I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
p
u
t
er
Science
Vo
l.
11
,
No
.
1
,
J
u
ly
201
8
,
p
p
.
1
1
3
~1
2
0
I
SS
N:
2502
-
4752
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ee
cs
.
v
1
1
.
i1
.
p
p
113
-
1
2
0
113
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
co
r
e.
co
m/jo
u
r
n
a
ls
/in
d
ex
.
p
h
p
/
ijeec
s
Cha
o
tic
Lo
ca
l Se
a
rch Ba
sed Alg
o
r
ith
m
f
o
r
O
p
ti
m
a
l
DG
P
V
Allo
ca
tion
Sh
a
rif
a
h
Az
m
a
Sy
ed
M
us
t
a
f
f
a
1
,
I
s
m
a
il M
us
irin
2
,
M
o
hd
.
M
urt
a
dh
a
O
t
h
m
a
n
3
,
M
o
ha
m
a
d
K
ha
iruzza
m
a
n
M
o
ha
m
a
d
Z
a
m
a
ni
4
,
A
kh
t
a
r
K
a
la
m
5
1
Co
ll
e
g
e
o
f
En
g
in
e
e
rin
g
,
Un
iv
e
rsiti
T
e
n
a
g
a
Na
sio
n
a
l,
M
a
lay
sia
2,
3,
4
F
a
c
u
lt
y
o
f
En
g
in
e
e
rin
g
,
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
A
R
A
,
S
h
a
h
A
la
m
,
M
a
la
y
sia
5
Co
ll
e
g
e
o
f
En
g
in
e
e
rin
g
&
S
c
ien
c
e
,
V
icto
ria U
n
iv
e
rsity
,
A
u
stra
li
a
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
a
n
5
,
2
0
1
8
R
ev
i
s
ed
Mar
15
,
2
0
1
8
A
cc
ep
ted
Mar
30
,
2
0
1
8
T
h
e
a
d
v
e
n
t
o
f
a
d
v
a
n
c
e
d
tec
h
n
o
lo
g
y
h
a
s
led
to
th
e
in
c
re
a
se
o
f
e
lec
tri
c
it
y
d
e
m
a
n
d
in
m
o
st
c
o
u
n
tri
e
s
in
th
e
w
o
rld
.
T
h
is
p
h
e
n
o
m
e
n
o
n
h
a
s
m
a
d
e
th
e
p
o
w
e
r
s
y
ste
m
n
e
t
w
o
rk
o
p
e
ra
te
c
lo
se
to
t
h
e
sta
b
il
i
ty
li
m
it
.
T
h
e
re
f
o
re
,
th
e
p
o
w
e
r
u
ti
li
ti
e
s
a
re
lo
o
k
in
g
f
o
rw
a
rd
t
o
th
e
so
lu
ti
o
n
to
in
c
re
a
se
th
e
lo
a
d
a
b
il
it
y
o
f
th
e
e
x
isti
n
g
in
f
r
a
stru
c
tu
re
.
In
teg
ra
ti
o
n
o
f
re
n
e
wa
b
le
e
n
e
rg
y
in
to
th
e
g
rid
su
c
h
a
s
Distrib
u
ted
G
e
n
e
ra
ti
o
n
P
h
o
t
o
v
o
lt
a
ic
(DG
P
V
)
c
a
n
b
e
o
n
e
o
f
th
e
p
o
ss
ib
le
so
l
u
ti
o
n
s.
In
t
h
is
p
a
p
e
r
,
Ch
a
o
ti
c
M
u
tati
o
n
Im
m
u
n
e
Ev
o
lu
ti
o
n
a
r
y
P
r
o
g
ra
m
m
in
g
(
CM
IEP
)
a
lg
o
rit
h
m
is
u
se
d
a
s
th
e
o
p
ti
m
iz
a
ti
o
n
m
e
th
o
d
w
h
il
e
th
e
c
h
a
o
t
ic
m
a
p
p
in
g
w
a
s
e
m
p
lo
y
e
d
in
th
e
lo
c
a
l
se
a
rc
h
f
o
r
o
p
ti
m
a
l
lo
c
a
ti
o
n
a
n
d
siz
in
g
o
f
DG
P
V.
T
h
e
c
h
a
o
ti
c
lo
c
a
l
se
a
rc
h
h
a
s
th
e
c
a
p
a
b
il
it
y
o
f
f
in
d
in
g
th
e
b
e
st so
lu
ti
o
n
by
in
c
re
a
sin
g
th
e
p
o
ss
ib
il
it
y
o
f
e
x
p
lo
rin
g
th
e
g
lo
b
a
l
m
in
ima
.
T
h
e
p
ro
p
o
se
d
tec
h
n
i
q
u
e
w
a
s
a
p
p
li
e
d
to
th
e
IEE
E
3
0
Bu
s
RT
S
w
it
h
v
a
riatio
n
o
f
lo
a
d
.
T
h
e
sim
u
latio
n
re
s
u
lt
s
a
re
c
o
m
p
a
re
d
w
it
h
Ev
o
lu
ti
o
n
a
ry
P
r
o
g
ra
m
m
in
g
(EP
)
a
n
d
it
is
f
o
u
n
d
th
a
t
CM
IE
P
p
e
rf
o
rm
e
d
b
e
tt
e
r
in
m
o
st
o
f
t
h
e
c
a
se
s.
K
ey
w
o
r
d
s
:
C
h
ao
tic
lo
ca
l sear
c
h
DGP
V
o
p
tim
al
lo
ca
tio
n
FVSI
P
o
w
er
lo
s
s
e
s
Co
p
y
rig
h
t
©
2
0
1
8
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Sh
ar
i
f
ah
A
z
m
a
S
y
ed
Mu
s
ta
f
f
a
,
C
o
lleg
e
o
f
E
n
g
in
ee
r
i
n
g
,
Un
i
v
er
s
iti T
en
ag
a
Na
s
io
n
al,
Ma
la
y
s
ia
.
E
m
ail:
s
h
ar
i
f
ah
az
m
a
@
u
n
i
ten
.
ed
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
w
o
r
ld
elec
tr
ical
p
o
w
er
d
e
m
an
d
h
as
in
cr
ea
s
ed
ab
o
u
t
2
2
.
9
%
f
r
o
m
th
e
y
ea
r
2
0
0
5
to
th
e
y
ea
r
2
0
1
6
d
u
e
to
r
ap
id
g
r
o
w
t
h
i
n
in
d
u
s
t
r
ial
an
d
co
m
m
er
cial
ac
ti
v
itie
s
[
1
]
.
Du
e
to
t
h
i
s
s
it
u
atio
n
,
s
e
v
er
al
o
p
tio
n
s
h
a
v
e
b
ee
n
co
n
s
id
er
ed
to
m
ee
t
t
h
e
f
u
tu
r
e
e
n
er
g
y
d
e
m
an
d
f
o
r
th
e
ex
is
t
in
g
p
o
w
er
s
y
s
te
m
.
O
n
e
o
f
t
h
e
o
p
tio
n
s
is
t
h
e
in
te
g
r
atio
n
o
f
r
e
n
e
w
ab
le
en
e
r
g
y
s
u
c
h
as
t
h
e
s
o
lar
en
er
g
y
in
to
th
e
e
x
i
s
tin
g
g
r
id
.
Dis
t
r
ib
u
ted
Gen
er
atio
n
P
h
o
to
v
o
ltaic
(
DGP
V)
is
t
h
e
p
r
ef
er
ab
le
s
o
u
r
ce
co
m
m
o
n
l
y
i
m
p
le
m
en
ted
i
n
p
o
w
er
s
y
s
te
m
.
T
h
e
r
o
le
o
f
D
GP
V
is
m
ai
n
l
y
to
p
r
o
v
id
e
th
e
ac
ti
v
e
p
o
w
er
an
d
n
o
r
ea
ctiv
e
p
o
w
er
g
en
er
ated
to
t
h
e
s
y
s
te
m
.
T
h
i
s
s
o
lu
tio
n
n
o
t
o
n
l
y
ab
le
to
m
ee
t
t
h
e
in
cr
ea
s
i
n
g
p
o
w
er
d
e
m
an
d
b
u
t
al
s
o
ca
n
f
u
r
th
er
i
m
p
r
o
v
e
th
e
p
o
w
er
lo
s
s
es
an
d
th
e
s
y
s
te
m
s
tab
ilit
y
[
2
]
.
E
f
f
ec
t o
f
D
GP
V
o
n
t
h
e
d
is
tr
ib
u
tio
n
[
3
]
,
[
4
]
an
d
tr
an
s
m
is
s
io
n
s
y
s
te
m
s
[
5
]
h
a
v
e
b
ee
n
a
n
i
n
ter
es
tin
g
s
u
b
j
ec
t
f
o
r
m
a
n
y
r
e
s
ea
r
ch
er
s
.
Mo
s
t
o
f
th
e
r
esear
ch
e
f
f
o
r
ts
a
r
e
co
n
d
u
cted
f
o
r
lo
ca
tio
n
an
d
s
izin
g
o
f
DGP
V
to
s
atis
f
y
th
e
tec
h
n
ical
b
en
e
f
i
ts
s
u
c
h
as
lo
s
s
m
i
n
i
m
izatio
n
,
v
o
ltag
e
s
tab
il
it
y
e
n
h
a
n
ce
m
en
t
an
d
m
a
x
i
m
u
m
lo
ad
ab
ilit
y
in
cr
e
m
e
n
t.
Am
o
n
g
th
ese,
s
o
m
e
o
f
t
h
e
r
esear
ch
er
s
f
o
c
u
s
ed
o
n
l
y
to
m
i
n
i
m
ize
t
h
e
p
o
w
er
lo
s
s
es
a
s
th
e
o
b
j
ec
tiv
e
f
u
n
ct
io
n
[
6
]
-
[
8
]
.
I
n
o
th
er
s
t
u
d
ies
,
t
h
e
o
p
ti
m
al
lo
ca
tio
n
an
d
s
iz
in
g
o
f
D
G
w
e
r
e
s
tu
d
ied
to
f
o
cu
s
o
n
l
y
o
n
t
h
e
v
o
lta
g
e
s
tab
ilit
y
i
m
p
r
o
v
e
m
e
n
t a
s
t
h
e
o
b
j
ec
tiv
e
f
u
n
ct
io
n
[
9
]
,
[
1
0
]
.
I
n
t
h
e
la
s
t
f
e
w
y
ea
r
s
,
v
ar
io
u
s
tech
n
iq
u
es
h
av
e
b
ee
n
d
e
v
elo
p
ed
to
f
i
n
d
t
h
e
o
p
ti
m
al
lo
ca
tio
n
a
n
d
s
iz
e
o
f
DG.
T
h
ese
tech
n
iq
u
e
s
ca
n
b
e
ca
teg
o
r
ized
as
th
e
an
al
y
tic
al
m
et
h
o
d
s
an
d
m
eta
-
h
eu
r
i
s
tic
m
eth
o
d
s
.
Se
v
er
al
an
al
y
tical
ap
p
r
o
ac
h
es
ar
e
p
r
o
p
o
s
ed
f
o
r
DG
allo
ca
tio
n
to
m
i
n
i
m
ize
t
h
e
p
o
w
er
lo
s
s
es
[
1
1
]
,
[
1
2
]
.
T
h
e
r
ev
ie
w
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
11
,
No
.
1
,
J
u
ly
2
0
1
8
:
1
1
3
–
120
114
m
o
s
t
a
n
al
y
tical
m
et
h
o
d
s
f
o
r
D
G
allo
ca
tio
n
is
d
is
c
u
s
s
ed
in
[
1
3
]
.
Fo
r
th
e
s
a
m
e
p
u
r
p
o
s
e,
ev
o
l
u
tio
n
ar
y
alg
o
r
it
h
m
(
E
A
)
tech
n
iq
u
e
s
h
a
v
e
also
b
e
en
ap
p
lied
f
o
r
s
in
g
le
o
r
m
u
l
ti
DGs
lo
ca
tio
n
an
d
s
iz
in
g
f
o
r
v
ar
io
u
s
o
b
j
ec
tiv
es.
E
A
tech
n
iq
u
e
s
ar
e
p
r
ef
er
ab
l
e
d
u
e
to
it
s
co
m
p
u
tatio
n
al
ti
m
e
s
co
m
p
ar
ed
to
a
n
al
y
tica
l
tech
n
iq
u
e
s
.
E
A
tech
n
iq
u
es
s
tar
t
w
it
h
r
a
n
d
o
m
in
itial
izatio
n
o
f
th
e
p
o
p
u
latio
n
f
o
llo
w
e
d
b
y
th
e
e
v
o
lv
e
m
e
n
t
o
f
th
e
p
o
p
u
latio
n
ac
r
o
s
s
s
ev
er
al
g
e
n
er
atio
n
s
.
I
n
ea
ch
g
e
n
er
atio
n
,
f
it
in
d
i
v
id
u
al
s
ar
e
s
elec
ted
to
b
ec
o
m
e
p
ar
en
t
in
d
i
v
id
u
al
s
.
T
h
en
th
ese
i
n
d
iv
id
u
als
u
n
d
er
g
o
th
e
m
u
tatio
n
p
r
o
ce
s
s
to
p
r
o
d
u
ce
o
f
f
s
p
r
in
g
i
n
d
iv
id
u
als.
I
n
ev
o
l
u
tio
n
ar
y
alg
o
r
it
h
m
,
th
e
r
e
ar
e
t
w
o
i
m
p
o
r
tan
t
cr
iter
ia
w
h
ic
h
n
ee
d
to
b
e
co
n
s
id
er
ed
in
o
r
d
er
to
en
h
a
n
ce
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
alg
o
r
ith
m
n
a
m
el
y
th
e
e
x
p
lo
r
atio
n
an
d
th
e
ex
p
lo
itati
o
n
p
h
ase
[
1
4
]
,
[
1
5
]
.
E
x
p
lo
r
atio
n
s
p
ac
e
r
ef
er
s
to
th
e
ab
ilit
y
o
f
t
h
e
al
g
o
r
ith
m
to
s
ea
r
ch
f
o
r
a
s
o
lu
tio
n
i
n
th
e
w
h
o
le
r
eg
io
n
o
f
s
ea
r
c
h
s
p
ac
e.
Me
an
w
h
ile,
ex
p
lo
r
atio
n
s
p
ec
i
f
ies
t
h
e
co
n
v
er
g
e
n
ce
to
w
ar
d
s
t
h
e
b
est
o
p
ti
m
al
s
o
lu
tio
n
in
t
h
e
e
x
p
lo
r
atio
n
s
p
ac
e.
T
h
er
e
ar
e
m
an
y
tec
h
n
i
q
u
es
u
s
ed
to
i
m
p
r
o
v
e
t
h
ese
p
h
ase
s
f
o
r
ex
a
m
p
le
g
r
ad
ien
t
d
e
s
ce
n
t,
r
an
d
o
m
w
al
k
an
d
lo
ca
l
s
ea
r
ch
.
Ho
w
e
v
er
,
cu
r
r
en
tl
y
,
m
a
n
y
r
esear
c
h
er
s
h
a
v
e
co
n
s
id
er
ed
u
s
i
n
g
ch
ao
s
t
h
eo
r
y
as
o
n
e
o
f
t
h
e
ap
p
r
o
ac
h
es to
i
m
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
ce
o
f
e
v
o
lu
tio
n
ar
y
a
lg
o
r
ith
m
[
1
6
]
-
[
1
8
]
.
Fr
o
m
th
e
p
r
e
v
io
u
s
s
tu
d
ies,
m
a
n
y
m
eta
h
eu
r
i
s
tic
alg
o
r
it
h
m
s
h
a
v
e
b
ee
n
in
teg
r
ated
w
i
th
a
ch
ao
t
ic
m
ap
in
o
r
d
er
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
.
I
n
[
1
8
]
,
th
e
ch
ao
tic
lo
ca
l
s
ea
r
ch
i
s
u
tili
ze
d
i
n
Di
f
f
er
en
t
ial
E
v
o
lu
tio
n
(
DE
)
an
d
h
as
b
ee
n
te
s
ted
o
n
1
3
class
ica
l
tes
t
f
u
n
ctio
n
s
.
T
h
e
r
es
u
lt
s
h
o
w
ed
s
ig
n
i
f
ica
n
t
i
m
p
r
o
v
e
m
en
t
i
n
e
x
p
lo
itatio
n
p
h
ase
a
s
co
m
p
ar
e
d
to
th
e
tr
ad
itio
n
al
DE
.
P
en
g
L
u
et
a
l
[
1
9
]
also
co
n
clu
d
ed
th
at
ch
ao
tic
b
eh
av
io
u
r
is
ab
le
to
en
h
an
ce
t
h
e
p
er
f
o
r
m
an
ce
o
f
d
if
f
er
e
n
tial
b
ee
co
lo
n
y
o
p
ti
m
izatio
n
to
s
o
lv
e
th
e
ec
o
n
o
m
i
c
d
is
p
atch
p
r
o
b
le
m
.
T
h
e
r
es
u
lts
o
f
t
h
ese
s
tu
d
ie
s
p
r
o
v
id
e
th
e
p
r
o
o
f
o
f
h
o
w
s
u
c
ce
s
s
f
u
l
c
h
ao
s
th
eo
r
y
is
i
n
i
m
p
r
o
v
in
g
th
e
ev
o
l
u
tio
n
ar
y
al
g
o
r
i
th
m
.
I
n
t
h
is
s
t
u
d
y
,
th
e
c
h
ao
tic
m
ap
p
i
n
g
i
s
u
s
e
d
to
en
h
a
n
ce
th
e
p
er
f
o
r
m
a
n
ce
o
f
C
h
ao
tic
M
u
tatio
n
I
m
m
u
n
e
E
v
o
lu
t
io
n
ar
y
A
l
g
o
r
ith
m
(
C
MI
E
P
)
as
t
h
e
lo
ca
l
s
ea
r
ch
tec
h
n
iq
u
e
f
o
r
o
p
tim
al
DGP
V
allo
ca
tio
n
.
C
o
m
p
ar
ativ
e
s
tu
d
ie
s
w
er
e
p
er
f
o
r
m
ed
w
it
h
r
esp
ec
t
to
E
v
o
lu
tio
n
a
r
y
P
r
o
g
r
a
m
m
i
n
g
(
E
P
)
.
R
esu
lt
s
h
ad
in
d
icate
d
th
a
t
C
MI
E
P
w
it
h
ch
ao
tic
lo
ca
l sear
ch
o
u
tp
er
f
o
r
m
ed
E
P
in
ter
m
s
o
f
ac
c
u
r
ac
y
o
f
F
V
S
I
an
d
tr
an
s
m
i
s
s
io
n
lo
s
s
es.
2.
P
RO
B
L
E
M
F
O
R
M
UL
AT
I
O
N
I
n
t
h
is
s
t
u
d
y
,
t
w
o
s
i
n
g
le
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
ar
e
co
n
s
i
d
er
ed
an
d
o
p
tim
ized
s
ep
ar
at
el
y
w
h
ile
s
atis
f
y
in
g
s
y
s
te
m
eq
u
alit
y
an
d
in
eq
u
alit
y
co
n
s
tr
ain
ts
.
2
.
1
.
O
bje
ct
iv
e
f
un
ct
io
ns
T
h
e
o
b
j
ec
tiv
e
o
f
th
e
o
p
ti
m
al
lo
ca
tio
n
a
n
d
s
izi
n
g
o
f
DGP
V
is
to
m
in
i
m
ize
t
w
o
o
b
j
ec
tiv
e
f
u
n
c
tio
n
s
n
a
m
e
l
y
t
h
e
ac
ti
v
e
p
o
w
er
lo
s
s
an
d
th
e
v
o
lta
g
e
s
tab
ilit
y
i
n
d
ex
s
ep
ar
atel
y
.
2
.
2
.
M
ini
m
iza
t
io
n o
f
Vo
lt
a
g
e
St
a
bil
it
y
I
nd
e
x
A
li
n
e
b
ased
v
o
ltag
e
s
tab
ilit
y
in
d
ex
,
F
V
S
I
d
ev
elo
p
ed
b
y
I
.
Mu
s
ir
i
n
et
a
l.
[
2
0
]
is
u
s
ed
to
m
ea
s
u
r
e
t
h
e
clo
s
en
es
s
o
f
th
e
s
y
s
te
m
to
v
o
ltag
e
co
llap
s
e.
T
h
e
F
V
S
I
f
o
r
m
u
la
tio
n
w
a
s
d
er
iv
ed
f
r
o
m
a
v
o
ltag
e
q
u
ad
r
atic
eq
u
atio
n
o
n
a
t
w
o
-
b
u
s
s
y
s
te
m
an
d
d
ef
in
ed
b
y
t
h
e
f
o
llo
w
i
n
g
e
q
u
atio
n
:
2
1
2
4
j
i
ZQ
fX
VX
1
1
F
X
m
i
n
f
X
(
1
)
Z
is
lin
e
i
m
p
ed
an
ce
X
is
lin
e
r
ea
cta
n
ce
Qj
is
r
ea
ctiv
e
p
o
w
er
at
t
h
e
r
ec
eiv
i
n
g
e
n
d
Vi
is
v
o
lta
g
e
at
th
e
s
en
d
i
n
g
e
n
d
X
f
1
is
th
e
f
ir
s
t o
b
j
ec
tiv
e
f
u
n
ctio
n
2
.
3
.
M
ini
m
iza
t
io
n Ac
t
iv
e
P
o
w
er
L
o
s
s
T
h
e
to
tal
r
ea
l p
o
w
er
lo
s
s
,
P
loss
in
th
e
tr
a
n
s
m
i
s
s
io
n
lin
e
s
ca
n
b
e
ex
p
r
ess
ed
as in
eq
u
atio
n
(
2
)
:
2
22
lo
s
s
,
i
f
X
P
fo
r
i
n
r
F
X
m
in
f
X
(
2
)
nr
is
th
e
n
u
m
b
er
o
f
tr
an
s
m
is
s
i
o
n
lin
e
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
C
h
a
o
tic
Lo
ca
l S
ea
r
ch
B
a
s
ed
A
lg
o
r
ith
m
fo
r
Op
tima
l D
GP
V
A
llo
ca
tio
n
(
S
h
a
r
ifa
h
A
z
ma
S
ye
d
Mu
s
ta
ffa
)
115
2
.
4
.
Co
ns
t
ra
ints
T
h
e
o
b
j
e
c
ti
v
e
f
u
n
c
t
i
o
n
s
a
r
e
s
u
b
j
e
c
te
d
t
o
t
h
e
f
o
l
l
o
w
i
n
g
c
o
n
s
tr
a
i
n
t
s
:
1)
T
h
e
g
en
er
atin
g
ca
p
ac
it
y
N
i
P
P
P
m
a
x
i
,
DG
m
i
n
i
,
DG
i
,
DG
(
3
)
w
h
er
e
P
m
i
n
i
,
DG
an
d
P
m
a
x
i
,
DG
ar
e
th
e
m
in
i
m
u
m
a
n
d
th
e
m
ax
i
m
u
m
o
u
tp
u
t o
f
D
GP
V
r
esp
ec
tiv
el
y
a
n
d
i
is
th
e
t
o
tal
b
u
s
n
u
m
b
er
.
T
h
e
b
u
s
v
o
ltag
e
c
o
n
s
tr
ain
t is d
ef
i
n
ed
as
f
o
llo
w
s
:
N
i
v
m
a
x
v
i
v
m
i
n
(
4
)
w
h
er
e
V
min
an
d
V
max
ar
e
th
e
lo
w
er
an
d
t
h
e
u
p
p
er
b
o
u
n
d
o
f
b
u
s
v
o
lta
g
e
li
m
it r
esp
ec
ti
v
e
l
y
a
n
d
V
i
is
th
e
v
o
lta
g
e
m
ag
n
it
u
d
e
at
b
u
s
i
f
o
r
all
th
e
N
b
u
s
.
T
h
e
p
o
w
er
b
alan
ce
co
n
s
tr
ain
t
i
s
s
h
o
w
n
i
n
eq
u
atio
n
(
5
)
:
N
i
P
)
P
P
P
l
o
s
s
i
,
D
i
,
G
i
,
DG
(
(
5
)
w
h
er
e
P
i
,
G
,
P
i
,
D
,
P
i
,
DG
an
d
P
l
o
s
s
ar
e
th
e
ac
tiv
e
p
o
w
er
o
f
b
u
s
g
en
er
ato
r
,
ac
tiv
e
lo
ad
an
d
ac
tiv
e
p
o
w
er
lo
s
s
es r
esp
ec
ti
v
el
y
.
i
i
s
th
e
to
t
al
b
u
s
n
u
m
b
er
.
3.
CM
I
E
P
F
O
R
DG
P
V
P
L
AC
E
M
E
NT
Fo
r
a
tr
an
s
m
is
s
io
n
n
e
t
w
o
r
k
,
lo
ad
f
lo
w
an
al
y
s
is
is
ca
r
r
ied
o
u
t
an
d
F
V
S
I
o
r
lo
s
s
v
alu
e
is
co
m
p
u
ted
f
o
r
ea
ch
lin
e
u
s
i
n
g
E
q
u
atio
n
(
1
)
an
d
E
q
u
atio
n
(
2
)
r
esp
ec
tiv
el
y
.
T
h
e
C
MI
E
P
alg
o
r
ith
m
i
s
u
s
ed
f
o
r
f
in
d
in
g
t
h
e
o
p
tim
u
m
s
ize
o
f
DGP
V
at
a
n
o
p
tim
u
m
lo
ca
tio
n
b
a
s
ed
o
n
a
m
i
n
i
m
u
m
to
tal
p
o
w
er
lo
s
s
,
w
i
th
co
n
s
tr
ain
t
s
g
iv
e
n
in
E
q
u
atio
n
(
3
)
to
(
5
)
.
I
n
th
is
s
tu
d
y
,
th
e
3
0
B
u
s
I
E
E
E
R
T
S
is
u
s
ed
as
th
e
test
s
y
s
te
m
.
T
h
e
co
m
p
lete
f
lo
w
c
h
ar
t
f
o
r
DGP
V
allo
ca
tio
n
an
d
s
izi
n
g
is
r
ep
r
esen
ted
i
n
Fi
g
u
r
e
1
.
3
.
1
.
Cha
o
t
ic
L
o
ca
l
Sea
rc
h
T
o
im
p
r
o
v
e
th
e
s
ea
r
c
h
ca
p
ab
ilit
y
an
d
f
o
r
ac
h
ie
v
i
n
g
g
lo
b
al
o
p
tim
a
s
o
l
u
tio
n
o
f
DGP
V
lo
c
atio
n
an
d
s
izin
g
,
c
h
a
o
tic
d
y
n
a
m
ics
i
s
i
n
co
r
p
o
r
ated
in
to
C
MI
E
P
.
A
ch
ao
tic
f
u
n
ctio
n
k
n
o
w
n
a
s
P
iece
w
i
s
e
L
i
n
ea
r
is
e
m
p
lo
y
ed
an
d
th
e
eq
u
atio
n
i
s
d
ef
in
ed
as i
n
E
q
u
atio
n
(
6
)
[
2
1
]
,
[
2
2
]
:
1
0
0
5
0
5
1
0
5
0
5
1
1
1
1
tt
it
t
tt
tt
c
p
c
,
p
c
p
.
p
c
p
,
.
c
p
c
.
p
c
.
,
p
c
p
c
p
,
(
6
)
t
c
is
th
e
ch
ao
tic
v
ar
iab
le
th
at
i
s
in
f
lu
e
n
ce
d
b
y
th
e
v
al
u
e
o
f
co
n
tr
o
l
p
ar
am
eter
,
p
.
T
h
e
PW
L
C
M
ex
h
ib
it
s
ch
ao
tic
d
y
n
a
m
ic
s
i
n
(
0
,
1
)
w
h
e
n
co
n
tr
o
l p
ar
a
m
eter
,
1
5
0
5
0
0
,
.
.
,
p
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
11
,
No
.
1
,
J
u
ly
2
0
1
8
:
1
1
3
–
120
116
Fig
u
r
e
1
.
Flo
w
c
h
ar
t f
o
r
DGP
V
A
l
lo
ca
tio
n
Usi
n
g
C
MI
E
P
Step
1
:
Setti
n
g
th
e
iter
atio
n
,
t
=0
,
g
lo
b
al
b
est
d
ec
is
io
n
s
,
V
...,
,
i
t
x
i
,
b
e
s
t
2
1
,
f
i
tn
es
s
v
a
lu
e
f
r
o
m
th
e
o
p
tim
izatio
n
t
i
,
b
e
s
t
x
F
,
in
itia
l
v
al
u
e
o
f
s
e
ar
ch
s
p
ac
e
f
o
r
ea
ch
o
f
t
h
e
v
ar
i
ab
le,
r
x
as in
E
q
u
atio
n
(
7
)
:
12
2
x
U
B
L
B
r
t
x
,
,
.
.
i
(
7
)
an
d
ch
ao
tic
v
ar
iab
les,
x
,
r
a
n
d
t
c
x
1
w
h
er
e
UB
an
d
LB
ar
e
t
h
e
u
p
p
er
an
d
lo
w
er
b
o
u
n
d
ar
y
o
f
s
ea
r
ch
i
n
g
s
p
ac
e
f
o
r
d
ec
is
io
n
v
ar
iab
les
x
.
an
d
V
is
th
e
to
tal
d
ec
is
io
n
v
ar
iab
les to
b
e
s
o
lv
e
r
es
p
ec
tiv
el
y
.
Step
2
:
Dete
r
m
i
n
e
t
h
e
c
h
ao
tic
v
ar
iab
les
1
t
c
x
f
o
r
t
h
e
n
ex
t
iter
ati
o
n
u
s
i
n
g
t
h
e
c
h
ao
tic
eq
u
a
tio
n
i
n
eq
u
atio
n
(
6
)
ac
co
r
d
in
g
to
t
c
x
.
Step
3
:
Dete
r
m
in
e
t
h
e
d
ec
is
io
n
v
ar
iab
le
t
i
U
b
y
co
n
v
er
ti
n
g
t
h
e
ch
ao
tic
v
ar
iab
les
1
t
c
x
u
s
i
n
g
th
e
f
o
llo
w
i
n
g
eq
u
atio
n
:
1
1
0
5
t
i
i
i
x
x
U
x
t
x
t
c
t
.
*
r
t
(
8
)
Step
4
: Calcu
late
t
h
e
n
e
w
f
i
tn
ess
t
i
U
F
.
Step
5
:
E
v
alu
ati
n
g
t
h
e
f
it
n
es
s
v
al
u
e
o
f
th
e
n
e
w
s
o
l
u
tio
n
,
t
i
U
F
w
ith
t
h
e
o
p
ti
m
ize
f
i
tn
e
s
s
v
alu
e
t
i
,
b
e
s
t
X
F
u
s
i
n
g
t
h
e
e
v
alu
a
tio
n
s
tep
as
F
ig
u
r
e
2
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
C
h
a
o
tic
Lo
ca
l S
ea
r
ch
B
a
s
ed
A
lg
o
r
ith
m
fo
r
Op
tima
l D
GP
V
A
llo
ca
tio
n
(
S
h
a
r
ifa
h
A
z
ma
S
ye
d
Mu
s
ta
ffa
)
117
Fig
u
r
e
2
.
E
v
alu
atio
n
A
l
g
o
r
ith
m
Step
6
: U
p
d
ate
th
e
s
ea
r
ch
r
ad
iu
s
f
o
r
ea
ch
d
ec
is
io
n
v
ar
iab
les,
x
i
as in
E
q
u
atio
n
(
9
)
:
1
0
1
xx
r
t
r
t
*
r
a
n
d
,
(
9
)
Step
7
:
I
f
m
a
x
i
m
u
m
i
ter
atio
n
is
r
ea
ch
ed
,
d
is
p
la
y
t
h
e
o
u
tp
u
t
o
f
ch
ao
tic
lo
ca
l
s
ea
r
c
h
.
Ot
h
er
w
i
s
e,
g
o
b
ac
k
to
Step
2
.
4.
SI
M
UL
AT
I
O
N
R
E
S
UL
T
S
T
h
e
ch
ao
tic
v
ar
iab
le
i
s
i
n
itia
li
ze
d
b
y
t
h
e
r
a
n
d
f
u
n
ct
io
n
w
h
o
s
e
ad
v
a
n
ta
g
e
i
s
alr
ea
d
y
ad
d
r
ess
ed
i
n
t
h
e
p
r
ev
io
u
s
s
ec
tio
n
.
I
E
E
E
3
0
b
u
s
R
T
S
is
u
s
ed
as
t
h
e
te
s
t
s
y
s
t
e
m
to
v
er
if
y
t
h
e
f
ea
s
ib
il
it
y
an
d
r
o
b
u
s
tn
es
s
o
f
th
e
p
r
o
p
o
s
ed
C
MI
E
P
w
it
h
ch
ao
ti
c
lo
ca
l
s
ea
r
ch
.
T
w
o
DGP
V
in
s
tallatio
n
s
ar
e
u
s
ed
i
n
th
i
s
s
t
u
d
y
to
f
in
d
o
p
ti
m
a
l
lo
ca
tio
n
an
d
s
ize
o
f
DGP
V.
T
h
er
e
ar
e
th
r
ee
ca
s
es
co
n
s
id
e
r
ed
in
t
h
is
p
ap
er
to
m
o
n
ito
r
t
h
e
ca
p
ab
ilit
y
o
f
th
e
o
p
tim
izatio
n
tec
h
n
iq
u
e.
T
h
e
s
i
m
u
lat
io
n
r
es
u
lt
o
b
tain
ed
u
s
in
g
C
MI
E
P
is
t
h
en
co
m
p
ar
ed
w
it
h
th
e
r
es
u
l
t
o
b
tain
ed
b
y
E
P
to
p
r
o
v
e
th
e
ef
f
ec
ti
v
en
e
s
s
o
f
t
h
e
p
r
o
p
o
s
ed
al
g
o
r
ith
m
.
4
.
1
.
B
a
s
e
Ca
s
e
T
h
e
b
est
r
esu
lts
o
b
tain
ed
b
y
th
e
i
m
p
le
m
e
n
tatio
n
o
f
th
e
t
w
o
o
b
j
ec
tiv
es
s
ep
ar
atel
y
i
n
b
ase
ca
s
e
co
n
d
itio
n
i
s
s
h
o
w
n
i
n
T
ab
le
1
.
T
h
e
r
esu
lt
s
o
f
p
o
s
t
-
in
s
tallatio
n
o
f
DGP
V
h
av
e
b
ee
n
co
m
p
ar
ed
w
it
h
th
e
r
esu
lts
f
r
o
m
p
r
e
-
DGP
V
i
n
s
ta
llatio
n
.
T
h
e
F
V
S
I
o
f
p
o
s
t
-
i
n
s
tallat
io
n
u
s
i
n
g
C
MI
E
P
h
as
b
ee
n
r
ed
u
c
ed
to
3
2
%
f
r
o
m
th
e
p
r
e
-
in
s
tallatio
n
F
V
S
I
.
Me
an
wh
ile,
b
y
u
s
i
n
g
EP
th
e
F
V
S
I
h
as
b
ee
n
r
ed
u
ce
d
to
2
5
%
o
f
th
e
p
r
e
-
i
n
s
ta
llatio
n
v
alu
e.
T
h
e
lo
s
s
s
h
o
w
s
t
h
e
r
ed
u
ctio
n
o
f
ab
o
u
t
6
7
%
f
r
o
m
t
h
e
p
r
e
-
in
s
tallatio
n
v
a
lu
e
b
y
u
s
in
g
C
MI
E
P
an
d
ab
o
u
t
6
5
%
b
y
u
s
i
n
g
E
P
.
T
h
er
ef
o
r
e,
it
ca
n
b
e
s
ee
n
th
at
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
is
ca
p
ab
le
o
f
f
i
n
d
i
n
g
b
etter
s
o
l
u
tio
n
s
f
o
r
ea
ch
o
b
j
ec
tiv
e
as
co
m
p
ar
e
d
to
E
P
.
T
h
e
co
n
v
er
g
e
n
ce
ch
a
r
ac
ter
is
tic
f
o
r
th
is
ca
s
e
is
p
r
es
en
ted
i
n
Fig
u
r
e
3
.
R
es
u
lts
i
n
d
icate
d
th
a
t CMI
E
P
h
as b
etter
f
i
tn
e
s
s
a
n
d
co
n
v
er
g
en
ce
r
ate
co
m
p
ar
ed
to
E
P.
T
ab
le
1
.
C
o
m
p
ar
is
o
n
o
f
Ob
j
ec
tiv
e
F
u
n
c
tio
n
s
f
o
r
2
DGP
V
I
n
s
tallatio
n
O
b
j
e
c
t
i
v
e
F
u
n
c
t
i
o
n
Pre
-
i
n
st
a
l
l
a
t
i
o
n
P
o
st
-
I
n
st
a
l
l
a
t
i
o
n
C
M
I
EP
EP
FVS
I
0
.
2
0
3
7
0
.
1
3
8
1
0
.
1
5
1
9
L
o
ss (M
W
)
1
7
.
5
8
5
.
6
8
5
.
9
9
1
1
0
50
1
tt
i
be
st
,
i
tt
ii
tt
be
st
i
tt
be
st
be
st
,
i
/
*
Ev
al
ua
t
i
on
s
t
e
p
*
/
t
w
hi
l
e
t
i
f
F
(
U
)
F
(
x
)
t
hen
XU
XX
e
l
s
e
Xx
e
nd
i
f
tt
e
nd
w
hi
l
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
11
,
No
.
1
,
J
u
ly
2
0
1
8
:
1
1
3
–
120
118
(
a)
(
b
)
Fig
u
r
e
3
.
(
a)
F
V
S
I
an
d
(
b
)
L
o
s
s
C
o
n
v
er
g
e
n
ce
C
h
ar
ac
ter
is
t
ics
o
f
C
MI
E
P
an
d
E
P
w
it
h
T
w
o
Un
it
s
DGP
V
4
.
2
.
F
V
SI
M
ini
m
iza
t
io
n
R
es
u
lts
f
o
r
F
V
S
I
m
in
i
m
izatio
n
u
s
i
n
g
C
MI
E
P
w
h
en
lo
ad
b
u
s
2
9
i
s
s
u
b
j
ec
ted
to
lo
ad
v
a
r
iatio
n
is
tab
u
lated
in
T
ab
le
2
.
T
h
e
in
cr
e
m
en
t
o
f
lo
ad
in
g
co
n
d
itio
n
s
h
o
w
ed
a
s
ig
n
i
f
ican
t
i
n
cr
ea
s
e
on
th
e
m
ax
i
m
u
m
F
V
S
I
o
f
th
e
s
y
s
te
m
.
Ho
w
e
v
er
,
af
ter
th
e
DGP
V
in
s
talla
tio
n
,
F
V
S
I
h
as
b
ee
n
r
ed
u
ce
d
t
o
2
8
%,
6
%
an
d
9
%
f
o
r
lo
ad
v
ar
iatio
n
o
f
1
0
,
2
0
an
d
3
0
MV
A
R
r
esp
ec
tiv
e
l
y
.
T
h
e
lo
ca
tio
n
a
n
d
s
izin
g
o
f
DG
P
V
to
ac
h
ie
v
e
t
h
e
i
m
p
r
o
v
e
m
en
t o
f
F
V
S
I
ca
n
b
e
r
ef
er
r
ed
to
th
e
s
a
m
e
tab
le.
T
ab
le
2
.
F
V
S
I
Min
i
m
izatio
n
wh
en
L
o
ad
Var
iatio
n
w
as
Su
b
j
ec
ted
to
B
u
s
2
9
L
o
a
d
i
n
g
C
o
n
d
i
t
i
o
n
Pre
-
I
n
st
a
l
l
a
t
i
o
n
P
o
st
-
I
n
st
a
l
l
a
t
i
o
n
u
si
n
g
C
M
I
EP
D
G
P
V
D
G
P
V
%
FV
S
I
R
e
d
u
c
t
i
o
n
Q
d29
(
M
V
A
R
)
FVS
I
L
o
c
a
t
i
o
n
S
i
z
e
(
M
W
)
FVS
I
10
0
.
2
1
1
1
17
29
4
2
.
8
9
4
2
.
9
0
0
.
1
5
0
3
2
8
.
7
8
20
0
.
3
5
7
3
30
24
1
3
.
1
8
5
6
.
2
9
0
.
3
3
5
9
5
.
9
9
30
0
.
5
9
8
7
30
24
1
5
.
9
6
5
6
.
6
1
0
.
5
4
4
9
8
.
9
9
4
.
3
.
T
ra
ns
m
i
s
s
io
n L
o
s
s
M
ini
m
iz
a
t
io
n
T
ab
le
3
tab
u
lates
th
e
r
es
u
lt
f
o
r
DGP
V
o
p
tim
a
l
lo
ca
tio
n
a
n
d
s
izi
n
g
u
s
i
n
g
C
MI
E
P
.
W
ith
t
h
e
s
a
m
e
lo
ad
in
g
co
n
d
it
io
n
s
a
s
t
h
e
p
r
ev
io
u
s
ca
s
e,
DGP
V
ca
n
also
r
ed
u
ce
th
e
tr
an
s
m
i
s
s
io
n
lo
s
s
e
s
o
f
th
e
s
y
s
te
m
.
Fo
r
in
s
ta
n
ce
,
at
lo
ad
in
g
co
n
d
it
io
n
o
f
2
0
MV
AR
t
h
e
tr
a
n
s
m
is
s
io
n
lo
s
s
es
r
ed
u
ce
d
to
7
.
1
9
MW
w
h
ic
h
6
2
.
9
1
%
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
C
h
a
o
tic
Lo
ca
l S
ea
r
ch
B
a
s
ed
A
lg
o
r
ith
m
fo
r
Op
tima
l D
GP
V
A
llo
ca
tio
n
(
S
h
a
r
ifa
h
A
z
ma
S
ye
d
Mu
s
ta
ffa
)
119
r
ed
u
ctio
n
f
r
o
m
t
h
e
p
r
e
-
i
n
s
tall
atio
n
lo
s
s
.
T
h
e
o
p
tim
al
lo
ca
ti
o
n
an
d
s
izin
g
o
p
ti
m
ized
b
y
C
MI
E
P
tech
n
iq
u
e
ar
e
b
u
s
es
2
1
an
d
7
w
it
h
5
9
.
6
8
MW
an
d
6
1
.
6
6
MW
r
esp
ec
tiv
ely
.
Fro
m
t
h
e
tab
le,
it
is
clea
r
l
y
s
h
o
w
ed
t
h
at
b
u
s
1
2
an
d
b
u
s
7
ar
e
th
e
o
p
ti
m
al
lo
ca
t
io
n
f
o
r
all
th
e
lo
ad
in
g
co
n
d
itio
n
.
T
ab
le
3
.
T
r
an
s
m
is
s
io
n
L
o
s
s
M
in
i
m
izatio
n
w
h
e
n
L
o
ad
Var
iatio
n
w
as
S
u
b
j
ec
ted
to
B
u
s
29
L
o
a
d
i
n
g
C
o
n
d
i
t
i
o
n
Pre
-
I
n
st
a
l
l
a
t
i
o
n
P
o
st
-
I
n
st
a
l
l
a
t
i
o
n
u
si
n
g
C
M
I
EP
L
o
ss
D
G
P
V
D
G
P
V
L
o
ss
%
L
o
ss
r
e
d
u
c
t
i
o
n
Q
d29
(
M
V
A
R
)
(
M
W
)
L
o
c
a
t
i
o
n
S
i
z
e
(
M
W
)
(
M
W
)
10
1
8
.
1
2
21
7
5
9
.
6
8
6
4
.
0
1
6
.
0
0
6
6
.
8
7
20
1
9
.
3
9
21
7
5
6
.
3
7
6
1
.
6
6
7
.
1
9
6
2
.
9
1
30
2
2
.
4
4
21
7
6
0
.
6
5
5
0
.
5
6
1
0
.
1
0
5
5
.
0
0
T
h
e
r
esu
lt
s
o
f
m
i
n
i
m
izatio
n
o
f
F
V
S
I
a
n
d
m
in
i
m
izatio
n
o
f
l
o
s
s
es
f
o
r
C
MI
E
P
an
d
E
P
ar
e
co
m
p
ar
ed
w
it
h
t
h
e
p
r
e
-
i
n
s
ta
llatio
n
v
al
u
e
an
d
s
h
o
w
n
i
n
Fi
g
u
r
e
4
.
I
n
Fi
g
u
r
e
4
(
a)
,
b
o
th
C
MI
E
P
an
d
E
P
ar
e
co
m
p
ar
ab
le
at
lo
ad
v
ar
iatio
n
o
f
1
0
MV
A
R
a
n
d
2
0
MV
A
R
.
Ho
w
e
v
er
,
at
lo
ad
v
ar
iatio
n
o
f
3
0
MV
A
R
,
C
MI
E
P
o
u
tp
er
f
o
r
m
ed
E
P
w
it
h
4
%
d
if
f
er
e
n
ce
o
f
p
o
s
t
-
i
n
s
tal
latio
n
lo
s
s
r
ed
u
ctio
n
.
I
n
Fig
u
r
e
4
(
b
)
,
it
is
clea
r
to
m
en
tio
n
t
h
at
C
MI
E
P
o
u
tp
er
f
o
r
m
ed
E
P
in
all
ca
s
es t
o
d
eter
m
i
n
e
th
e
o
p
ti
m
al
lo
ca
ti
o
n
an
d
s
ize
o
f
DGP
V
f
o
r
F
V
S
I
r
ed
u
ctio
n
.
(
a)
(
b
)
Fig
u
r
e
4
: (
a)
L
o
s
s
(
b
)
FVS
I
w
i
th
L
o
ad
Var
iatio
n
Su
b
j
ec
ted
to
B
u
s
2
9
5.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
p
r
o
p
o
s
ed
a
m
o
d
if
i
ed
v
er
s
io
n
o
f
a
p
r
e
-
d
ev
elo
p
ed
C
h
ao
tic
Mu
ta
tio
n
I
m
m
u
n
e
E
v
o
lu
tio
n
ar
y
P
r
o
g
r
am
m
i
n
g
(
C
MI
E
P
)
.
C
h
a
o
tic
lo
ca
l
s
ea
r
c
h
h
as
b
ee
n
a
d
d
ed
in
to
th
e
o
r
ig
i
n
al
C
MI
E
P
alg
o
r
ith
m
.
T
h
e
in
cl
u
s
io
n
o
f
c
h
ao
tic
lo
ca
l
s
ea
r
ch
m
a
n
a
g
ed
to
ac
h
ie
v
e
a
b
e
tter
o
p
ti
m
al
s
o
l
u
tio
n
f
o
r
lo
ca
tio
n
a
n
d
s
izi
n
g
o
f
DGP
V
in
th
e
tr
an
s
m
is
s
io
n
s
y
s
te
m
.
I
n
t
h
e
p
r
o
p
o
s
ed
p
lace
m
e
n
t
s
ch
e
m
e,
t
h
e
tr
an
s
m
i
s
s
io
n
l
o
s
s
a
n
d
F
V
S
I
v
al
u
e
ar
e
tr
ea
ted
as
th
e
f
itn
e
s
s
eq
u
atio
n
s
a
n
d
o
p
ti
m
ized
s
ep
a
r
ate
l
y
.
Fro
m
t
h
e
s
tu
d
y
,
th
e
o
p
ti
m
al
v
al
u
e
o
f
DGP
V
u
n
i
ts
f
o
r
d
if
f
er
en
t
lo
ad
in
g
lev
els
ar
e
ch
a
n
g
ed
a
s
lo
ad
ch
a
n
g
es.
T
h
e
r
esu
lt
s
also
r
ev
ea
led
t
h
at
t
h
e
u
tili
za
tio
n
o
f
DGP
V
in
to
t
h
e
tr
a
n
s
m
i
s
s
io
n
s
y
s
te
m
r
ed
u
ce
s
th
e
to
tal
o
f
a
ctiv
e
p
o
w
er
lo
s
s
es
a
n
d
e
f
f
ec
t
iv
el
y
i
m
p
r
o
v
e
t
h
e
v
o
ltag
e
s
tab
ili
t
y
o
f
th
e
s
y
s
te
m
.
A
co
m
p
ar
ativ
e
s
t
u
d
y
,
als
o
s
h
o
w
ed
t
h
at
th
e
p
r
o
p
o
s
ed
C
MI
E
P
alg
o
r
it
h
m
o
u
tp
er
f
o
r
m
ed
E
P
ex
h
ib
ited
b
y
a
b
etter
r
ed
u
ctio
n
in
F
V
S
I
v
alu
e
s
a
n
d
lo
w
er
lo
s
s
v
alu
e
s
,
f
o
r
all
ca
s
e
s
.
I
n
t
h
e
f
u
tu
r
e,
C
MI
E
P
ca
n
b
e
u
s
ed
to
p
er
f
o
r
m
m
u
lti
-
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
ta
k
i
n
g
F
V
S
I
an
d
lo
s
s
es
as
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
.
ACK
NO
WL
E
D
G
E
M
E
NT
T
h
e
au
th
o
r
s
w
o
u
ld
li
k
e
to
a
ck
n
o
w
led
g
e
T
h
e
I
n
s
tit
u
te
o
f
R
esear
ch
Ma
n
a
g
e
m
en
t
a
n
d
I
n
n
o
v
a
tio
n
(
I
R
MI
)
UiT
M,
Sh
ah
Ala
m
,
Se
lan
g
o
r
,
Ma
la
y
s
ia
f
o
r
th
e
s
u
p
p
o
r
t
o
f
th
is
r
esear
c
h
.
T
h
is
r
esea
r
ch
is
s
u
p
p
o
r
ted
b
y
Min
i
s
tr
y
o
f
Hi
g
h
er
E
d
u
ca
tio
n
(
MO
HE
)
u
n
d
er
th
e
Fu
n
d
a
m
e
n
tal
R
esear
ch
Gr
an
t
Sch
e
m
e
(
F
R
GS)
w
it
h
p
r
o
j
ec
t
co
d
e:
6
0
0
-
R
MI
/F
R
GS 5
/3
(
0
1
0
2
/2
0
1
6
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
11
,
No
.
1
,
J
u
ly
2
0
1
8
:
1
1
3
–
120
120
RE
F
E
R
E
NC
E
S
[1
]
Brit
ish
P
e
tr
o
leu
m
,
“
BP
S
tatisti
c
a
l
Re
v
ie
w
o
f
W
o
rld
En
e
rg
y
,
”
2
0
1
7
.
[2
]
P
.
M
e
h
ta,
e
t
a
l.
,
“
Op
ti
m
a
l
se
l
e
c
ti
o
n
o
f
d
istri
b
u
ted
g
e
n
e
ra
ti
n
g
u
n
it
s
a
n
d
it
s
p
lac
e
m
e
n
t
f
o
r
v
o
lt
a
g
e
sta
b
il
it
y
e
n
h
a
n
c
e
m
e
n
t
a
n
d
e
n
e
rg
y
lo
ss
m
in
im
iz
a
ti
o
n
,
”
Ai
n
S
h
a
ms
En
g
in
e
e
ri
n
g
.
J
o
u
r
n
a
l
,
2
0
1
5
.
[3
]
S
.
G
.
Na
i
k
,
e
t
a
l.
,
“
Op
ti
m
a
l
a
ll
o
c
a
ti
o
n
o
f
c
o
m
b
in
e
d
DG
a
n
d
c
a
p
a
c
it
o
r
f
o
r
re
a
l
p
o
we
r
lo
ss
m
in
i
m
iza
ti
o
n
in
d
istri
b
u
ti
o
n
n
e
tw
o
rk
s,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
El
e
c
trica
l
P
o
we
r E
n
e
rg
y
S
y
ste
m
,
v
o
l.
53
,
p
p
.
9
6
7
-
9
7
3
,
2
0
1
3
.
[4
]
J.
O.
P
e
ti
n
r
in
a
n
d
M
.
S
h
a
a
b
a
n
b
,
“
Im
p
a
c
t
o
f
re
n
e
wa
b
le
g
e
n
e
ra
ti
o
n
o
n
v
o
lt
a
g
e
c
o
n
tro
l
in
d
istri
b
u
ti
o
n
sy
ste
m
s,
”
Ren
e
wa
b
le
S
u
st
a
in
a
b
le
En
e
rg
y
R
e
v
iew
,
v
o
l.
65
,
p
p
.
7
7
0
-
7
8
3
,
2
0
1
6
.
[5
]
Z.
A
.
Ka
m
a
ru
z
z
a
m
a
n
a
n
d
A
.
M
o
h
a
m
e
d
,
“
D
y
n
a
m
ic
v
o
lt
a
g
e
sta
b
il
it
y
;
p
o
w
e
r
d
istri
b
u
t
io
n
s
y
ste
m
;
g
ri
d
-
c
o
n
n
e
c
ted
P
V
s
y
ste
m
,
”
J
o
u
rn
a
l
El
e
c
trica
l
S
y
ste
m
,
v
o
l.
2
,
p
p
.
2
3
9
-
2
4
8
,
2
0
1
6
.
[6
]
J.
E.
C
.
Be
c
e
rr
a
a
n
d
H.
E.
H
.
Ria
ñ
o
,
“
L
o
c
a
ti
o
n
a
n
d
S
ize
o
f
D
istri
b
u
ted
G
e
n
e
ra
ti
o
n
to
Re
d
u
c
e
P
o
w
e
r
L
o
ss
e
s
u
sin
g
a
Ba
t
-
in
sp
ired
A
lg
o
rit
h
m
,
”
VII
S
im
p
o
sio
In
ter
n
a
c
io
n
a
l
so
b
re
C
a
li
d
a
d
d
e
l
a
E
n
e
rg
ía
El
é
c
trica
S
ICEL
,
2
1
0
3
.
[7
]
S
.
S
h
a
d
d
i
q
,
e
t
a
l.
,
“
Op
ti
m
a
l
Ca
p
a
c
it
y
a
n
d
P
lac
e
m
e
n
t
o
f
Distrib
u
t
e
d
G
e
n
e
ra
ti
o
n
Us
in
g
M
e
tah
e
u
rist
ic
Op
ti
m
iza
ti
o
n
A
l
g
o
rit
h
m
to
Re
d
u
c
e
P
o
w
e
r
L
o
s
se
s
in
Ba
n
tu
l
Distrib
u
ti
o
n
S
y
ste
m
,
Yo
g
y
a
k
a
rta
,
”
8
th
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
In
fo
rm
a
t
io
n
T
e
c
h
n
o
l
o
g
y
a
n
d
El
e
c
trica
l
En
g
i
n
e
e
rin
g
,
v
o
l.
7
,
p
p
.
2
-
6
,
2
0
1
6
.
[8
]
L
.
I.
Du
lău
,
e
t
a
l.
,
“
Op
ti
m
a
l
l
o
c
a
ti
o
n
o
f
a
Distrib
u
ted
G
e
n
e
ra
to
r
f
o
r
p
o
w
e
r
lo
ss
e
s
i
m
p
ro
v
e
m
e
n
t,
”
Pro
c
e
d
ia
T
e
c
h
n
o
l
o
g
y
,
v
o
l.
22
,
p
p
.
7
3
4
-
7
3
9
,
2
0
1
5
.
[9
]
P
.
M
e
h
ta,
e
t
a
l.
,
“
Op
ti
m
a
l
se
l
e
c
ti
o
n
o
f
d
istri
b
u
ted
g
e
n
e
ra
ti
n
g
u
n
it
s
a
n
d
it
s
p
lac
e
m
e
n
t
f
o
r
v
o
lt
a
g
e
sta
b
il
it
y
e
n
h
a
n
c
e
m
e
n
t
a
n
d
e
n
e
rg
y
lo
ss
m
in
im
iz
a
ti
o
n
,
”
Ai
n
S
h
a
ms
En
g
in
e
e
r
i
n
g
J
o
u
rn
a
l
,
A
in
S
h
a
m
s Un
iv
e
rsit
y
,
2
0
1
5
.
[1
0
]
J.
J.
Ja
m
ian
,
e
t
a
l.
,
“
Op
ti
m
u
m
m
u
lt
i
D
G
u
n
it
s
p
lac
e
m
e
n
t
a
n
d
siz
in
g
b
a
se
d
o
n
v
o
lt
a
g
e
sta
b
il
it
y
i
n
d
e
x
a
n
d
P
S
O
,
”
Pro
c
e
e
d
in
g
Un
ive
rs
it
y
Po
we
r E
n
g
in
e
e
rin
g
Co
n
fo
n
fer
e
n
c
e
,
2
0
1
2
.
[1
1
]
S
.
S
h
a
rm
a
a
n
d
A
.
R.
A
b
h
y
a
n
k
a
r,
“
L
o
ss
A
ll
o
c
a
ti
o
n
f
o
r
Wea
k
l
y
M
e
sh
e
d
Distrib
u
t
io
n
S
y
ste
m
U
s
in
g
A
n
a
l
y
ti
c
a
l
F
o
rm
u
latio
n
o
f
S
h
a
p
ley
V
a
lu
e
,
”
I
EE
E
T
ra
n
sa
c
ti
o
n
o
n
Po
we
r
S
y
ste
m
,
v
o
l/
issu
e
:
3
2
(
2
)
,
p
p
.
1
3
6
9
-
1
3
7
7
,
2
0
1
7
.
[1
2
]
P
.
Ku
m
a
wa
t,
e
t
a
l.
,
“
A
n
a
n
a
l
y
ti
c
a
l
a
p
p
ro
a
c
h
f
o
r
o
p
ti
m
a
l
a
ll
o
c
a
ti
o
n
o
f
D
G
u
n
it
in
d
istri
b
u
t
io
n
s
y
st
e
m
,
”
IEE
E
7
th
Po
we
r In
d
ia
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
(
PII
CON)
,
2
016
.
[1
3
]
A
.
Eh
sa
n
a
n
d
Q.
Ya
n
g
,
“
Op
ti
m
a
l
in
teg
ra
ti
o
n
a
n
d
p
lan
n
in
g
o
f
re
n
e
w
a
b
le
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
in
th
e
p
o
w
e
r
d
istri
b
u
ti
o
n
n
e
tw
o
rk
s: A
re
v
ie
w
o
f
a
n
a
l
y
ti
c
a
l
tec
h
n
iq
u
e
s,”
Ap
p
li
e
d
En
e
rg
y
,
v
o
l.
2
1
0
,
p
p
.
44
-
59
,
2
0
1
7
.
[1
4
]
G
.
G
.
W
a
n
g
,
e
t
a
l.
,
“
Ch
a
o
ti
c
Kril
l
He
rd
a
lg
o
rit
h
m
,
”
In
fo
rm
a
ti
o
n
S
c
ien
c
e
s
,
v
o
l.
2
7
4
,
p
p
.
17
-
34
,
2
0
1
4
.
[1
5
]
D.
Jia
,
e
t
a
l.
,
“
A
n
e
ff
e
c
ti
v
e
m
e
m
e
ti
c
d
iff
e
r
e
n
ti
a
l
e
v
o
lu
ti
o
n
a
lg
o
rit
h
m
b
a
s
e
d
o
n
c
h
a
o
ti
c
lo
c
a
l
se
a
rc
h
,
”
In
fo
rm
a
ti
o
n
S
c
ien
c
e
s
,
v
o
l/
issu
e
:
1
8
1
(1
5
)
,
p
p
.
3
1
7
5
-
3
1
8
7
,
2
0
1
1
.
[1
6
]
G
.
Ka
u
r
a
n
d
S
.
A
ro
ra
,
“
Ch
a
o
ti
c
W
h
a
le
Op
ti
m
iz
a
ti
o
n
A
lg
o
rit
h
m
,
”
J
o
u
rn
a
l
o
f
Co
m
p
u
t
a
ti
o
n
a
l
De
sig
n
a
n
d
En
g
i
n
e
e
rin
g
,
2
0
1
8
.
[1
7
]
V
.
P
.
S
a
k
th
iv
e
l
a
n
d
S
.
V
V
ij
a
y
a
s
u
n
d
a
ra
m
,
“
Ch
a
o
ti
c
P
a
rti
c
le
S
w
a
rm
Op
ti
m
iz
a
ti
o
n
f
o
r
Co
n
g
e
stio
n
M
a
n
a
g
e
m
e
n
t
in
a
n
El
e
c
tri
c
it
y
M
a
rk
e
t,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
S
c
ien
ti
fi
c
a
n
d
Res
e
a
rc
h
Pu
b
li
c
a
ti
o
n
s,
v
o
l/
issu
e
:
4
(
6
)
,
p
p
.
1
-
6
,
2
0
1
4
.
[1
8
]
M
.
K.
M
.
Zam
a
n
i,
e
t
a
l.
,
“
Ch
a
o
s
e
m
b
e
d
d
e
d
s
y
m
b
io
ti
c
o
rg
a
n
ism
s
se
a
r
c
h
tec
h
n
iq
u
e
f
o
r
o
p
ti
m
a
l
F
A
C
T
S
d
e
v
ice
a
ll
o
c
a
ti
o
n
f
o
r
v
o
l
tag
e
p
ro
f
il
e
a
n
d
se
c
u
rit
y
i
m
p
ro
v
e
m
e
n
t,
”
In
d
o
n
e
si
a
n
J
o
u
rn
a
l
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
(
IJ
EE
CS
)
,
v
o
l.
8
,
p
p
.
1
4
6
-
1
5
3
,
2
0
1
7
.
[1
9
]
Z.
G
u
o
,
e
t
a
l.
,
“
A
n
e
n
h
a
n
c
e
d
d
if
fe
re
n
ti
a
l
e
v
o
lu
ti
o
n
w
it
h
e
li
te
c
h
a
o
ti
c
lo
c
a
l
se
a
rc
h
,
”
Co
mp
u
t
e
r
In
telli
g
e
n
c
e
Ne
u
ro
sc
ien
c
e
,
2
0
1
6
.
[2
0
]
P
.
L
u
,
e
t
a
l
.
,
“
C
h
a
o
ti
c
d
if
f
e
re
n
ti
a
l
b
e
e
c
o
l
o
n
y
o
p
t
im
iza
ti
o
n
a
lg
o
rit
h
m
f
o
r
d
y
n
a
m
ic
e
c
o
n
o
m
ic
d
isp
a
tch
p
ro
b
lem
w
it
h
v
a
lv
e
-
p
o
in
t
e
ff
e
c
ts,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
El
e
c
trica
l
P
o
we
r E
n
e
rg
y
S
y
ste
m,
v
o
l.
62
,
p
p
.
1
3
0
-
1
4
3
,
2
0
1
4
.
[2
1
]
I.
M
u
siri
n
a
n
d
T
.
Ra
h
m
a
n
,
“
No
v
e
l
fa
st
v
o
lt
a
g
e
st
a
b
il
it
y
in
d
e
x
(F
V
S
I)
f
o
r
v
o
lt
a
g
e
sta
b
il
it
y
a
n
a
ly
sis
in
p
o
w
e
r
tran
sm
issio
n
s
y
ste
m
,
”
S
tu
d
e
n
t
C
o
n
fer
e
n
c
e
o
n
Res
e
a
rc
h
a
n
d
De
v
e
lo
p
me
n
t
Pro
c
e
e
d
i
n
g
,
p
p
.
2
6
5
-
2
6
8
,
2
0
0
2
.
[2
2
]
Y.
F
.
W
a
n
g
,
e
t
a
l.
,
“
Re
se
a
rc
h
o
n
a
p
iec
e
w
ise
li
n
e
a
r
c
h
a
o
ti
c
m
a
p
a
n
d
it
s
c
ry
p
to
g
ra
p
h
ica
l
a
p
p
li
c
a
ti
o
n
,
”
Pro
c
e
e
d
in
g
s
-
Fo
u
rt
h
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
F
u
zz
y
S
y
ste
ms
a
n
d
Kn
o
wled
g
e
Disc
o
v
e
ry
,
v
o
l.
4
,
p
p
.
2
6
0
-
2
6
3
,
2
0
0
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.