC
T
S d
ev
ice
f
o
r
s
o
l
v
i
n
g
Op
ti
m
al
r
ea
ctiv
e
p
o
w
er
d
is
p
atch
p
r
o
b
le
m
th
at
i
s
lig
h
ten
in
g
o
v
er
lo
ad
s
i
n
tr
an
s
m
is
s
io
n
lin
e
a
n
d
v
o
lta
g
e
v
io
lati
o
n
s
d
u
e
to
co
n
ti
n
g
en
c
y
.
T
h
e
d
ev
elo
p
ed
alg
o
r
ith
m
ap
p
lied
to
th
e
Ne
w
E
n
g
la
n
d
3
9
-
b
u
s
s
y
s
te
m
a
n
d
th
e
W
E
C
C
1
7
9
-
b
u
s
s
y
s
te
m
[
11
].
T
h
is
p
ap
er
p
r
esen
ts
a
n
e
w
a
p
p
r
o
ac
h
f
o
r
in
s
tallatio
n
o
f
F
AC
T
S
b
ased
SVC
o
n
M
u
lti
-
Ob
j
ec
tiv
e
E
v
o
lu
tio
n
ar
y
P
r
o
g
r
a
m
m
in
g
(
MO
E
P
)
o
p
ti
m
izatio
n
tec
h
n
iq
u
e
co
n
s
id
er
in
g
m
u
lt
i
-
c
o
n
tin
g
e
n
cies
(N
-
m)
o
cc
u
r
r
en
ce
in
th
e
s
y
s
te
m
.
T
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
d
e
ter
m
i
n
es
t
h
e
o
p
ti
m
u
m
s
izin
g
o
f
Static
V
AR
C
o
m
p
en
s
ato
r
(
SV
C
)
i
n
o
r
d
er
to
r
ed
u
ce
t
h
e
to
tal
tr
a
n
s
m
is
s
io
n
lo
s
s
in
t
h
e
s
y
s
te
m
.
Stat
i
c
Vo
ltag
e
Stab
ilit
y
I
n
d
ex
(
S
V
S
I
)
is
u
s
ed
as
t
h
e
to
o
l
to
in
d
icate
th
e
SV
C
’
s
lo
ca
ti
o
n
to
b
e
in
s
talled
in
to
t
h
e
p
o
w
er
s
y
s
te
m
n
et
w
o
r
k
.
T
h
e
S
V
S
I
an
d
t
r
an
s
m
i
s
s
io
n
lo
s
s
m
i
n
i
m
izatio
n
w
a
s
u
s
ed
as
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
i
n
th
e
s
y
s
te
m
.
A
co
m
p
u
ter
p
r
o
g
r
am
w
a
s
w
r
it
ten
i
n
MA
T
L
A
B
an
d
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
s
w
er
e
test
ed
o
n
th
e
I
E
E
E
3
0
-
b
u
s
R
T
S.
I
n
ad
d
itio
n
,
c
o
m
p
ar
ativ
e
s
tu
d
ies
ar
e
co
n
d
u
cted
b
y
co
m
p
ar
i
n
g
t
h
e
r
es
u
lts
w
it
h
Mu
lti
-
Ob
j
ec
tiv
e
A
r
ti
f
ici
a
l I
m
m
u
n
e
S
y
s
te
m
(
MO
A
I
S).
An
alg
o
r
ith
m
to
ap
p
ly
s
u
c
h
m
u
lti
-
o
b
j
ec
tiv
e
o
p
ti
m
izatio
n
h
as
b
ee
n
f
o
r
m
u
lated
b
ased
o
n
th
e
s
a
m
e
n
o
n
-
d
o
m
i
n
ated
s
o
r
tin
g
co
n
ce
p
t
i
m
p
le
m
e
n
ted
i
n
n
o
n
-
d
o
m
i
n
ated
s
o
r
tin
g
g
e
n
etic
al
g
o
r
ith
m
(
N
SG
A
-
I
I
)
.
I
n
ad
d
itio
n
,
a
p
r
o
g
r
am
is
al
s
o
d
ev
elo
p
ed
to
o
b
tain
b
est co
m
p
r
o
m
is
e
s
o
lu
tio
n
in
a
p
o
w
er
s
y
s
te
m
.
2.
M
UL
T
I
O
B
J
E
CT
I
V
E
O
P
T
I
M
I
Z
AT
I
O
N
Mu
lti
-
o
b
j
ec
tiv
e
o
p
ti
m
izatio
n
is
a
p
r
o
ce
s
s
to
f
in
d
th
e
v
al
u
e
o
f
th
e
v
ar
iab
les
t
h
at
m
i
n
i
m
ize
t
h
e
o
b
j
ec
tiv
e
f
u
n
c
tio
n
n
a
m
el
y
S
V
S
I
an
d
tr
an
s
m
i
s
s
io
n
lo
s
s
w
h
ile
th
e
s
y
s
te
m
is
o
p
er
atin
g
w
it
h
i
n
its
co
n
s
tr
ai
n
t li
m
it
.
Mu
lti
-
o
b
j
ec
tiv
e
p
r
o
b
lem
s
ar
e
m
o
r
e
d
i
f
f
ic
u
lt
to
s
o
l
v
e
co
m
p
ar
ed
to
th
e
s
in
g
le
o
b
j
ec
tiv
e
s
in
c
e
th
er
e
is
n
o
u
n
iq
u
e
s
o
lu
tio
n
.
I
n
s
tead
o
f
o
n
e
o
p
ti
m
al
r
eso
lu
tio
n
,
t
h
e
i
m
p
le
m
e
n
t
atio
n
o
f
m
u
lt
i
-
o
b
j
ec
tiv
e
ca
n
g
iv
e
a
s
et
o
f
o
p
ti
m
al
s
o
lu
tio
n
s
.
T
h
ese
o
p
ti
m
al
s
o
l
u
tio
n
s
ar
e
k
n
o
w
n
a
s
P
ar
eto
-
o
p
tim
a
l
s
o
lu
t
io
n
s
.
T
h
e
s
et
o
f
all
f
ea
s
ib
le
n
o
n
-
d
o
m
i
n
ated
s
o
l
u
tio
n
is
r
e
f
er
r
ed
to
as
th
e
P
ar
eto
o
p
tim
a
l
s
et,
an
d
f
o
r
a
g
i
v
en
P
ar
et
o
o
p
tim
al
s
et,
t
h
e
co
r
r
esp
o
n
d
in
g
o
b
j
ec
tiv
e
f
u
n
c
t
io
n
v
al
u
es
in
t
h
e
o
b
j
ec
tiv
e
s
p
ac
e
is
ca
lled
th
e
P
ar
eto
f
r
o
n
t.
T
h
e
m
u
lti
o
b
j
ec
tiv
e
o
p
tim
izatio
n
p
r
o
b
le
m
is
s
p
ec
if
ied
as f
o
llo
w
s
[
1
2
]
:
Min
/
m
ax
i
m
iza
tio
n
:
F
(
x)
= [
f
1
(
x)
,
f
2
(
x
)
,
f
3
(
x)
.
.
.
.
.
.
.
f
k
(
x)
]
Su
b
j
ec
ted
to
:
g
i
(
x)
≤
0
w
h
er
e
i=
1
,
2
,
3
.
.
.
.
.
i
:
h
j
(
x)
=
0
w
h
er
e
k
=
1
,
2
,
3
.
.
.
.
.
.
j
(
1
)
W
h
er
e
F
(
x
)
i
s
o
b
j
ec
tiv
e
,
f
1
(
x
)
,
f
2
(
x
)
,
f
3
(
x)
.......
f
k
(
x
)
ar
e
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
,
x
is
t
h
e
v
ec
to
r
o
f
d
ep
en
d
en
t v
ar
iab
le,
g
is
th
e
eq
u
alit
y
co
n
s
tr
ain
t
s
an
d
h
i
s
th
e
i
n
eq
u
alit
y
co
n
s
tr
ain
t.
T
h
e
FA
C
T
S
d
ev
ice
in
s
talled
o
n
th
e
w
ea
k
b
u
s
es
a
n
d
h
ea
v
i
l
y
lo
ad
ed
ar
ea
s
in
o
r
d
e
r
to
r
ed
u
ce
th
e
s
tr
ess
co
n
d
itio
n
i
n
t
h
e
s
y
s
te
m
.
T
h
e
lo
ca
tio
n
s
o
f
SV
C
s
d
ev
ices
i
n
d
icate
d
u
s
i
n
g
t
h
e
S
V
S
I
tech
n
iq
u
e
t
h
at
o
p
er
ates
at
s
a
m
e
o
p
er
atin
g
co
n
d
itio
n
s
i
n
th
e
p
o
w
er
s
y
s
te
m
n
et
w
o
r
k
.
W
h
e
n
t
h
e
lo
ad
f
lo
w
p
r
o
g
r
am
w
as
r
u
n
,
s
tab
ilit
y
in
d
ices
ar
e
ca
lc
u
lated
an
d
th
e
s
y
s
te
m
id
e
n
ti
f
ied
th
e
lin
w
it
h
th
e
h
i
g
h
e
s
t
S
V
S
I
f
o
th
e
in
s
tallat
io
n
o
f
F
A
C
T
S
d
ev
ice.
Fi
n
all
y
,
MO
E
P
tech
n
iq
u
e
w
a
s
u
s
ed
to
id
en
tify
th
e
o
p
ti
m
al
s
ize
o
f
t
h
e
SV
C
.
T
h
e
p
r
o
ce
s
s
o
f
in
s
ta
llatio
n
co
n
s
id
er
ed
th
e
o
cc
u
r
r
en
ce
o
f
g
en
er
ato
r
o
u
ta
g
es.
2
.
1
Sta
t
ic
Vo
lt
a
g
e
Sta
bil
it
y
I
nd
ex
(
SV
SI
)
S
V
S
I
w
h
ic
h
is
a
li
n
e
-
b
ased
v
o
ltag
e
s
tab
ilit
y
i
n
d
ex
w
a
s
d
ev
elo
p
ed
b
y
[
13
]
.
T
h
is
in
d
e
x
u
s
ed
in
th
e
v
o
ltag
e
s
tab
ilit
y
a
n
al
y
s
is
as
a
n
i
n
d
icato
r
o
f
th
e
v
o
lta
g
e
s
ta
b
ilit
y
co
n
d
it
io
n
o
f
a
s
y
s
te
m
.
T
h
e
v
o
ltag
e
s
tab
ilit
y
co
n
d
itio
n
o
f
al
l
li
n
es
i
n
p
o
w
er
s
y
s
te
m
co
u
ld
b
e
ass
e
s
s
ed
u
s
i
n
g
th
i
s
i
n
d
ex
,
w
h
ic
h
co
u
ld
p
r
ed
ict
th
e
o
cc
u
r
r
en
c
e
o
f
v
o
lta
g
e
co
llap
s
e
i
n
a
s
y
s
te
m
.
S
V
S
I
w
as
f
o
r
m
u
lated
b
y
d
e
r
iv
in
g
t
h
e
v
o
ltag
e
q
u
ad
r
atic
eq
u
atio
n
f
o
r
a
g
e
n
er
al
t
w
o
-
b
u
s
s
y
s
te
m
at
t
h
e
r
ec
eiv
i
n
g
en
d
.
S
V
S
I
ji
ca
n
b
e
d
ef
in
ed
as
s
h
o
w
n
i
n
E
q
u
at
io
n
(
2
)
f
o
r
th
e
t
w
o
-
b
u
s
s
y
s
te
m
.
ji
ji
ji
ji
i
ji
ji
ji
ji
ji
P
R
Q
X
V
Q
P
R
X
S
V
S
I
2
2
2
2
2
2
2
2
(
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
694
I
n
t J
P
o
w
E
lec
&
Dr
i
S
y
s
t
,
Vo
l.
9
,
No
.
2
,
J
u
n
e
2
0
1
8
:
880
–
888
882
W
h
er
e
t
h
e
ac
ti
v
e
p
o
w
er
an
d
r
ea
ctiv
e
p
o
w
er
ar
e
P
ji
an
d
Q
ji
.
T
h
e
lin
e
r
esis
ta
n
ce
a
n
d
r
ea
ctan
ce
ar
e
R
ji
an
d
X
ji
.
T
h
e
v
o
ltag
e
m
ag
n
it
u
d
e
an
d
a
n
g
le
ar
e
|
V
|
a
n
d
δ.
T
h
e
s
u
b
s
cr
ip
t
i
a
n
d
j
d
e
n
o
te
v
ar
iab
les
as
s
o
ciate
d
w
it
h
b
u
s
i
an
d
b
u
s
j
.
S
V
S
I
in
d
icate
d
th
e
s
tead
y
s
tate
v
o
lta
g
e
s
tab
ilit
y
o
f
th
e
l
in
e.
I
f
th
e
S
V
S
I
is
less
t
h
an
o
n
e,
th
er
e
ar
e
s
o
lu
tio
n
s
a
n
d
th
e
s
y
s
te
m
is
s
tab
l
e.
I
f
th
e
S
V
S
I
is
lar
g
er
th
an
o
n
e,
th
er
e
is
n
o
s
o
lu
ti
o
n
an
d
th
e
s
y
s
te
m
b
ec
o
m
e
s
u
n
s
tab
le
o
r
s
tead
y
s
t
ate
v
o
ltag
e
co
llap
s
e
o
cc
u
r
s
i
n
th
e
s
y
s
te
m
.
2
.
2
M
ini
m
iza
t
io
n o
f
t
ra
ns
m
i
s
s
io
n lo
s
s
a
s
o
bje
ct
iv
e
f
un
ct
io
n
An
o
th
er
o
b
j
ec
tiv
e
f
u
n
ctio
n
co
n
s
id
er
ed
in
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
is
m
in
i
m
izin
g
t
h
e
tr
an
s
m
is
s
io
n
p
o
w
er
lo
s
s
es
i
n
t
h
e
tr
an
s
m
is
s
io
n
n
et
w
o
r
k
,
w
h
ile
s
atis
f
y
i
n
g
a
s
et
o
f
p
h
y
s
ical
a
n
d
o
p
er
atio
n
,
s
u
b
j
ec
ted
t
o
a
s
et
o
f
eq
u
alit
y
a
n
d
in
eq
u
alit
y
co
n
s
tr
ain
ts
i
n
t
h
e
p
o
w
er
s
y
s
te
m
[
14
]
.
T
h
e
m
at
h
e
m
atica
l e
q
u
a
tio
n
o
f
tr
an
s
m
is
s
io
n
lo
s
s
ca
n
b
e
w
r
itte
n
as
j
i
k
N
k
ij
j
i
j
i
k
N
k
k
p
E
E
L
o
ss
V
V
V
V
g
V
P
f
,
2
2
c
o
s
2
,
m
i
n
Su
b
j
ec
t to
:
-
PQ
N
j
ij
ij
ij
ij
j
i
Di
Gi
Qi
N
i
B
G
V
V
Q
Q
h
i
,
0
c
o
s
s
i
n
(
3
)
Su
b
j
ec
t to
th
e
co
n
s
tr
ai
n
t
o
f
eq
u
alit
y
in
r
ea
cti
v
e
an
d
ac
ti
v
e
p
o
w
er
b
alan
ce
,
0
c
o
s
s
i
n
,
0
PQ
N
j
ij
ij
ij
ij
j
i
Di
Gi
i
Di
Gi
i
N
i
B
G
V
V
Q
Q
Q
Q
Q
Q
i
,
0
s
i
n
c
o
s
,
0
1
B
N
j
ij
ij
ij
ij
j
i
Di
Gi
i
Di
Gi
i
N
i
B
G
V
V
P
P
P
P
P
P
i
(
4
)
Hen
ce
,
in
eq
u
alit
y
co
n
s
tr
ain
t
s
o
n
co
n
tr
o
l
v
ar
iab
le
li
m
its
;
g
e
n
er
ato
r
p
o
w
er
r
ea
cti
v
e
ca
p
ab
ilit
y
li
m
i
ts
,
g
en
er
ato
r
p
o
w
er
ac
ti
v
e
ca
p
ab
ilit
y
li
m
it
s
,
an
d
v
o
lta
g
e
co
n
s
tr
ain
ts
ar
e
g
iv
e
n
b
y
;
B
i
i
i
Gi
Gi
Gi
c
ci
ci
ci
G
Gi
Gi
Gi
N
i
V
V
V
S
l
a
c
k
b
u
s
i
P
P
P
N
i
Q
Q
Q
N
i
Q
Q
Q
,
m
a
x
m
i
n
m
a
x
m
i
n
m
a
x
m
i
n
m
a
x
m
i
n
(
5
)
w
h
er
e,
g
k
is
t
h
e
co
n
d
u
cta
n
ce
o
f
b
r
an
ch
k
,
n
s
i
s
t
h
e
s
lac
k
(
r
ef
er
en
ce
)
b
u
s
n
u
m
b
er
;
N
PQ
is
PQ
b
u
s
n
u
m
b
er
,
N
PV
i
s
PV
b
u
s
n
u
m
b
e
r
,
N
B
i
s
t
h
e
to
tal
n
u
m
b
er
o
f
b
u
s
es,
N
B
-
1
is
th
e
to
tal
b
u
s
e
s
e
x
cl
u
d
in
g
s
lac
k
b
u
s
,
N
c
is
th
e
p
o
s
s
ib
le
r
ea
ctiv
e
p
o
w
er
s
o
u
r
ce
in
s
tallat
io
n
b
u
s
es
n
u
m
b
er
,
N
E
is
th
e
b
r
an
c
h
n
u
m
b
er
,
N
i
is
t
h
e
n
u
m
b
er
s
o
f
b
u
s
es
ad
j
ac
en
t
to
b
u
s
i
in
clu
d
in
g
b
u
s
i
,
θ
i
j
is
v
o
lta
g
e
an
g
le
d
if
f
er
e
n
t
b
et
w
ee
n
b
u
s
i
an
d
b
u
s
j
(
r
ad
)
,
Q
i
an
d
Q
j
ar
e
th
e
r
ea
cti
v
e
p
o
w
er
o
n
t
h
e
se
n
d
i
n
g
an
d
r
ec
ei
v
i
n
g
b
u
s
es;
Q
G
is
t
h
e
g
en
er
ated
r
ea
cti
v
e
p
o
w
er
,
V
i
a
n
d
V
j
ar
e
th
e
v
o
lta
g
e
m
a
g
n
i
tu
d
e
at
t
h
e
s
e
n
d
in
g
a
n
d
r
ec
ei
v
in
g
b
u
s
e
s
,
G
ij
a
n
d
B
ij
i
s
th
e
m
u
t
u
al
co
n
d
u
cta
n
ce
a
n
d
s
u
b
ce
p
tan
ce
b
et
w
ee
n
b
u
s
i
a
n
d
b
u
s
j
an
d
L
o
s
s
K
P
is
t
h
e
to
tal
ac
tiv
e
p
o
w
er
lo
s
s
i
n
th
e
s
y
s
te
m
.
3.
M
UL
T
I
-
O
B
J
E
CT
I
V
E
E
VO
L
UT
I
O
NARY
P
RO
G
RAM
M
I
NG
T
h
e
MO
E
P
m
ain
l
y
ca
r
r
ied
o
u
t
s
ix
s
tep
s
,
n
a
m
el
y
,
i
n
itia
liz
atio
n
,
n
on
-
d
o
m
i
n
ated
s
o
r
ti
n
g
,
cr
o
w
d
in
g
d
is
tan
ce
,
m
u
ta
tio
n
,
co
m
b
i
n
ati
o
n
an
d
s
elec
tio
n
.
T
h
e
p
o
p
u
l
atio
n
is
i
n
itialized
to
g
e
n
er
at
e
r
an
d
o
m
n
u
m
b
er
g
en
er
atio
n
.
I
n
MO
E
P
in
itializ
atio
n
is
o
n
e
o
f
th
e
i
m
p
o
r
tan
t
p
r
o
ce
s
s
es
to
p
r
o
d
u
ce
f
ir
s
t
p
o
p
u
latio
n
ter
m
ed
as
p
ar
en
ts
.
T
h
en
,
T
h
e
p
o
p
u
latio
n
is
s
o
r
ted
b
ased
o
n
t
h
e
n
on
-
d
o
m
i
n
atio
n
.
E
ac
h
s
o
l
u
tio
n
s
h
o
u
l
d
b
e
co
m
p
ar
ed
w
it
h
ev
er
y
o
th
er
s
o
l
u
tio
n
in
t
h
e
p
o
p
u
latio
n
to
f
in
d
i
f
it
is
d
o
m
i
n
a
ted
.
E
ac
h
s
o
lu
tio
n
a
s
s
i
g
n
ed
a
f
itn
es
s
o
r
r
an
k
eq
u
al
to
its
n
o
n
-
d
o
m
in
at
io
n
lev
el
(
1
is
th
e
b
est
lev
el,
2
is
th
e
n
ex
t
b
est
lev
el
an
d
s
o
o
n
)
.
F
u
r
th
er
m
o
r
e,
th
e
f
ir
s
t
r
an
k
b
elo
n
g
s
to
th
e
m
o
s
t
ex
ce
lle
n
t
n
o
n
-
d
o
m
in
ated
s
et
in
t
h
e
p
o
p
u
latio
n
[
15
].
On
ce
th
e
n
o
n
-
d
o
m
i
n
atio
n
s
o
r
t
is
co
m
p
leted
,
t
h
e
cr
o
w
d
in
g
d
is
tan
ce
i
s
as
s
ig
n
ed
.
C
r
o
w
d
in
g
Dis
ta
n
ce
also
k
n
o
w
n
a
s
a
f
it
n
es
s
v
a
lu
e
o
f
a
n
in
d
iv
id
u
al.
T
h
e
p
u
r
p
o
s
e
o
f
cr
o
w
d
in
g
d
is
ta
n
ce
is
to
p
r
o
v
id
e
th
e
d
iv
er
s
it
y
in
t
h
e
p
o
p
u
lat
io
n
[
12
]
.
T
h
en
,
th
e
in
d
iv
id
u
al
s
o
lu
tio
n
s
ar
e
s
o
r
ted
in
d
escen
d
in
g
o
r
d
er
b
ased
o
n
th
e
m
ag
n
it
u
d
e
o
f
th
e
cr
o
w
d
i
n
g
d
is
ta
n
ce
v
al
u
es
.
Su
b
s
eq
u
e
n
tl
y
,
th
e
p
r
o
ce
s
s
co
n
ti
n
u
ed
w
it
h
th
e
m
u
tatio
n
p
r
o
ce
s
s
.
T
h
e
m
u
tatio
n
o
p
er
ato
r
ch
an
g
ed
its
cu
r
r
en
t
v
alu
e
o
f
a
co
n
t
in
u
o
u
s
v
ar
iab
le
to
a
n
eig
h
b
o
r
in
g
v
al
u
e
u
s
i
n
g
P
o
ly
n
o
m
ial
P
r
o
b
ab
ilit
y
Di
s
tr
i
b
u
tio
n
a
n
d
th
i
s
is
a
b
asic p
r
o
ce
d
u
r
e
o
f
an
y
g
e
n
etic
o
p
er
ato
r
[
1
6
].
T
h
e
o
f
f
s
p
r
in
g
p
r
o
d
u
ce
s
f
r
o
m
t
h
e
m
u
tat
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n
p
r
o
ce
s
s
ar
e
co
m
b
in
ed
w
it
h
t
h
e
clo
n
e
p
ar
en
t
to
u
n
d
er
g
o
a
s
elec
tio
n
p
r
o
ce
s
s
in
o
r
d
er
to
id
en
tify
t
h
e
ca
n
d
id
ates
h
a
v
e
th
e
c
h
an
ce
to
b
e
tr
an
s
cr
ib
ed
in
th
e
f
o
llo
w
in
g
g
en
er
atio
n
.
T
h
e
b
est
i
n
d
i
v
id
u
al
f
r
o
m
t
h
e
o
f
f
s
p
r
i
n
g
p
o
p
u
lat
io
n
w
ill
b
e
s
elec
ted
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a
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ch
e
m
e
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o
r
d
er
to
f
o
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m
t
h
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p
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t
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f
o
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th
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f
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i
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g
g
e
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.
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h
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f
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p
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in
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e
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s
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t
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h
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h
o
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o
b
j
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tiv
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ca
n
n
o
t
b
e
i
m
p
r
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h
o
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t
s
ac
r
i
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in
g
o
t
h
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j
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e.
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h
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r
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P
ar
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tim
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l
s
et
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f
n
o
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ated
s
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t
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n
s
,
th
e
B
est
C
o
m
p
r
o
m
is
e
So
l
u
tio
n
(
B
C
S)
w
a
s
s
elec
t
ed
f
o
r
th
e
d
ec
is
io
n
m
ak
er
as t
h
e
B
C
S
w
ill d
ec
id
e
th
e
b
est s
o
l
u
tio
n
i
n
b
et
w
ee
n
b
o
th
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
[
1
7
]
.
Fig
u
r
e
1
.
SVC
Mo
d
el
4.
ST
A
T
I
C
V
AR
CO
M
P
E
NSAT
O
R
(
SVC)
No
w
ad
a
y
s
,
SV
C
i
s
t
h
e
m
o
s
t p
o
p
u
lar
F
A
C
T
S
d
ev
ice
s
w
h
ic
h
ar
e
u
s
ed
to
s
o
l
v
e
o
p
ti
m
al
r
ea
ct
iv
e
p
o
w
er
p
r
o
b
lem
(
OR
P
D)
.
Fu
r
th
er
m
o
r
e,
SVC
n
o
t
o
n
l
y
g
e
n
er
ate
r
ea
ctiv
e
p
o
w
er
b
u
t
is
al
s
o
ab
s
o
r
b
in
g
r
ea
cti
v
e
p
o
w
er
.
SVC
co
n
n
ec
ted
i
n
p
ar
allel
to
tr
an
s
m
is
s
io
n
l
in
e
w
h
er
e
T
C
R
lo
ca
ted
in
p
ar
allel
w
it
h
ca
p
ac
ito
r
b
an
k
[
18
]
.
Usu
al
l
y
,
S
VC
i
n
s
talled
at
t
h
e
en
d
o
f
t
h
e
tr
an
s
m
is
s
io
n
li
n
e
o
r
m
id
p
o
in
t
o
f
tr
a
n
s
m
is
s
io
n
i
n
ter
co
n
n
ec
tio
n
s
.
Mo
r
eo
v
er
,
SVC
is
a
th
r
ee
p
h
ase
a
n
d
s
h
u
n
t
co
n
n
ec
ted
d
e
v
ice.
T
h
e
m
ai
n
f
u
n
ctio
n
o
f
S
VC
is
to
i
m
p
r
o
v
ed
v
o
ltag
e
i
n
w
ea
k
tr
an
s
m
i
s
s
io
n
l
in
e.
T
h
e
m
ath
e
m
at
ical
m
o
d
ell
in
g
o
f
SV
C
co
n
s
id
er
ed
in
th
i
s
s
tu
d
y
is
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
I
n
r
ec
en
t
y
ea
r
s
SVC
h
as
b
ee
n
u
s
ed
f
o
r
r
ea
cti
v
e
p
o
w
er
s
u
p
p
o
r
t
an
d
to
en
h
a
n
ce
v
o
ltag
e
s
tab
ilit
y
i
n
t
h
e
elec
t
r
ical
p
o
w
er
s
y
s
te
m
n
et
w
o
r
k
.
F
u
r
th
er
m
o
r
e,
th
e
SV
C
ca
n
ac
t a
s
b
o
t
h
in
d
u
cti
v
e
an
d
ca
p
ac
itiv
e
co
m
p
en
s
a
tio
n
b
y
ab
s
o
r
b
in
g
r
elea
s
i
n
g
r
ea
ctiv
e
p
o
w
er
.
Hen
ce
,
it
is
m
o
d
elled
as
id
ea
l
r
ea
ctiv
e
p
o
w
e
r
in
j
ec
tio
n
s
to
p
er
f
o
r
m
th
e
s
te
ad
y
-
s
tate
co
n
d
itio
n
at
b
u
s
i
.
T
h
e
ab
s
o
r
b
ed
o
r
in
jecte
d
p
o
w
er
at
b
u
s
i
in
t
h
e
s
y
s
te
m
is
r
ep
r
esen
ted
b
y
Q
svc
.
T
h
e
m
at
h
e
m
a
tical
f
o
r
m
u
latio
n
o
f
SV
C
co
n
s
tr
ai
n
t
s
h
o
w
n
a
s
Q
min
≤
Q
SV
C
≤
Q
max
-
2
0
0
MV
ar
≤
Q
S
VC
≤
2
0
0
MV
ar
(
6
)
5.
AP
P
L
I
CA
T
I
O
N
O
F
M
O
E
P
I
N
SVC
D
E
VI
CE
I
NS
T
AL
L
AT
I
O
N
MO
E
P
in
v
o
l
v
ed
in
itializatio
n
,
n
on
-
d
o
m
i
n
ated
s
o
r
ti
n
g
,
c
r
o
w
d
i
n
g
d
is
ta
n
ce
,
m
u
tat
io
n
,
co
m
b
in
at
io
n
an
d
s
elec
tio
n
.
T
r
an
s
m
i
s
s
io
n
lo
s
s
m
i
n
i
m
izatio
n
a
n
d
v
o
lta
g
e
s
t
ab
ilit
y
w
er
e
c
h
o
s
en
a
s
t
h
e
o
b
jectiv
e
f
u
n
ctio
n
f
o
r
th
e
o
p
ti
m
izatio
n
p
r
o
ce
s
s
.
T
h
e
f
lo
w
c
h
ar
t
f
o
r
th
e
ap
p
licatio
n
o
f
MO
E
P
in
SV
C
d
ev
ice
i
n
s
ta
llatio
n
i
s
s
h
o
w
n
i
n
E
rr
o
r!
Ref
er
ence
s
o
urce
no
t
f
o
un
d.
.
Sev
er
al
in
eq
u
a
lit
y
co
n
s
tr
ai
n
ts
ar
e
s
et
i
n
th
i
s
s
t
u
d
y
s
o
as
to
ac
h
iev
e
th
e
o
p
tim
a
l
s
o
lu
tio
n
.
Se
v
er
al
g
e
n
er
ato
r
o
u
tag
es
n
a
m
el
y
g
e
n
e
r
ato
r
at
b
u
s
1
1
an
d
1
3
w
er
e
s
u
b
j
ec
ted
in
to
th
e
s
y
s
te
m
.
T
h
e
s
elec
tio
n
s
o
f
o
u
ta
g
es
ar
e
b
ased
o
n
th
e
m
o
s
t
s
e
v
er
e
g
en
er
ato
r
an
d
in
t
h
e
s
y
s
te
m
to
m
a
x
i
m
ize
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
s
y
s
te
m
.
T
h
er
e
ar
e
t
w
o
co
n
s
tr
ai
n
ts
as
s
ig
n
ed
b
ef
o
r
e
th
e
SV
C
s
s
izin
g
is
o
p
ti
m
i
s
ed
.
T
h
e
co
n
s
tr
ain
ts
ar
e;
to
tal
lo
s
s
to
b
e
less
th
a
n
th
e
lo
s
s
_
s
et
an
d
v
o
ltag
e
at
th
e
lo
ad
ed
b
u
s
h
ig
h
er
th
an
V
_
s
et.
T
h
e
lo
s
s
_
s
et
a
n
d
V
_
s
et
ar
e
th
e
t
o
tal
lo
s
s
a
n
d
v
o
ltag
e
at
t
h
e
lo
ad
ed
b
u
s
b
ef
o
r
e
t
h
e
o
p
ti
m
i
s
atio
n
p
r
o
ce
s
s
w
as
co
n
d
u
cted
.
T
h
e
f
o
llo
w
i
n
g
s
tep
s
s
h
o
w
t
h
e
i
m
p
le
m
e
n
tatio
n
o
f
E
P
.
i.
Set th
e
g
en
er
ato
r
o
u
ta
g
es
.
ii.
Set th
e
lo
ad
in
g
f
ac
to
r
,
λ
.
iii.
Settin
g
t
h
e
lo
ca
tio
n
f
o
r
SVC
u
s
in
g
S
V
S
I
in
d
ex
o
f
s
tab
il
it
y
.
iv
.
Set th
e
O
R
P
D
co
n
s
tr
ai
n
ts
i.e
.
S
V
S
I
≤
S
V
S
I
_
s
et
an
d
to
tal
lo
s
s
≤
lo
s
s
_
s
et
as o
b
j
ec
tiv
e
f
u
n
ct
i
o
n
s
.
v.
Gen
er
ate
r
an
d
o
m
n
u
m
b
er
i.e
.
x
1
, x
2
,…
x
5
.
C
h
ec
k
f
o
r
co
n
s
tr
ai
n
t v
io
lat
io
n
s
.
I
f
co
n
s
tr
ai
n
ts
v
io
lated
,
g
o
to
s
tep
iv
,
o
th
er
w
is
e
g
o
to
s
tep
v
i
.
v
i.
Fil
l in
p
o
p
u
latio
n
p
o
o
l.
R
ep
ea
t step
(
ii
)
if
p
o
o
l w
a
s
n
o
t
f
u
ll,
o
th
er
w
is
e
co
n
tin
u
e
to
s
tep
(
v
i
i
).
v
ii.
C
alcu
late
th
e
n
o
n
-
d
o
m
i
n
ated
s
o
lu
tio
n
f
o
r
ea
ch
in
d
i
v
id
u
al
i
n
t
h
e
p
o
p
u
latio
n
.
v
iii.
So
r
t th
e
en
tire
p
o
p
u
latio
n
u
s
in
g
f
r
o
n
t.
ix
.
C
alcu
late
th
e
cr
o
w
d
in
g
d
is
ta
n
ce
f
o
r
ea
ch
f
r
o
n
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
0
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x.
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tate
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h
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3
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4
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d
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er
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n
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an
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in
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u
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ata
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x
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x
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Fig
u
r
e
2
.
Flo
w
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h
ar
t f
o
r
SV
C
i
n
s
tal
latio
n
u
s
i
n
g
MO
E
P
x
ii.
C
alcu
late
f
i
tn
e
s
s
b
y
r
u
n
n
i
n
g
lo
ad
f
lo
w
p
r
o
g
r
a
m
to
e
v
alu
a
te
S
V
S
I
v
alu
es a
n
d
tr
an
s
m
is
s
io
n
lo
s
s
v
alu
e
s
.
x
iii.
C
o
m
b
i
n
e
p
ar
en
ts
a
n
d
o
f
f
s
p
r
in
g
(
co
m
b
in
at
io
n
p
r
o
ce
s
s
)
.
x
iv
.
P
er
f
o
r
m
s
e
lectio
n
b
y
to
u
r
n
a
m
en
t selec
t
io
n
p
r
o
ce
s
s
f
r
o
m
t
h
e
co
m
b
i
n
e
d
ata.
x
v
.
I
d
en
tify
an
d
tr
an
s
cr
ib
e
n
e
w
g
e
n
er
atio
n
s
.
x
v
i.
I
f
s
o
lu
t
io
n
is
n
o
t c
o
n
v
er
g
ed
,
r
ep
ea
t step
v
to
x
ii
,
o
th
er
w
i
s
e
g
o
to
s
tep
x
v
i
i.
x
v
ii.
So
r
t th
e
P
ar
eto
o
p
ti
m
al
f
r
o
n
t.
x
v
iii.
Fin
d
t
h
e
b
est co
m
p
r
o
m
is
e
s
o
lu
tio
n
.
x
ix
.
P
lo
t th
e
P
ar
eto
o
p
tim
al
f
r
o
n
t a
n
d
th
e
b
est co
m
p
r
o
m
i
s
e
s
o
l
u
ti
o
n
in
to
g
r
ap
h
.
xx.
Sto
p
6.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
T
h
e
r
esu
lt
s
h
av
e
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ee
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e
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ed
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o
r
ith
m
f
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r
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lti
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tiv
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tio
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m
i
n
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in
s
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d
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e
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o
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ith
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h
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ee
n
test
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th
e
I
E
E
E
3
0
-
B
u
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
P
o
w
E
lec
&
Dr
i
S
y
s
t
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F
lex
ib
le
AC
T
ra
n
s
m
issio
n
s
y
ste
m
s,
”
IEE
E
Po
we
r E
n
g
i
n
e
e
rin
g
S
o
c
iety
,
IE
EE
P
re
ss
,
2
0
0
1
.
[4
]
N.
G
.
Hin
g
o
ra
n
i
a
n
d
L
.
Gy
u
g
y
i,
“
Un
d
e
rsta
n
d
i
n
g
F
A
C
T
S
:
Co
n
c
e
p
ts
a
n
d
T
e
c
h
n
o
lo
g
y
o
f
F
l
e
x
ib
le
AC
T
ra
n
s
m
is
sio
n
S
y
st
e
m
s,”
Ne
w
Y
o
rk
:
IEE
E
Pre
ss
,
2
0
0
0
.
[5
]
M
.
Eslam
i
,
e
t
a
l.
,
“
A
p
p
li
c
a
ti
o
n
o
f
P
S
S
a
n
d
F
A
CT
S
De
v
ice
s
fo
r
In
ten
sif
ica
ti
o
n
o
f
P
o
w
e
r
S
y
s
tem
S
tab
il
it
y
”
,
In
ter
n
a
t
io
n
a
l
Rev
iew o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
(
IRE
E)
,
v
o
l.
5
,
p
p
.
5
5
2
-
5
7
0
,
A
p
ril
2
0
1
0
.
[6
]
S
.
S
in
g
h
,
e
t
a
l.
,
,
“
A
p
p
li
c
a
ti
o
n
o
f
S
V
C
o
n
IEE
E
6
Bu
s
S
y
ste
m
f
o
r
Op
ti
m
iza
ti
o
n
o
f
V
o
lt
a
g
e
S
tab
il
it
y
,
”
In
d
o
n
e
sia
n
Jo
u
rn
a
l
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
a
n
d
In
f
o
rm
a
ti
c
s (IJEE
I),
v
o
l.
3
,
p
p
.
1
-
6
M
a
rc
h
2
0
1
5
.
[7
]
N.
P
.
P
a
d
h
y
,
e
t
a
l
.
,
“
A
H
y
b
rid
M
o
d
e
l
f
o
r
Op
t
im
a
l
P
o
w
e
r
F
lo
w
In
c
o
rp
o
ra
ti
n
g
F
A
CT
S
De
v
ic
e
s,
”
IEE
E
Po
we
r
En
g
i
n
e
e
rin
g
S
o
c
iety
W
in
ter
M
e
e
ti
n
g
,
v
o
l.
2
,
p
p
.
5
1
0
–
5
1
5
,
Ja
n
u
a
ry
2
0
0
1
.
[8
]
M
.
I.
A
z
i
m
a
n
d
M
.
F
.
Ra
h
m
a
n
,
“
Ge
n
e
ti
c
A
lg
o
rit
h
m
Ba
s
e
d
Re
a
c
t
iv
e
P
o
w
e
r
M
a
n
a
g
e
m
e
n
t
b
y
S
V
C,
”
In
tern
a
ti
o
n
a
l
Jo
u
rn
a
l
o
f
El
e
c
tri
c
a
l
a
n
d
C
o
m
p
u
ter E
n
g
in
e
e
rin
g
(IJECE),
v
o
l.
4
,
p
p
.
2
0
0
-
2
0
6
,
2
0
1
4
.
[9
]
S
.
Ra
n
g
a
n
a
th
a
n
a
n
d
M
.
S
u
ry
a
Ka
lav
a
th
i.
M
,
“
S
V
C
P
lac
e
m
e
n
t
f
o
r
Vo
lt
a
g
e
P
ro
f
il
e
E
n
h
a
n
c
e
m
e
n
t
Us
in
g
S
e
lf
A
d
a
p
ti
v
e
F
iref
l
y
A
l
g
o
rit
h
m
,
”
TE
L
KO
M
NIK
A
In
d
o
n
e
sia
n
Jo
u
r
n
a
l
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
v
o
l
.
1
2
,
p
p
.
5
9
7
6
-
5
9
8
4
,
2
0
1
4
.
[1
0
]
T
.
S
.
Ch
u
n
g
a
n
d
Y.
Z
.
L
i,
“
A
H
y
b
rid
G
A
A
p
p
ro
a
c
h
f
o
r
OP
F
w
it
h
Co
n
sid
e
ra
ti
o
n
o
f
F
A
C
T
S
De
v
i
c
e
s
,
”
IEE
E
Po
we
r
En
g
i
n
e
e
rin
g
Rev
iew
,
2
1
(
2
),
p
p
.
4
7
–
5
0
,
2
0
0
1
.
[1
1
]
W
.
S
h
a
o
a
n
d
V
.
V
i
tt
a
l,
“
L
P
-
B
a
se
d
OP
F
f
o
r
Co
rre
c
ti
v
e
F
A
C
T
S
Co
n
t
ro
l
to
Re
li
e
v
e
Ov
e
rlo
a
d
s
a
n
d
V
o
lt
a
g
e
V
io
latio
n
s,”
IEE
E
T
ra
n
sa
c
ti
o
n
o
n
PW
RS
,
2
1
(
4
),
p
p
.
1
8
3
2
–
1
8
3
9
,
2
0
0
6
.
8
[1
2
]
K.
De
b
,
e
t
a
l.
,
“
A
F
a
st
a
n
d
El
it
ist
M
u
lt
i
o
b
jec
ti
v
e
Ge
n
e
ti
c
A
l
g
o
rit
h
m
:
NSGA
-
II,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Evo
lu
ti
o
n
a
ry
Co
mp
u
t
a
ti
o
n
,
6
(2
)
,
p
p
.
1
8
2
–
1
9
7
,
A
p
ril
2
0
0
2
.
[1
3
]
L
.
Qi.
,
“
A
C
S
y
ste
m
S
tab
il
it
y
A
n
a
l
y
sis
a
n
d
A
ss
e
ss
m
e
n
t
f
o
r
S
h
ip
b
o
a
rd
P
o
w
e
r
S
y
ste
m
s
”
,
Ph
D
T
h
e
s
e
s
,
Un
iv
e
rsit
y
o
f
A
&
M
Tex
a
s,
2
0
0
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
Dr
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t
I
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N:
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694
Mu
lti
-
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jective
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vo
lu
tio
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r
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r
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mmin
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ta
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(
N
o
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u
l H
a
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)
887
[1
4
]
D.V
a
n
V
e
ld
h
u
ize
n
,
“
M
u
l
ti
o
b
jec
t
iv
e
Ev
o
lu
ti
o
n
a
ry
A
l
g
o
rit
h
m
s:
Cl
a
ss
if
i
c
a
ti
o
n
s,
A
n
a
l
y
s
e
s,
a
n
d
Ne
w
In
n
o
v
a
ti
o
n
s”
,
Ph
.
D.
t
h
e
sis
,
De
p
a
rtme
n
t
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
ri
n
g
.
Gr
a
d
u
a
te
S
c
h
o
o
l
o
f
E
n
g
i
n
e
e
rin
g
.
Ai
r
F
o
rc
e
,
2
0
1
1
.
[1
5
]
F.
D
i
P
ierr
o
,
e
t
a
l.
,
“
A
n
In
v
e
stig
a
ti
o
n
o
n
P
re
f
e
re
n
c
e
Ord
e
r
Ra
n
k
in
g
S
c
h
e
m
e
f
o
r
M
u
lt
io
b
jec
ti
v
e
Ev
o
lu
ti
o
n
a
ry
Op
ti
m
iza
ti
o
n
,”
IEE
E
T
ra
n
s
a
c
ti
o
n
s o
n
Ev
o
lu
ti
o
n
a
ry
Co
m
p
u
t
a
ti
o
n
,
v
o
l.
1
1
,
p
p
.
1
7
-
4
5
,
2
0
0
7
.
[1
6
]
K.
De
b
a
n
d
M
.
G
o
y
a
l,
“
A
Co
m
b
in
e
d
G
e
n
e
ti
c
A
d
a
p
ti
v
e
S
e
a
r
c
h
(G
e
n
e
a
s)
f
o
r
En
g
in
e
e
rin
g
De
s
ig
n
”,
Co
mp
u
ter
S
c
ien
c
e
a
n
d
In
fo
rm
a
t
ics
,
2
6
(4
),
p
p
.
30
-
4
5
,
1
9
9
6
.
[1
7
]
J.
Dh
il
l
o
n
,
“
M
u
lt
i
O
b
jec
ti
v
e
Op
t
im
iz
a
ti
o
n
o
f
P
o
w
e
r
Disp
a
tch
P
ro
b
lem
Us
in
g
NSGA
-
II
,
”
M
a
ste
r
T
h
e
sis,
T
h
a
p
a
r
Un
ive
rs
it
y
,
Pa
ti
a
la
,
Ju
ly
2
0
0
9
.
[1
8
]
D.
M
u
ra
li
,
e
t
a
l
.
,
“
Co
m
p
a
riso
n
o
f
F
A
C
T
S
De
v
ic
e
s
f
o
r
P
o
w
e
r
S
y
ste
m
S
tab
il
it
y
En
h
a
n
c
e
m
e
n
t
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Co
m
p
u
ter
A
p
p
l
ica
ti
o
n
s
,
Vo
l.
8
.
,
Oc
to
b
e
r
2
0
1
0
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
No
r
Ru
l
Ha
sm
a
A
b
d
u
ll
a
h
o
b
tain
e
d
a
Ba
c
h
e
lo
r
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
(Ho
n
s)
f
ro
m
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
a
lay
sia
in
2
0
0
2
,
M
.
En
g
in
El
e
c
tri
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a
l
En
g
in
e
e
rin
g
f
ro
m
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
la
y
sia
in
2
0
0
4
a
n
d
P
h
D
in
El
e
c
tri
c
a
l
En
g
in
e
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rin
g
i
n
2
0
1
2
f
ro
m
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
A
RA
.
He
r
re
se
a
r
c
h
in
tere
st
in
c
l
u
d
e
s
p
o
w
e
r
s
y
ste
m
sta
b
il
it
y
,
o
p
ti
m
iz
a
ti
o
n
tec
h
n
iq
u
e
s,
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
,
sw
a
r
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o
p
ti
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iza
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n
a
n
d
m
e
ta
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h
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risti
c
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h
n
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u
e
s.
T
o
d
a
te,
sh
e
is
c
u
r
re
n
tl
y
a
s
e
n
io
r
lec
tu
re
r
a
t
Un
iv
e
rsiti
M
a
lay
sia
P
a
h
a
n
g
,
M
a
lay
sia
.
M
a
h
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letc
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m
i
A
/P
M
o
rg
a
n
o
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tai
n
e
d
a
Ba
c
h
e
lo
r
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f
El
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c
tri
c
a
l
&
El
e
c
tro
n
ics
En
g
in
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rin
g
(Ho
n
s)
f
ro
m
Un
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M
a
l
a
y
sia
P
a
h
a
n
g
(UMP
)
in
2
0
1
4
,
M
.
E
n
g
in
El
e
c
tri
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a
l
En
g
in
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g
(P
o
w
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r
S
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m
)
f
ro
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Un
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M
a
la
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si
a
P
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h
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n
g
(UM
P
)
in
2
0
1
7
.
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r
re
s
e
a
rc
h
in
tere
st
in
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l
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d
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s
p
o
w
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r
s
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ste
m
sta
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il
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y
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p
t
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n
tec
h
n
i
q
u
e
s
e
sp
e
c
ially
Ev
o
lu
ti
o
n
a
ry
P
r
o
g
ra
m
m
in
g
.
Cu
rre
n
tl
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,
sh
e
is
w
o
rk
in
g
a
s an
A
ss
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t
M
a
n
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g
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r
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t
T
e
le
k
o
m
M
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la
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,
T
M
.
M
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h
f
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z
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h
M
u
sta
f
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o
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d
Di
p
lo
m
a
in
El
e
c
tro
n
ics
f
ro
m
Un
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e
rsiti
Tek
n
o
lo
g
i
M
a
lay
si
a
in
1
9
9
8
.
S
h
e
re
c
e
iv
e
d
Ba
c
h
e
lo
r
o
f
En
g
in
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g
(Ho
n
s)
in
Co
m
p
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ter
S
y
ste
m
&
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m
m
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ica
ti
o
n
s
f
ro
m
Un
iv
e
rsiti
P
u
tra
M
a
lay
sia
in
2
0
0
2
,
t
h
e
n
,
s
h
e
re
c
e
iv
e
d
M
a
s
ter
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
f
ro
m
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
la
y
sia
in
2
0
0
4
.
He
r
P
h
il
o
s
o
p
h
y
Do
c
to
r
wa
s
r
e
c
e
i
v
e
d
in
2
0
1
5
f
ro
m
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
A
R
A
M
a
la
y
si
a
in
th
e
f
ield
o
f
Bio
-
sig
n
a
l
EE
G
A
n
a
l
y
sis.
Cu
rre
n
tl
y
sh
e
is
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n
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c
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p
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ter i
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ti
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(HCI
).
Evaluation Warning : The document was created with Spire.PDF for Python.
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Evaluation Warning : The document was created with Spire.PDF for Python.