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I
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tab
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DGs
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w
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k
[
1
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.
I
n
[
2
]
,
th
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DGs
ar
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d
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in
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p
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ter
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m
,
i
s
an
o
t
h
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d
ef
in
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ti
o
n
o
f
DGs [
3
,
4
]
.
DG
u
n
its
g
e
n
er
ate
p
o
w
er
clo
s
er
to
th
e
lo
ad
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n
ter
s
,
th
u
s
a
v
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th
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t
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g
y
tr
an
s
p
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r
tatio
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an
d
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p
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w
er
lo
s
s
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s
i
n
tr
an
s
m
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li
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es.
F
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th
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D
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tech
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p
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to
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n
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tatio
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[
5
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.
No
r
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aller
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a
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elatio
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w
it
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d
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n
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w
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[
6
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.
Hen
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ir
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s
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DG
s
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an
d
t
h
e
i
n
te
g
r
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g
lo
ca
tio
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[
7
,
8
]
.
Ho
w
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n
o
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p
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m
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p
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y
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m
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.
B
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p
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s
,
D
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h
a
v
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s
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f
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to
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r
s
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t
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s
.
T
y
p
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DG
s
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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4
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J
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C
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m
p
Sci,
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2
0
2
1
:
64
7
-
65
6
6
48
in
j
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t
ac
tiv
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p
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d
t
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DGs
in
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s
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t,
it
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r
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p
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r
s
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p
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t
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a
n
e
s
s
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eq
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im
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p
tab
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lim
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ts
m
in
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p
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d
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g
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s
s
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Ma
n
y
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ch
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s
h
a
v
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s
t
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d
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n
o
p
ti
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al
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p
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t
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s
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d
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f
f
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n
t
m
et
h
o
d
s
[
9
,
1
2
]
.
Ho
w
e
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th
e
ab
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n
j
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tin
g
b
o
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p
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r
k
p
o
w
er
lo
s
s
by
i
n
te
g
r
atio
n
o
f
DGs.
T
h
ese
ap
p
r
o
ac
h
es
ca
n
b
e
m
ai
n
l
y
ca
teg
o
r
ized
as
cla
s
s
ical
a
n
d
ar
ti
f
icial
I
n
telli
g
en
t
alg
o
r
it
h
m
s
[
1
3
]
.
A
co
m
p
ar
ati
v
e
s
t
u
d
y
f
o
r
DG
a
llo
ca
tio
n
tech
n
iq
u
es
b
ased
o
n
ac
tiv
e
p
o
w
er
a
n
d
r
ea
ctiv
e
p
o
w
er
i
n
d
ices
a
n
d
v
o
lta
g
e
lo
s
s
r
ed
u
c
tio
n
h
a
s
b
ee
n
ad
d
r
ess
ed
i
n
[
1
4
]
.
I
n
[
1
5
]
,
a
n
o
n
li
n
ea
r
p
r
o
g
r
am
m
i
n
g
(
N
L
P
)
m
u
lti
o
b
j
ec
tiv
e
f
r
a
m
e
w
o
r
k
h
a
s
b
ee
n
p
r
o
p
o
s
ed
f
o
r
th
e
p
er
f
ec
t
s
i
tti
n
g
an
d
s
izi
n
g
o
f
DG
u
n
i
ts
.
M
i
n
i
m
izi
n
g
t
h
e
n
u
m
b
e
r
o
f
DGs
an
d
p
o
w
er
lo
s
s
es
to
g
eth
er
w
it
h
m
ax
i
m
izin
g
t
h
e
v
o
ltag
e
s
tab
ilit
y
m
ar
g
i
n
ar
e
th
e
o
b
j
ec
tiv
es
o
f
th
is
ap
p
r
o
ac
h
.
An
i
m
p
r
o
v
ed
an
al
y
t
ical
m
et
h
o
d
h
as
b
ee
n
p
r
esen
ted
in
[
1
6
]
f
o
cu
s
in
g
o
n
t
h
e
id
en
ti
f
icatio
n
o
f
t
h
e
b
est
lo
ca
tio
n
o
f
i
n
te
g
r
atio
n
.
B
u
t
m
o
s
t
o
f
t
h
e
a
n
al
y
tical
m
et
h
o
d
s
h
a
v
e
b
ee
n
an
t
iq
u
a
ted
d
u
e
to
m
o
r
e
tim
e
co
n
s
u
m
p
tio
n
a
n
d
th
e
les
s
a
cc
u
r
ac
y
.
Gen
etic
a
l
g
o
r
ith
m
s
[
1
7
]
,
Har
m
o
n
y
s
ea
r
c
h
[
1
8
]
,
p
ar
ticle
s
w
ar
m
o
p
ti
m
izat
io
n
(
P
SO)
[
1
9
-
21
],
an
d
T
ab
u
s
ea
r
ch
[
2
2
]
ar
e
s
o
m
e
o
f
t
h
e
ar
ti
f
icia
l
i
n
telli
g
e
n
ce
te
ch
n
iq
u
es,
th
at
h
a
v
e
b
ee
n
u
s
e
d
to
d
eter
m
in
e
t
h
e
o
p
tim
a
l
lo
ca
tio
n
a
n
d
t
h
e
s
iz
e
o
f
th
e
d
is
tr
ib
u
ted
g
en
er
ato
r
s
.
T
h
e
m
ain
f
ea
t
u
r
e
o
f
th
e
p
o
p
u
lar
it
y
o
f
t
h
es
e
tech
n
iq
u
es
is
t
h
e
co
m
p
u
tatio
n
al
r
o
b
u
s
tn
e
s
s
.
R
ef
er
e
n
ce
[
2
3
]
h
as
p
r
ese
n
ted
a
DG
p
lace
m
en
t
an
d
s
izi
n
g
m
et
h
o
d
co
n
s
id
er
in
g
r
ed
u
ctio
n
o
f
s
y
s
te
m
lo
s
s
es,
v
o
lta
g
e
m
a
g
n
i
tu
d
e
an
d
s
tab
ilit
y
e
n
h
a
n
ce
m
en
t.
I
n
[
2
4
]
,
a
n
ew
r
o
b
u
s
t
p
o
w
er
f
lo
w
m
et
h
o
d
w
it
h
w
h
a
le
o
p
ti
m
izatio
n
h
a
s
b
ee
n
p
r
o
p
o
s
ed
f
o
r
DG
p
lace
m
e
n
t
a
n
d
s
izin
g
.
Mo
s
t
o
f
th
e
r
esear
ch
w
o
r
k
r
elate
d
to
o
p
ti
m
al
p
lac
e
m
e
n
t
a
n
d
s
izi
n
g
o
f
DGs
u
s
in
g
P
SO
tech
n
iq
u
es
d
is
clo
s
e
a
lo
w
p
er
ce
n
tag
e
o
f
lo
s
s
r
ed
u
ctio
n
.
T
h
e
u
s
ag
e
o
f
u
n
-
t
u
n
ed
P
SO
p
ar
a
m
eter
s
is
t
h
e
p
r
in
cip
al
ca
u
s
e
f
o
r
th
at
p
o
o
r
lo
s
s
r
ed
u
ctio
n
.
P
ar
a
m
eter
s
elec
ti
o
n
co
u
ld
b
e
id
en
ti
f
ied
a
s
t
h
e
k
e
y
i
n
f
lu
e
n
ce
o
f
th
e
p
r
o
d
u
ctiv
it
y
a
n
d
t
h
e
p
er
f
o
r
m
a
n
ce
.
I
n
th
is
p
ap
er
,
a
f
i
n
e
-
tu
n
ed
p
a
r
ticle
s
w
ar
m
o
p
ti
m
izat
io
n
ap
p
r
o
ac
h
an
d
v
o
lta
g
e
s
tab
ilit
y
i
n
d
ex
(
VSI
)
ha
ve
b
ee
n
u
s
ed
to
d
eter
m
i
n
e
th
e
o
p
ti
m
a
l
s
ize
a
n
d
lo
ca
tio
n
o
f
th
e
DGs
to
m
i
n
i
m
ize
t
h
e
p
o
w
er
lo
s
s
es
w
h
ile
m
ai
n
tai
n
in
g
t
h
e
v
o
ltag
e
p
r
o
f
il
e
an
d
s
tab
ilit
y
m
ar
g
i
n
.
T
h
e
alg
o
r
ith
m
p
ar
a
m
eter
s
o
f
P
SO
h
av
e
b
ee
n
s
elec
ted
to
o
b
tain
th
e
m
i
n
i
m
u
m
lo
s
s
r
ed
u
ctio
n
.
Mo
s
t
o
f
t
h
e
ap
p
r
o
ac
h
es
p
r
esen
ted
s
o
f
ar
h
av
e
b
ee
n
u
tili
ze
d
o
n
l
y
t
y
p
e
I
DGs
to
th
e
n
et
w
o
r
k
to
d
eter
m
i
n
e
th
e
o
p
ti
m
al
s
ize
a
n
d
th
e
lo
ca
tio
n
.
I
n
th
e
c
u
r
r
en
t
w
o
r
k
,
th
e
ca
p
ab
ilit
y
o
f
i
m
p
r
o
v
i
n
g
t
h
e
p
o
w
er
lo
s
s
r
ed
u
ctio
n
a
n
d
t
h
e
v
o
ltag
e
s
tab
il
it
y
h
av
e
b
ee
n
in
v
es
tig
a
ted
b
y
in
te
g
r
atin
g
b
o
th
t
y
p
e
I
an
d
t
y
p
e
I
I
DG
s
to
t
h
e
n
e
t
wo
r
k
s
y
s
te
m
s
.
T
h
e
e
f
f
ec
tiv
e
n
e
s
s
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
is
d
e
m
o
n
s
tr
ated
o
n
s
tan
d
ar
d
I
E
E
E
3
3
b
u
s
,
I
E
E
E
6
9
b
u
s
an
d
a
r
ea
l
Ma
lay
s
ia
5
4
b
u
s
n
et
w
o
r
k
s
y
s
te
m
.
T
h
e
in
teg
r
atio
n
o
f
t
y
p
e
I
I
DGs is
s
u
g
g
ested
to
i
m
p
r
o
v
e
t
h
e
r
ed
u
ctio
n
o
f
p
o
w
er
lo
s
s
a
n
d
th
e
v
o
ltag
e
s
tab
ilit
y
o
f
t
h
e
s
y
s
te
m
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
P
r
o
ble
m
f
o
r
m
ula
t
io
n
2
.
1
.
1.
O
bje
ct
iv
e
f
un
ct
io
n
T
h
e
m
ain
o
b
j
ec
tiv
e
o
f
allo
ca
tin
g
DG
s
i
n
a
d
i
s
tr
ib
u
tio
n
n
et
w
o
r
k
is
to
g
et
t
h
e
m
ax
i
m
u
m
f
ea
s
ib
le
b
en
ef
it
s
b
y
e
n
h
a
n
ci
n
g
t
h
e
s
y
s
te
m
’
s
ef
f
icie
n
c
y
i
n
ter
m
s
o
f
i
m
p
r
o
v
in
g
th
e
p
o
w
er
lo
s
s
r
ed
u
ctio
n
.
T
h
e
p
r
o
b
le
m
co
u
ld
b
e
m
at
h
e
m
atica
l
l
y
f
o
r
m
u
lated
as a
n
o
b
j
ec
ti
v
e
o
f
m
in
i
m
izi
n
g
t
h
e
lo
s
s
o
f
r
ea
l p
o
w
er
.
∑
∑
(
1
)
w
h
er
e
,
an
d
ar
e
th
e
b
r
an
ch
c
u
r
r
en
t
,
t
h
e
b
r
an
ch
r
es
is
tan
ce
a
n
d
n
u
m
b
er
o
f
b
r
an
ch
e
s
r
esp
ec
tiv
el
y
.
2
.
1
.
2
.
Co
ns
t
ra
ints
a)
Vo
ltag
e
C
o
n
s
tr
ai
n
ts
A
b
s
o
l
u
te
v
al
u
e
o
f
t
h
e
v
o
lta
g
e
m
a
g
n
i
tu
d
e
at
ea
c
h
n
o
d
e
m
u
s
t
b
e
s
tatio
n
ed
w
i
th
in
th
eir
allo
w
ab
le
r
an
g
e
s
in
o
r
d
er
to
m
ai
n
tai
n
t
h
e
s
y
s
te
m
’
s
p
o
w
er
q
u
alit
y
.
I
t is
d
ef
in
ed
as b
elo
w
.
|
|
|
|
(
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
N
etw
o
r
k
lo
s
s
r
ed
u
ctio
n
a
n
d
vo
lta
g
e
imp
r
o
ve
men
t b
y
o
p
tima
l
p
la
ce
men
t a
n
d
s
iz
in
g
o
f
.. (
E
s
h
a
n
K
a
r
u
n
a
r
a
th
n
e
)
649
b)
DG
ca
p
ac
it
y
co
n
s
tr
ain
t
s
T
o
tal
c
o
n
n
ec
ted
DG
u
n
it
s
’
ac
tiv
e
an
d
r
ea
cti
v
e
p
o
w
er
g
en
er
atio
n
m
u
s
t
b
e
lo
w
er
t
h
a
n
th
e
b
ase
s
y
s
te
m
’
s
ac
ti
v
e
an
d
r
ea
cti
v
e
p
o
w
er
lo
ad
s
.
Fu
r
t
h
er
m
o
r
e
,
it
s
h
o
u
ld
b
e
lo
w
er
t
h
an
th
e
DG’
s
m
a
x
i
m
u
m
g
en
er
atio
n
ca
p
ab
ilit
y
.
Ma
t
h
e
m
atica
ll
y
,
t
h
is
co
n
s
tr
ai
n
t
w
as d
e
f
i
n
ed
as f
o
llo
w
s
:
(
3
)
(
4
)
Ass
u
m
in
g
,
,
)
)
,
w
h
er
e
is
th
e
p
o
w
er
f
ac
to
r
o
f
DG
u
n
it,
t
h
e
g
e
n
er
ated
r
ea
ctiv
e
p
o
w
er
ca
n
b
e
ex
p
r
ess
ed
as
:
(
5
)
Fo
r
ty
p
e
I
DGs
,
an
d
f
o
r
t
y
p
e
II
DGs
,
.
T
h
e
in
j
ec
ted
r
ea
ctiv
e
p
o
w
e
r
at
b
u
s
is
:
(
6
)
w
h
er
e
is
t
h
e
n
et
r
ea
cti
v
e
p
o
w
e
r
d
em
a
n
d
at
b
u
s
.
T
h
e
th
er
m
al
li
m
it
m
u
s
t
n
o
t e
x
ce
ed
its
l
i
m
it
s
.
(
7
)
2
.
2
.
P
a
rt
icle
s
wa
r
m
o
pti
m
iz
a
t
io
n (
P
SO
)
P
SO
alg
o
r
ith
m
i
s
o
n
e
o
f
th
e
ev
o
lu
tio
n
ar
y
co
m
p
u
tat
io
n
tech
n
iq
u
es
t
h
at
o
p
ti
m
izes
a
n
o
b
j
ec
tiv
e
f
u
n
ctio
n
b
y
iter
ati
v
el
y
at
te
m
p
tin
g
to
i
m
p
r
o
v
e
a
s
o
l
u
tio
n
b
y
g
i
v
i
n
g
co
n
s
id
er
atio
n
s
to
p
r
ed
ef
in
ed
m
ea
s
u
r
e
o
f
q
u
alit
y
.
I
n
t
h
i
s
r
esear
c
h
w
o
r
k
,
P
SO
alg
o
r
ith
m
h
a
s
b
ee
n
u
s
ed
to
estab
lis
h
t
h
e
o
p
ti
m
a
l
s
i
ze
o
f
t
h
e
DG
s
.
An
o
u
tlin
e
o
f
th
e
P
SO
w
it
h
s
tep
s
is
g
iv
e
n
b
elo
w
.
P
SO
al
g
o
r
it
h
m
i
s
a
p
o
p
u
latio
n
-
b
ased
s
ea
r
c
h
al
g
o
r
ith
m
o
r
ie
n
ted
o
n
th
e
s
i
m
u
latio
n
o
f
th
e
s
o
cial
b
eh
av
io
r
o
f
a
b
ir
d
s
’
f
lo
c
k
,
in
tr
o
d
u
ce
d
o
r
ig
in
all
y
b
y
Ke
n
n
ed
y
a
n
d
E
b
er
h
ar
t
i
n
1
9
9
5
[
2
5
]
.
T
h
e
n
u
m
b
er
o
f
p
ar
ticles
in
th
e
s
w
ar
m
r
ep
r
es
en
t
th
e
n
o
m
i
n
ee
s
o
lu
tio
n
s
.
E
ac
h
p
ar
ticle
is
a
r
ea
l
v
alu
ed
d
i
m
e
n
s
io
n
al
v
ec
to
r
w
h
er
e
i
s
t
h
e
n
u
m
b
er
o
f
p
ar
a
m
eter
s
o
p
ti
m
ized
.
C
o
n
s
eq
u
e
n
tl
y
,
e
v
er
y
o
p
tim
ized
p
ar
am
eter
r
ep
r
esen
t
s
a
d
i
m
en
s
io
n
o
f
th
e
p
r
o
b
le
m
s
p
ac
e.
Ste
p
1
:
I
n
s
er
t
th
e
d
ata
o
f
t
h
e
n
et
w
o
r
k
f
o
r
th
e
p
o
w
er
f
lo
w
s
i
m
u
latio
n
s
a
n
d
in
itia
lize
p
ar
a
m
eter
s
o
f
P
SO
alg
o
r
ith
m
(
i.e
.
n
u
m
b
er
o
f
iter
a
tio
n
s
,
n
u
m
b
er
o
f
p
ar
ticles,
s
o
cial
co
ef
f
icie
n
t
(C
2
)
,
co
g
n
iti
v
e
co
ef
f
icie
n
t
(C
1
)
,
m
i
n
i
m
u
m
a
n
d
m
a
x
i
m
u
m
li
m
it
s
o
f
in
er
tia
w
e
ig
h
t
)
Ste
p
2
:
C
o
n
s
tr
u
ct
r
an
d
o
m
l
y
i
n
itialized
s
w
ar
m
m
atr
ices
f
o
r
th
e
p
o
s
itio
n
a
n
d
v
elo
cit
y
an
d
r
u
n
t
h
e
b
ase
ca
s
e
p
o
w
er
f
lo
w
.
Ste
p
3
:
Use
f
o
r
w
ar
d
an
d
b
a
ck
w
ar
d
s
w
ee
p
m
eth
o
d
to
p
o
w
er
f
lo
w
s
i
m
u
latio
n
s
a
n
d
co
m
p
u
te
th
e
lo
s
s
o
f
ac
tiv
e
p
o
w
er
(
f
itn
e
s
s
f
u
n
ctio
n
)
u
s
i
n
g
(
1
)
,
th
e
n
o
d
al
v
o
lta
g
es,
an
d
th
e
f
lo
w
o
f
p
o
w
er
i
n
ea
ch
lin
e.
Ste
p 4
:
T
est o
n
t
h
e
n
et
w
o
r
k
c
o
n
s
tr
ain
ts
co
n
m
p
r
i
s
in
g
th
e
v
o
l
tag
es
o
f
th
e
n
o
d
es,
D
G
ca
p
ac
it
y
a
n
d
li
n
e
p
o
w
er
f
lo
w
s
w
h
ich
i
s
th
e
th
er
m
al
c
ap
ac
it
y
as
s
h
o
w
n
in
(
2
)
to
(
4
)
an
d
(
7
)
.
I
f
all
t
h
e
co
n
s
tr
ai
n
ts
ar
e
s
atis
f
ied
,
p
r
o
ce
ed
t
o
s
tep
6
; o
th
er
w
is
e
p
r
o
ce
ed
to
th
e
n
ex
t step
.
Ste
p 5
:
E
m
p
lo
y
t
h
e
p
en
al
t
y
f
u
n
ctio
n
m
et
h
o
d
(
P
FM)
f
o
r
th
e
DGs
w
h
ich
ar
e
i
n
b
r
ea
ch
o
f
th
e
co
n
s
tr
ain
t
s
.
Ste
p
6
:
I
d
en
tify
th
e
b
e
s
t
p
er
s
o
n
al
e
x
p
er
ien
ce
(
)
o
f
ea
ch
p
ar
ticle
an
d
t
h
e
b
est
g
l
o
b
al
ex
p
er
ien
ce
(
)
,
o
u
t o
f
e
v
er
y
p
ar
ticle
in
th
e
s
w
ar
m
.
Ste
p
7
:
Up
d
ate
ea
ch
p
ar
ticle’
s
p
o
s
itio
n
(
)
an
d
v
elo
cit
y
(
)
u
s
i
n
g
(
9
)
an
d
(
1
0
)
.
is
th
e
in
er
tia
co
n
s
ta
n
t
a
n
d
(
)
is
a
r
a
n
d
o
m
l
y
g
en
er
ated
n
u
m
b
er
∈
[
0
1
]
.
T
h
e
eq
u
atio
n
f
o
r
li
n
ea
r
l
y
in
cr
ea
s
i
n
g
in
er
t
ia
co
n
s
ta
n
t in
ea
c
h
iter
atio
n
i
s
s
h
o
w
n
in
(
8
)
.
)
(
8
)
)
)
)
)
(
9
)
(
1
0
)
2
.
3
.
Vo
l
t
a
g
e
s
t
a
bil
it
y
ind
ex
T
h
e
p
lace
m
en
t
o
f
th
e
DG
s
i
s
co
n
d
u
cted
b
y
r
an
d
o
m
l
y
c
h
o
o
s
in
g
t
h
e
p
o
s
itio
n
s
f
r
o
m
t
h
e
VSI
n
o
d
e
ar
r
ay
.
T
h
e
VSI
n
o
d
e
ar
r
ay
is
co
m
p
o
s
ed
o
f
th
e
n
o
d
es,
w
h
ic
h
h
a
v
e
a
n
i
n
d
ex
le
s
s
t
h
a
n
0
.
9
as
th
e
n
o
d
es
w
it
h
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I
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4
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p
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Vo
l.
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,
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2
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Feb
r
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m
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llap
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h
e
VSI
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f
o
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m
ed
u
tili
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g
tr
an
s
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ed
ac
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p
o
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e
n
d
v
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ltag
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s
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n
d
in
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ac
tiv
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p
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tiv
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2
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4
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M
et
ho
do
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h
e
f
i
n
e
-
t
u
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ed
P
SO
tec
h
n
iq
u
e
f
o
r
s
tan
d
ar
d
I
E
E
E
3
3
b
u
s
,
IEEE
69
b
u
s
a
n
d
a
r
ea
l
Ma
la
y
s
ia
5
4
b
u
s
n
et
w
o
r
k
s
w
er
e
i
m
p
le
m
e
n
ted
an
d
s
i
m
u
lated
o
n
M
A
T
L
AB
TM
s
i
m
u
latio
n
p
lat
f
o
r
m
.
T
h
e
Ma
la
y
s
i
a
5
4
b
u
s
n
et
w
o
r
k
is
s
h
o
w
n
in
F
ig
u
r
e
1
.
I
n
itiall
y
,
T
y
p
e
I
DGs
w
er
e
in
t
eg
r
ated
an
d
in
cr
ea
s
ed
u
p
to
th
r
ee
n
u
m
b
er
o
f
DG
s
an
d
r
ec
o
r
d
ed
th
e
r
esu
lts
.
T
h
en
T
y
p
e
I
I
DGs
w
it
h
a
P
F
o
f
0
.
9
w
er
e
i
n
te
g
r
ated
to
all
th
e
n
et
w
o
r
k
s
an
d
f
o
llo
w
ed
th
e
s
a
m
e
p
r
o
ce
d
u
r
e.
T
h
e
p
e
r
f
ec
t
s
o
lu
tio
n
f
o
r
th
e
p
lace
m
en
t
an
d
s
izin
g
in
ev
er
y
n
et
w
o
r
k
w
er
e
o
b
ta
in
ed
b
y
p
er
f
o
r
m
in
g
P
SO a
lg
o
r
it
h
m
w
it
h
th
e
p
o
p
u
latio
n
s
ize
o
f
3
0
.
Fig
u
r
e
1
.
Ma
la
y
s
ia
5
4
b
u
s
n
et
w
o
r
k
3.
RE
SU
L
T
S
A
ND
D
IS
CU
SS
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N
T
h
e
im
p
le
m
en
ted
r
o
u
tin
es
d
e
s
cr
ib
ed
u
n
d
er
m
eth
o
d
o
lo
g
y
s
ec
tio
n
w
er
e
s
i
m
u
lated
an
d
th
e
o
p
tim
al
lo
ca
tio
n
s
a
n
d
s
izes
o
f
DGs,
v
o
ltag
e
p
r
o
f
ile
s
,
r
ea
l
p
o
w
er
lo
s
s
d
ata
w
er
e
o
b
tain
ed
.
Fi
g
u
r
e
2
(
a)
,
Fig
u
r
e
2
(
c)
an
d
Fig
u
r
e
2
(
e)
p
r
esen
t
th
e
v
o
lta
g
e
p
r
o
f
iles
a
f
ter
t
y
p
e
I
DG
i
n
teg
r
at
io
n
f
o
r
I
E
E
E
3
3
b
u
s
,
IEEE
69
b
u
s
an
d
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la
y
s
ia
5
4
b
u
s
n
et
w
o
r
k
s
r
es
p
ec
tiv
el
y
co
n
s
id
er
in
g
t
h
e
u
n
it
y
p
o
w
er
f
ac
to
r
DGs.
F
ig
u
r
e
2
(
b
)
,
Fig
u
r
e
2
(
d
)
an
d
Fig
u
r
e
2
(
f
)
d
ep
ict
th
e
v
o
ltag
e
p
r
o
f
iles
af
ter
t
y
p
e
I
I
DG
i
n
teg
r
at
io
n
f
o
r
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E
E
E
3
3
b
u
s
,
IEEE
69
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u
s
an
d
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la
y
s
ia
5
4
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u
s
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et
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k
s
r
esp
ec
tiv
el
y
an
d
t
h
e
p
o
w
er
f
ac
t
o
r
o
f
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er
y
DG
is
d
e
f
i
n
ed
as
0
.
9
.
I
n
ea
ch
g
r
ap
h
u
n
d
er
Fi
g
u
r
e
2
,
th
e
b
ase
ca
s
e
w
ith
o
u
t
DG
s
,
o
n
e
DG,
t
w
o
DGs
a
n
d
t
h
r
ee
DGs
w
er
e
r
ep
r
esen
ted
b
y
b
l
u
e,
g
r
ee
n
,
r
ed
an
d
p
in
k
co
lo
u
r
li
n
es
r
esp
ec
ti
v
el
y
.
T
h
e
s
tat
u
to
r
y
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o
lta
g
e
li
m
it
s
o
f
1
.
0
5
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u
(
u
p
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er
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d
0
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5
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u
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p
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r
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ar
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ash
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e
s
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s
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o
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u
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3
s
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e
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n
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o
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et
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k
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n
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er
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et
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o
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ec
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n
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ad
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n
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n
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h
e
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g
o
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ith
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is
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i
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o
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r
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h
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o
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ith
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g
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r
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4
s
h
o
w
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e
v
o
lta
g
e
p
r
o
f
ile
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o
f
th
r
ee
DG
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o
b
tai
n
ed
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o
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e
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f
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0
.
9
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i
n
g
a
n
d
0
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9
lead
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g
p
o
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er
f
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e
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e
d
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e
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ty
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e
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V
DG
s
r
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ec
tiv
el
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.
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h
e
r
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lts
f
o
r
o
p
tim
al
s
iti
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g
a
n
d
s
izin
g
,
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o
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lo
s
s
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d
p
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o
s
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ctio
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n
ta
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e
f
o
r
e
ac
h
n
et
w
o
r
k
w
er
e
d
escr
ib
ed
in
T
a
b
le
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
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g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
N
etw
o
r
k
lo
s
s
r
ed
u
ctio
n
a
n
d
vo
lta
g
e
imp
r
o
ve
men
t b
y
o
p
tima
l
p
la
ce
men
t a
n
d
s
iz
in
g
o
f
.. (
E
s
h
a
n
K
a
r
u
n
a
r
a
th
n
e
)
651
3
.
1
.
I
E
E
E
3
3
bu
s
s
y
s
t
e
m
W
ith
a
to
tal
lo
ad
o
f
3
.
7
2
MW
an
d
2
.
3
0
Mv
ar
,
th
e
I
E
E
E
3
3
b
u
s
s
y
s
te
m
is
a
r
ad
ial
d
is
tr
ib
u
tio
n
n
et
w
o
r
k
.
T
h
e
o
v
er
all
ac
t
iv
e
p
o
w
er
lo
s
s
in
th
e
b
ase
ca
s
e
s
y
s
te
m
is
2
1
0
.
0
7
k
W
,
w
h
er
ea
s
t
o
tal
r
ea
ctiv
e
p
o
w
e
r
lo
s
s
i
s
1
4
2
.
3
3
7
k
v
ar
.
B
y
e
x
a
m
i
n
in
g
th
e
Fi
g
u
r
e
2
(
a)
,
it
w
a
s
o
b
s
er
v
ed
t
h
at,
t
h
e
b
ase
s
y
s
t
e
m
h
as
v
io
lated
th
e
lo
w
er
s
tatu
to
r
y
v
o
lta
g
e
l
i
m
it
at
t
w
o
in
ter
v
als
o
f
t
h
e
n
et
w
o
r
k
.
T
h
e
v
o
lta
g
e
p
r
o
f
iles
af
ter
ad
d
in
g
o
n
e,
t
w
o
an
d
th
r
ee
DGs
w
it
h
u
n
it
y
P
F
s
h
o
w
a
g
r
o
w
t
h
in
n
o
d
al
v
o
ltag
e
lev
els
o
f
b
ase
s
y
s
te
m
a
n
d
th
e
y
lie
i
n
s
id
e
th
e
allo
w
ab
le
b
o
u
n
d
ar
ies
e
x
ce
p
t
i
n
o
n
e
DG
s
ce
n
ar
i
o
.
T
h
e
s
i
n
g
le
DG
p
lace
m
e
n
t
h
as
y
ie
ld
ed
a
n
et
w
o
r
k
p
o
w
er
lo
s
s
r
ed
u
ctio
n
o
f
5
1
.
3
7
%,
an
d
it
h
as
i
n
cr
ea
s
ed
to
6
5
.
2
9
%
af
ter
th
e
p
lace
m
en
t
o
f
th
r
ee
DGs.
Ho
w
e
v
er
,
th
e
DG
s
w
it
h
0
.
9
P
F
h
a
v
e
r
ein
f
o
r
ce
d
th
e
all
v
o
lta
g
e
p
r
o
f
iles
h
i
g
h
e
r
th
an
th
e
lo
w
er
s
tat
u
to
r
y
li
m
it
a
n
d
t
h
er
e
is
a
n
i
m
p
r
o
v
e
m
en
t
in
v
o
lta
g
e
p
r
o
f
il
e
co
m
p
ar
ed
to
th
e
DG
i
n
teg
r
at
io
n
w
it
h
a
u
n
it
y
p
o
w
er
f
ac
to
r
.
I
t
co
u
ld
b
e
s
ee
n
as
s
h
o
w
n
i
n
Fi
g
u
r
e
2
(
b
)
.
T
h
e
m
a
x
i
m
u
m
p
o
w
er
lo
s
s
r
ed
u
ctio
n
ac
h
iev
ed
b
y
t
h
r
ee
DGs,
h
av
in
g
a
0
.
9
P
F
is
8
9
.
5
4
%
an
d
it
w
a
s
6
8
.
0
9
%
f
o
r
s
in
g
le
DG
an
d
8
3
.
6
9
%
f
o
r
t
w
o
DGs
.
T
h
e
DG
s
izes
w
er
e
v
ar
ied
f
r
o
m
0
.
7
MV
A
to
3
MV
A
f
o
r
b
o
th
t
y
p
e
o
f
DG
s
.
3
.
2
.
I
E
E
E
6
9
bu
s
s
y
s
t
e
m
T
h
e
I
E
E
E
6
9
b
u
s
s
y
s
te
m
h
a
s
co
n
n
ec
ted
to
a
to
tal
ac
ti
v
e
lo
ad
o
f
3
.
7
9
1
MW
an
d
a
r
ea
ctiv
e
lo
ad
o
f
2
.
6
9
4
Mv
ar
.
T
h
e
ac
tiv
e
p
o
w
e
r
lo
s
s
an
d
t
h
e
r
ea
ctiv
e
p
o
w
er
lo
s
s
w
it
h
o
u
t
in
te
g
r
ati
n
g
DGs
ar
e
2
3
8
.
1
4
k
W
an
d
1
0
6
.
7
6
k
v
ar
r
esp
ec
ti
v
el
y
.
B
y
r
ev
ie
w
i
n
g
Fig
u
r
e
2
(
c)
,
th
e
s
in
g
le
DG
w
it
h
u
n
it
y
P
F
h
a
s
co
n
tr
ib
u
ted
a
lo
s
s
r
ed
u
ctio
n
o
f
6
5
.
3
5
%.
Sim
ilar
l
y
,
6
9
.
0
7
%
an
d
6
9
.
7
2
%
ar
e
th
e
l
o
s
s
r
ed
u
ctio
n
s
ac
h
iev
ed
b
y
t
w
o
an
d
th
r
ee
DG
s
r
esp
ec
tiv
el
y
.
As
s
h
o
w
n
Fi
g
u
r
e
2
(
d
)
,
it
w
as
r
e
v
ea
led
th
at
a
co
n
s
id
er
ab
le
v
o
lta
g
e
i
m
p
r
o
v
e
m
e
n
t
f
o
r
t
h
e
s
e
g
m
e
n
t
af
ter
5
0
th
b
u
s
w
a
s
ac
h
ie
v
ed
b
y
i
n
j
ec
tin
g
r
ea
ctiv
e
p
o
w
er
i
n
o
n
e,
t
w
o
an
d
t
h
r
ee
DG
s
ce
n
ar
io
s
.
T
h
e
p
o
w
er
l
o
s
s
r
ed
u
ctio
n
f
o
r
s
i
n
g
le
DG
w
it
h
0
.
9
P
F
is
8
8
.
5
0
%
an
d
9
4
.
0
1
%
f
o
r
t
w
o
DGs
w
i
th
t
h
e
s
a
m
e
P
F.
Ma
x
i
m
u
m
lo
s
s
r
ed
u
ctio
n
p
er
ce
n
tag
e
w
as
r
ec
o
r
d
ed
w
ith
t
y
p
e
I
I
th
r
ee
DGs
an
d
it
is
9
4
.
9
5
%.
T
h
e
o
p
ti
m
al
DG
s
izes
w
er
e
v
ar
ied
f
r
o
m
0
.
5
MV
A
to
4
MV
A
f
o
r
b
o
th
t
y
p
e
o
f
DG
s
.
3
.
3
.
M
a
la
y
s
ia
5
4
bu
s
s
y
s
t
e
m
T
h
e
Ma
lay
s
ia
5
4
b
u
s
s
y
s
te
m
is
also
a
r
ad
ial
d
is
tr
ib
u
tio
n
n
et
w
o
r
k
w
i
th
a
to
tal
ac
ti
v
e
lo
ad
o
f
4
.
5
9
5
MW
an
d
r
ea
ctiv
e
lo
ad
o
f
2
.
2
9
8
Mv
ar
.
T
h
e
ac
tiv
e
an
d
r
ea
cti
v
e
p
o
w
er
lo
s
s
e
s
ar
e
3
3
8
.
4
6
k
W
an
d
2
4
2
.
2
8
k
v
ar
r
esp
ec
tiv
el
y
.
T
h
e
s
y
s
te
m
h
as
v
io
lated
th
e
lo
w
er
v
o
ltag
e
li
m
it
in
t
h
r
ee
s
ec
tio
n
s
.
As
ex
p
ec
ted
,
th
e
v
io
lated
v
o
ltag
e
n
o
d
es
h
av
e
r
i
s
en
u
p
th
eir
v
o
lta
g
e
m
a
g
n
itu
d
e
b
y
i
n
j
ec
tin
g
t
y
p
e
I
DGs
to
th
e
n
et
w
o
r
k
s
y
s
te
m
.
I
t
h
a
s
ac
h
iev
ed
7
2
.
2
6
%
f
r
o
m
o
n
e
D
G,
7
8
.
0
%
f
r
o
m
t
w
o
DGs
an
d
7
9
.
6
4
%
f
r
o
m
th
r
ee
DGs.
T
h
e
i
m
p
r
o
v
ed
v
ar
iat
io
n
s
in
n
o
d
al
v
o
lta
g
es c
o
m
p
ar
ed
to
th
e
b
ase
s
y
s
te
m
co
u
ld
b
e
s
ee
n
in
Fi
g
u
r
e
2
(
e)
.
T
h
e
Fig
u
r
e
2
(
f
)
s
h
o
w
s
h
o
w
t
h
e
n
o
d
al
v
o
lta
g
es
i
n
Ma
la
y
s
ia
n
et
w
o
r
k
ar
e
d
ev
iated
u
s
i
n
g
b
o
th
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
w
er
.
I
t h
a
s
s
i
g
n
if
ica
n
tl
y
i
m
p
r
o
v
ed
t
h
an
t
h
at
o
f
in
j
ec
tin
g
o
n
l
y
ac
tiv
e
p
o
w
er
an
d
co
u
ld
b
e
clea
r
l
y
o
b
s
er
v
ed
f
r
o
m
th
e
g
r
ap
h
s
.
T
h
e
lo
s
s
r
ed
u
ctio
n
h
a
s
ad
v
a
n
ce
d
u
p
to
8
6
.
5
3
%
b
y
ad
d
in
g
s
i
n
g
le
DG
w
i
th
0
.
9
P
F
an
d
it
w
as
a
n
in
cr
e
m
e
n
t
i
n
p
er
f
o
r
m
a
n
ce
th
a
n
th
r
ee
D
Gs
w
it
h
u
n
i
t
y
P
F.
Af
ter
p
laci
n
g
o
f
DG
s
at
p
er
f
ec
t
lo
ca
tio
n
s
a
n
d
s
izes
g
i
v
e
n
b
y
P
SO
alg
o
r
ith
m
,
t
h
e
n
et
w
o
r
k
h
as
atta
in
ed
a
m
a
x
i
m
u
m
p
o
wer
lo
s
s
r
ed
u
ctio
n
o
f
9
6
.
2
5
%
b
y
t
y
p
e
I
I
DGs.
T
h
e
ac
tiv
e
an
d
r
ea
cti
v
e
p
o
w
er
lo
s
s
es
i
n
all
th
e
n
et
w
o
r
k
s
ar
e
s
h
o
w
n
in
F
ig
u
r
e
3
.
A
s
p
r
esu
m
ed
,
it
d
e
m
o
n
s
tr
ates
t
h
e
r
ed
u
ctio
n
o
f
p
o
w
er
lo
s
s
e
s
w
i
th
th
e
n
u
m
b
er
o
f
DG
s
co
n
n
ec
ted
as
w
ell
as
t
h
e
t
y
p
e
o
f
th
e
D
G.
T
y
p
e
I
I
DGs (
w
it
h
0
.
9
P
F)
h
av
e
ex
h
ib
ited
th
e
m
ax
i
m
u
m
p
o
w
er
lo
s
s
r
ed
u
ct
io
n
.
Fig
u
r
e
4
s
h
o
w
s
th
e
g
ai
n
ed
lo
s
s
r
ed
u
ctio
n
o
f
t
y
p
e
I
I
DG
s
co
m
p
ar
ed
to
th
e
t
y
p
e
I
DGs
an
d
it
h
a
s
in
cr
ea
s
ed
b
et
w
ee
n
1
5
%
an
d
2
5
%.
T
h
e
v
ar
iatio
n
o
f
n
o
d
al
v
o
ltag
es
in
I
E
E
E
3
3
b
u
s
n
et
w
o
r
k
f
o
r
ev
er
y
t
y
p
e
o
f
DGs
w
er
e
s
h
o
w
n
i
n
Fi
g
u
r
e
5
.
T
h
e
least
g
r
o
w
t
h
in
v
o
ltag
e
c
o
u
ld
b
e
s
ee
n
b
y
t
y
p
e
I
I
I
DGs,
w
h
ich
i
n
j
ec
ts
o
n
l
y
r
ea
ctiv
e
p
o
w
er
.
T
h
e
n
e
x
t
en
h
an
ce
m
e
n
t
i
n
n
o
d
al
v
o
ltag
e
was
in
d
icate
d
b
y
t
y
p
e
I
V
DGs
an
d
it
in
j
ec
ts
ac
ti
v
e
p
o
w
er
an
d
ab
s
o
r
b
s
r
ea
ctiv
e
p
o
w
er
.
A
m
o
d
er
ate
i
n
cr
e
m
e
n
t
co
m
p
ar
ed
to
th
e
b
ase
s
y
s
te
m
w
as
d
is
p
la
y
ed
b
y
t
y
p
e
I
DGs.
T
h
e
y
o
n
l
y
i
n
j
ec
t
ac
tiv
e
p
o
w
er
.
T
y
p
e
I
I
DGs
h
av
e
ac
h
iev
ed
th
e
b
est
g
ai
n
i
n
n
o
d
al
v
o
ltag
e
s
b
y
in
j
ec
tin
g
b
o
th
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
w
er
to
th
e
b
ase
s
y
s
te
m
.
Nu
m
b
er
o
f
DG
s
w
er
e
r
etain
ed
at
th
r
ee
f
o
r
all
th
e
ca
s
es
d
escr
ib
ed
in
Fig
u
r
e
5
an
d
th
e
lead
in
g
a
n
d
lag
g
in
g
P
Fs
w
er
e
f
ix
ed
at
0
.
9
.
T
ab
le
2
s
h
o
w
s
t
h
e
co
m
p
ar
is
o
n
o
f
th
e
r
esu
lts
w
it
h
o
th
er
s
t
u
d
ie
s
u
n
d
er
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l
p
la
ce
men
t a
n
d
s
iz
in
g
o
f
.. (
E
s
h
a
n
K
a
r
u
n
a
r
a
th
n
e
)
655
[2
]
N.
Je
n
k
in
s,
J.
B.
Ek
a
n
a
y
a
k
e
,
a
n
d
G
.
S
trb
a
c
,
“
Distrib
u
ted
g
e
n
e
ra
ti
o
n
,
”
T
h
e
In
stit
u
t
io
n
o
f
E
n
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
,
2010.
[3
]
S
o
ro
u
d
i
,
A
li
re
z
a
a
n
d
M
.
E
h
sa
n
,
“
M
u
lt
i
-
o
b
jec
ti
v
e
p
lan
n
i
n
g
m
o
d
e
l
f
o
r
in
teg
ra
ti
o
n
o
f
d
i
strib
u
ted
g
e
n
e
ra
ti
o
n
s
i
n
d
e
re
g
u
late
d
p
o
w
e
r
sy
ste
m
s
,
”
Ira
n
ia
n
J
o
u
rn
a
l
o
f
S
c
ien
c
e
T
e
c
h
n
o
lo
g
y
,
v
o
l.
3
4
,
n
o
.
3
,
p
p
.
3
0
7
-
3
2
4
,
2
0
1
0
.
[4
]
D.
E.
Oliv
a
re
s,
C.
A
.
Ca
ñ
iza
re
s,
a
n
d
M
.
Ka
z
e
ra
n
i,
“
A
c
e
n
tralize
d
o
p
t
im
a
l
e
n
e
rg
y
m
a
n
a
g
e
m
e
n
t
sy
ste
m
f
o
r
m
icro
g
rid
s,”
IEE
E
Po
we
r a
n
d
E
n
e
rg
y
S
o
c
iety
Ge
n
e
ra
l
M
e
e
ti
n
g
,
p
p
.
1
-
6
,
2
0
1
1
.
[
5
]
W
i
l
l
i
s
,
H
.
L
e
e
,
S
c
o
t
t
,
W
a
l
t
e
r
G
.
,
“
D
i
s
t
r
i
b
u
t
e
d
P
o
w
e
r
G
e
n
e
r
a
t
i
o
n
:
P
l
a
n
n
i
n
g
a
n
d
E
v
a
l
u
a
t
i
o
n
,
”
C
R
C
P
r
e
s
s
,
1
st
e
d
i
t
i
o
n
,
2000.
[6
]
K.
D.
M
istry
a
n
d
R.
Ro
y
,
“
En
h
a
n
c
e
m
e
n
t
o
f
lo
a
d
in
g
c
a
p
a
c
it
y
o
f
d
istri
b
u
t
io
n
sy
ste
m
th
ro
u
g
h
d
istri
b
u
ted
g
e
n
e
ra
to
r
p
lac
e
m
e
n
t
c
o
n
sid
e
rin
g
tec
h
n
o
-
e
c
o
n
o
m
ic
b
e
n
e
f
it
s
w
it
h
lo
a
d
g
ro
w
th
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
Po
we
r
a
n
d
En
e
rg
y
S
y
ste
ms
,
v
o
l.
5
4
,
p
p
.
5
0
5
–
5
1
5
,
2
0
1
4
.
[7
]
R.
A
.
W
a
ll
in
g
,
R.
S
a
i
n
t,
R
.
C.
D
u
g
a
n
,
J.
B
u
rk
e
,
a
n
d
L
.
A
.
Ko
jo
v
i
c
,
“
S
u
m
m
a
r
y
o
f
d
istri
b
u
ted
re
so
u
rc
e
s
im
p
a
c
t
o
n
p
o
w
e
r
d
e
li
v
e
r
y
s
y
ste
m
s,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r De
li
v
e
ry
,
v
o
l.
2
3
,
n
o
.
3
,
p
p
.
1
6
3
6
-
1
6
4
4
,
2
0
0
8
.
[8
]
T
.
Ac
k
e
r
m
a
n
n
a
n
d
V.
Kn
y
a
z
k
i
n
,
“
In
t
e
ra
c
ti
o
n
b
e
tw
e
e
n
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
a
n
d
t
h
e
d
istr
ib
u
ti
o
n
n
e
tw
o
rk
:
Op
e
ra
ti
o
n
a
sp
e
c
ts,
”
IEE
E/
P
ES
T
ra
n
sm
issio
n
a
n
d
Distrib
u
ti
o
n
C
o
n
fer
e
n
c
e
a
n
d
Exh
ib
it
io
n
,
v
o
l
.
2
,
p
p
.
1
3
5
7
–
1
3
6
2
,
2
0
0
2
.
d
o
i:
1
0
.
1
1
0
9
/T
DC.2
0
0
2
.
1
1
7
7
6
7
7
.
[9
]
S
.
M
.
Ka
n
n
a
n
,
P
.
Re
n
u
g
a
,
S
.
Ka
l
y
a
n
i,
a
n
d
E.
M
u
t
h
u
k
u
m
a
ra
n
,
“
Op
ti
m
a
l
c
a
p
a
c
it
o
r
p
lac
e
m
e
n
t
a
n
d
siz
in
g
u
sin
g
f
u
z
z
y
-
DE
a
n
d
f
u
z
z
y
-
M
A
P
S
O m
e
t
h
o
d
s
,
”
Ap
p
li
e
d
S
o
ft
Co
m
p
u
t
in
g
J
o
u
rn
a
l
,
v
o
l.
1
1
,
n
o
.
8
,
p
p
.
4
9
9
7
-
5
0
0
5
,
2
0
1
1
.
[
1
0
]
V
.
T
a
m
i
l
s
e
l
v
a
n
,
T
.
J
a
y
a
b
a
r
a
t
h
i
,
T
.
R
a
g
h
u
n
a
t
h
a
n
,
a
n
d
X
.
S
.
Y
a
n
g
,
“
O
p
t
i
m
a
l
c
a
p
a
c
i
t
o
r
p
l
a
c
e
m
e
n
t
i
n
r
a
d
i
a
l
d
i
s
t
r
i
b
u
t
i
o
n
s
y
s
t
e
m
s
u
s
i
n
g
f
l
o
w
e
r
p
o
l
l
i
n
a
t
i
o
n
a
l
g
o
r
i
t
h
m
,
”
A
l
e
x
a
n
d
r
i
a
E
n
g
i
n
e
e
r
i
n
g
J
o
u
r
n
a
l
,
v
o
l
.
5
7
,
n
o
.
4
,
p
p
.
2
7
7
5
-
2
7
8
6
,
2
0
1
8
.
[1
1
]
A
.
El
sh
e
ik
h
,
Y.
He
lmy
,
Y.
A
b
o
u
e
lse
o
u
d
,
a
n
d
A
.
El
sh
e
rif
,
“
Op
ti
m
a
l
c
a
p
a
c
it
o
r
p
lac
e
m
e
n
t
a
n
d
siz
in
g
in
ra
d
ial
e
lec
tri
c
p
o
w
e
r
s
y
ste
m
s,
”
Al
e
x
a
n
d
ria
En
g
in
e
e
rin
g
J
o
u
rn
a
l
,
v
o
l.
5
3
,
n
o
.
4
,
p
p
.
8
0
9
-
8
1
6
,
2
0
1
4
.
[
1
2
]
A
.
S
a
d
i
g
h
m
a
n
e
s
h
,
K
.
Z
a
r
e
,
a
n
d
M
.
S
a
b
a
h
i
,
“
D
i
s
t
r
i
b
u
t
e
d
G
e
n
e
r
a
t
i
o
n
u
n
i
t
a
n
d
C
a
p
a
c
i
t
o
r
P
l
a
c
e
m
e
n
t
f
o
r
M
u
l
t
i
-
o
b
j
e
c
t
i
v
e
O
p
t
i
m
i
z
a
t
i
o
n
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
E
l
e
c
t
r
i
c
a
l
a
n
d
C
o
m
p
u
t
e
r
E
n
g
i
n
e
e
r
i
n
g
(
I
J
E
C
E
)
,
v
o
l
.
2
,
n
o
.
5
,
p
p
.
6
1
5
-
6
2
0
,
2012.
[1
3
]
M
.
P
e
sa
ra
n
H.A
,
P
.
D.
Hu
y
,
a
n
d
V
.
K.
Ra
m
a
c
h
a
n
d
a
ra
m
u
rth
y
,
“
A
re
v
ie
w
o
f
th
e
o
p
ti
m
a
l
a
ll
o
c
a
ti
o
n
o
f
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
:
Ob
jec
ti
v
e
s,
c
o
n
stra
in
ts,
m
e
th
o
d
s,
a
n
d
a
lg
o
rit
h
m
s,”
Re
n
e
wa
b
le
a
n
d
S
u
sta
i
n
a
b
le
En
e
rg
y
Rev
iews
,
v
o
l.
7
5
,
p
p
.
2
9
3
-
3
1
2
,
2
0
1
7
.
[1
4
]
J.
J.
Ja
m
ian
,
M
.
W
.
M
u
sta
f
a
,
M
.
M
.
Am
a
n
,
G
.
B.
J
a
s
m
o
n
,
H.
M
o
k
h
li
s,
a
n
d
A
.
H.
A
.
B
a
k
a
r,
“
Co
m
p
a
ra
ti
v
e
stu
d
y
o
n
o
p
ti
m
u
m
D
G
p
lac
e
m
e
n
t
f
o
r
d
istri
b
u
ti
o
n
n
e
tw
o
rk
,
”
Prze
g
la
d
El
e
k
tro
tec
h
n
icz
n
y
,
v
o
l.
8
9
,
n
o
.
3
A
,
p
p
.
1
99
-
2
0
5
,
2
0
1
3
.
[1
5
]
M
.
Esm
a
il
i,
“
P
lac
e
m
e
n
t
o
f
m
in
i
m
u
m
d
istri
b
u
te
d
g
e
n
e
ra
ti
o
n
u
n
i
ts
o
b
se
rv
in
g
p
o
w
e
r
lo
ss
e
s
a
n
d
v
o
lt
a
g
e
sta
b
il
it
y
w
it
h
n
e
tw
o
rk
c
o
n
stra
in
ts,
”
IET
Ge
n
e
r
a
ti
o
n
,
T
ra
n
sm
issio
n
a
n
d
Distri
b
u
ti
o
n
,
v
o
l.
7
,
n
o
.
8
,
p
p
.
8
1
3
-
8
2
1
,
2
0
1
3
.
[1
6
]
D.
Q.
Hu
n
g
a
n
d
N
.
M
it
h
u
la
n
a
n
th
a
n
,
“
M
u
lt
ip
le
d
istri
b
u
ted
g
e
n
e
ra
to
r
p
lac
e
m
e
n
t
in
p
rim
a
r
y
d
istri
b
u
ti
o
n
n
e
tw
o
rk
s
f
o
r
lo
ss
re
d
u
c
ti
o
n
,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s o
n
In
d
u
stri
a
l
E
lec
tro
n
ics
,
v
o
l.
6
0
,
n
o
.
4
,
p
p
.
1
7
0
0
-
1
7
0
8
,
2
0
1
3
.
[1
7
]
I.
P
isica
,
C.
Bu
lac
,
a
n
d
M
.
Erem
i
a
,
“
Op
ti
m
a
l
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
lo
c
a
ti
o
n
a
n
d
siz
in
g
u
si
n
g
g
e
n
e
ti
c
a
lg
o
rit
h
m
s,”
2
0
0
9
1
5
t
h
I
n
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
In
telli
g
e
n
t
S
y
ste
m A
p
p
li
c
a
ti
o
n
s t
o
P
o
we
r S
y
ste
ms
,
p
p
.
1
-
6
,
2
0
0
9
.
[1
8
]
P
.
Da
s,
“
Op
ti
m
a
l
a
ll
o
c
a
ti
o
n
o
f
d
istri
b
u
te
d
g
e
n
e
ra
ti
o
n
in
a
ra
d
ial
d
ist
rib
u
ti
o
n
sy
ste
m
u
sin
g
lo
ss
se
n
siti
v
i
t
y
fa
c
to
r
a
n
d
h
a
rm
o
n
y
s
e
a
rc
h
a
l
g
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
A
d
v
a
n
c
e
d
En
g
in
e
e
rin
g
Res
e
a
rc
h
a
n
d
S
c
ien
c
e
(
IJ
AE
RS
)
,
v
o
l.
2
,
n
o
.
4
,
p
p
.
6
-
1
4
,
2
0
1
5
.
[1
9
]
K.
Bh
u
m
k
it
ti
p
ich
a
n
d
W
.
P
h
u
a
n
g
p
o
r
n
p
it
a
k
,
“
Op
ti
m
a
l
p
lac
e
m
e
n
t
a
n
d
siz
in
g
o
f
d
istri
b
u
te
d
g
e
n
e
ra
ti
o
n
f
o
r
p
o
w
e
r
lo
ss
re
d
u
c
ti
o
n
u
si
n
g
p
a
rti
c
le sw
a
r
m
o
p
ti
m
iza
ti
o
n
,
”
En
e
rg
y
Pro
c
e
d
i
a
,
v
o
l
.
3
4
,
p
p
.
3
0
7
-
3
1
7
,
2
0
1
3
.
[2
0
]
J.
J.
Ja
m
ian
,
M
.
W
.
M
u
sta
f
a
,
H.
M
o
k
h
li
s,
a
n
d
M
.
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.
[2
1
]
J.
J.
Ja
m
ian
,
M
.
W
.
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u
sta
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M
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k
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li
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d
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.
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h
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ru
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,
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3
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4
]
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.
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o
.
1
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p
p
.
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.
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5
]
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Ke
n
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e
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y
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d
R.
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rt,
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6
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7
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.
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Ku
m
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ter
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.
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8
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I.
A
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o
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m
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d
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.
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,
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siti
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teria
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9
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D.
Ra
m
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P
ra
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T
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Ja
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m
,
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Ai
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.
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p
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683
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