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ly
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s
:
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er
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Dis
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w
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©
2
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1
9
In
stit
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f
A
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E
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Al
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rig
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C
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p
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A
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:
Kath
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C
ab
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a
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Dep
ar
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C
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p
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d
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Un
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id
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5
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5
5
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1.
I
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RO
D
UCT
I
O
N
T
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DG
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d
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n
n
et
w
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r
k
h
as
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cr
ea
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ed
r
ap
id
ly
[
1
]
,
s
in
ce
t
h
e
p
o
s
s
ib
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o
f
g
en
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ati
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v
in
g
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ilit
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a
n
d
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th
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s
[
2
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,
[
3
]
.
Ho
w
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lar
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ce
s
b
r
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s
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tab
ilit
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r
o
b
lem
s
[
4
]
,
[
5
]
.
Fo
r
th
is
r
ea
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o
n
,
th
e
v
o
ltag
e
s
tab
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n
d
h
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p
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r
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p
p
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r
t is a
m
aj
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r
c
h
allen
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e
[
6
]
–
[
8
]
.
A
cc
o
r
d
in
g
to
[
9
]
,
d
is
tr
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tio
n
n
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k
s
h
a
v
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o
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ilit
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o
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lem
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n
t
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o
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m
a
l
d
esig
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b
u
t
t
h
e
in
te
g
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atio
n
o
f
a
lar
g
e
n
u
m
b
er
o
f
DG
b
r
in
g
s
s
o
m
e
s
tab
il
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s
u
e
s
.
So
m
e
p
r
ev
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s
s
t
u
d
ies
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e
d
eter
m
i
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t
h
at
th
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co
n
n
ec
t
io
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o
f
DG
m
a
y
a
f
f
ec
t th
e
v
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e
s
tab
ili
t
y
in
d
i
s
t
r
ib
u
tio
n
s
y
s
te
m
s
[
1
0
]
,
[
9
]
.
I
n
[
1
1
]
,
th
e
r
esear
ch
er
s
p
r
esen
ted
th
e
p
ar
ticle
s
w
ar
m
o
p
tim
izatio
n
(
P
SO)
as
a
n
alte
r
n
ativ
e
to
o
p
ti
m
ize
a
p
o
w
er
in
j
ec
tio
n
m
o
d
el
f
r
o
m
DG,
w
it
h
t
h
e
ai
m
o
f
m
ax
i
m
iz
i
n
g
v
o
lta
g
e
s
tab
ilit
y
.
I
n
[
1
2
]
,
a
co
m
b
in
at
io
n
o
f
ev
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l
u
tio
n
ar
y
p
r
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g
r
am
m
i
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g
(
E
P
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an
d
P
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as
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ted
to
ac
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n
v
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ce
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d
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r
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in
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D
G
s
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d
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.
Fu
r
t
h
er
m
o
r
e,
th
e
a
u
t
h
o
r
s
i
n
[
1
3
]
h
av
e
p
r
o
p
o
s
ed
th
e
o
p
ti
m
al
p
lace
m
en
t,
s
ize
a
n
d
n
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m
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er
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d
if
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er
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t
t
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n
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n
d
is
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y
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m
s
co
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s
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t
h
e
v
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g
e
li
m
it
s
an
d
th
e
li
n
e
s
’
tr
a
n
s
f
er
ca
p
ac
ities
,
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s
i
n
g
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
9
:
5
5
-
65
56
g
en
et
ic
alg
o
r
it
h
m
(
G
A
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as
a
n
o
p
ti
m
izatio
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tech
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iq
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e
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ac
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(
B
FS
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.
T
h
e
au
th
o
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s
i
n
[
1
4
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in
v
e
s
ti
g
at
ed
th
e
ev
al
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atio
n
o
f
s
tatic
v
o
lt
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s
tab
ili
t
y
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n
I
E
E
E
3
3
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b
u
s
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P
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6
9
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u
s
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w
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n
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r
e,
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tu
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ies
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r
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ted
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u
s
o
n
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m
i
n
in
g
h
o
w
th
e
in
te
g
r
atio
n
a
f
f
ec
ts
t
h
e
v
o
lta
g
e
s
tab
ilit
y
o
f
t
h
e
d
is
tr
ib
u
tio
n
n
e
t
w
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k
an
d
t
h
e
m
ax
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m
u
m
v
alu
e
o
f
in
teg
r
ati
n
g
t
h
e
g
en
er
ato
r
s
[
1
5
]
.
Ho
w
ev
er
,
s
o
m
e
o
f
t
h
ese
s
tu
d
ie
s
h
a
v
e
f
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lo
ca
tio
n
o
f
d
is
tr
ib
u
ted
g
en
er
atio
n
to
m
in
i
m
ize
p
o
w
er
lo
s
s
o
r
co
s
t
f
u
n
ctio
n
s
,
n
o
t
an
a
l
y
s
i
n
g
th
e
v
o
lta
g
e
s
e
n
s
it
iv
i
ties
o
f
th
e
n
o
d
es.
An
d
s
o
m
e
s
t
u
d
ies
u
s
e
s
m
eta
h
eu
r
i
s
tics
ap
p
l
y
i
n
g
t
h
e
s
a
m
e
o
b
j
ec
tiv
e
f
u
n
ct
io
n
s
to
d
eter
m
i
n
e
th
e
b
est
p
lace
f
o
r
th
e
p
o
w
er
s
o
u
r
ce
s
,
b
u
t
n
o
t
o
th
e
r
an
al
y
s
is
s
u
c
h
as
v
o
lta
g
e
ch
an
g
es
ar
e
co
n
s
id
er
ed
to
ev
alu
ate
t
h
e
p
o
s
s
ib
l
e
in
te
g
r
ati
o
n
o
f
t
h
e
r
en
e
w
ab
le
e
n
er
g
y
s
o
u
r
ce
s
w
it
h
o
u
t a
f
f
ec
t
in
g
th
e
o
p
er
atin
g
co
n
d
itio
n
s
.
T
h
is
p
ap
er
f
o
cu
s
es
o
n
d
eter
m
i
n
in
g
t
h
e
m
a
x
i
m
u
m
i
n
te
g
r
atio
n
o
f
d
is
tr
ib
u
ted
g
e
n
er
atio
n
i
n
d
i
s
tr
ib
u
tio
n
n
et
w
o
r
k
s
b
y
u
s
i
n
g
t
h
e
p
o
w
er
f
lo
w
iter
atio
n
p
r
o
ce
s
s
i
n
te
g
r
ated
to
th
e
m
etah
e
u
r
is
tic
al
g
o
r
ith
m
s
to
s
ea
r
c
h
a
b
etter
p
lace
f
o
r
th
e
p
o
w
er
s
o
u
r
ce
s
an
d
th
e
m
a
x
i
m
u
m
p
o
wer
s
u
p
o
r
ted
b
y
th
e
n
et
w
o
r
k
with
o
u
t
a
f
f
ec
tin
g
t
h
e
v
o
ltag
e
co
n
d
itio
n
s
o
f
t
h
e
n
et
w
o
r
k
.
B
ec
au
s
e
th
is
is
a
lar
g
e
co
m
b
i
n
ato
r
ial
p
r
o
b
lem
,
w
e
u
s
ed
p
ar
ticle
s
w
ar
m
o
p
tim
izatio
n
(
P
SO)
an
d
s
i
m
u
l
ated
an
n
ea
li
n
g
(
S
A
)
as
th
e
o
p
ti
m
izat
io
n
tech
n
iq
u
e
s
to
d
eter
m
i
n
e
th
e
p
lace
an
d
th
e
s
ize
o
f
DG.
As
t
h
e
o
b
jectiv
e
f
u
n
ctio
n
w
e
u
s
ed
t
h
e
i
m
p
r
o
v
e
m
en
t
o
f
t
h
e
v
o
lta
g
e
v
o
ltag
e
p
r
o
f
ile
in
d
is
tr
ib
u
tio
n
n
e
t
w
o
r
k
s
.
T
h
e
a
p
p
r
o
ac
h
is
ap
p
lied
o
n
I
E
E
E
3
3
-
b
u
s
,
I
E
E
E
6
9
-
b
u
s
an
d
I
E
E
E
1
1
8
-
b
u
s
r
ad
ial
d
is
tr
ib
u
tio
n
s
y
s
te
m
.
T
o
ac
h
iev
e
th
is
,
Sec
tio
n
2
d
escr
ib
es
t
h
e
m
et
h
o
d
o
lo
g
y
o
f
t
h
e
r
esear
ch
.
Sectio
n
3
p
r
esen
t
s
th
e
r
ev
ie
w
a
n
d
i
m
p
le
m
e
n
tati
o
n
o
f
th
e
m
et
h
o
d
o
lo
g
y
i
n
a
ca
s
e
s
t
u
d
y
.
Sectio
n
4
p
r
esen
t
s
a
b
r
ief
d
is
cu
s
s
io
n
ab
o
u
t
d
is
tr
i
b
u
ted
g
en
er
atio
n
ef
f
ec
ts
.
Fi
n
all
y
,
th
e
m
aj
o
r
co
n
tr
ib
u
tio
n
s
an
d
co
n
cl
u
s
io
n
s
o
f
th
e
p
ap
er
s
ar
e
s
u
m
m
ar
ized
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
Fig
u
r
e
1
s
u
m
m
ar
izes
th
e
m
et
h
o
d
u
s
ed
in
t
h
i
s
r
esear
ch
to
d
eter
m
in
e
th
e
ca
n
d
id
ate
n
o
d
es
f
o
r
in
s
tal
lin
g
DG.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
s
ta
r
ts
r
ea
d
in
g
t
h
e
i
n
p
u
t
d
ata
f
r
o
m
th
e
m
o
d
els
o
f
t
h
e
d
is
tr
ib
u
t
io
n
n
et
w
o
r
k
.
T
h
en
,
a
n
in
itial
p
o
p
u
lat
io
n
i
s
d
ef
i
n
ed
an
d
ev
al
u
ated
.
Fi
n
all
y
,
th
e
lo
ca
tio
n
an
d
s
ize
o
f
DG
is
d
et
er
m
in
ed
d
u
r
i
n
g
t
h
e
iter
atio
n
o
f
t
h
e
alg
o
r
it
h
m
s
.
Fig
u
r
e
1
.
Flo
w
c
h
ar
t o
f
th
e
m
e
th
o
d
o
lo
g
y
Di
st
r
i
b
u
ti
o
n
sy
st
em
m
o
d
el
S
o
l
a
r
a
n
d
w
i
n
d
m
o
d
el
s
P
r
o
b
a
b
i
l
i
sti
c
m
o
d
e
l
o
f
D
G
L
o
c
a
ti
o
n
a
n
d
si
z
e
o
f
D
G
I
n
p
u
t
d
a
ta
I
n
i
ti
a
l
p
o
p
u
l
a
ti
o
n
F
i
tn
e
s
s
ev
a
l
u
a
ti
o
n
S
a
v
e
th
e
b
est
s
o
l
u
ti
o
n
F
i
n
d
a
n
ew
s
o
l
u
ti
o
n
E
v
a
l
u
a
te
th
e
n
ew
so
l
u
ti
o
n
S
a
v
e
th
e
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ew
b
est
s
o
l
u
ti
o
n
S
to
p
i
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g
c
r
i
ter
i
a
?
S
ta
r
t
E
n
d
P
a
r
a
m
eter
s
of
th
e
a
l
g
o
r
i
th
m
s
No
Y
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
V
o
lta
g
e
s
en
s
itivity a
n
a
lysi
s
to
d
etermin
e
th
e
o
p
tim
a
l in
teg
r
a
t
io
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o
f d
is
tr
ib
u
ted
...
(
K
a
th
erin
e
C
a
b
a
n
a
)
57
2
.
1
.
No
des
f
o
r
inje
ct
ing
po
w
er
Fin
d
i
n
g
t
h
e
n
o
d
e
to
in
j
ec
t
p
o
w
er
i
n
t
h
e
d
is
tr
ib
u
t
io
n
n
et
w
o
r
k
is
i
m
p
o
r
tan
t
f
o
r
th
i
s
s
t
u
d
y
,
b
ec
au
s
e
i
t
d
ef
in
e
s
t
h
e
b
est
p
lace
to
in
s
tal
l th
e
DG,
m
ai
n
tai
n
in
g
g
o
o
d
o
p
er
atin
g
co
n
d
itio
n
s
o
f
t
h
e
s
y
s
te
m
u
n
d
er
s
tu
d
y
.
Fo
r
th
is
p
u
r
p
o
s
e,
th
e
f
o
llo
w
i
n
g
t
h
r
ee
s
tag
e
s
w
er
e
p
r
o
p
o
s
ed
in
t
h
e
a
n
al
y
s
is
to
d
eter
m
i
n
e
t
h
e
b
est
n
o
d
es
to
i
n
j
ec
t
p
o
w
er
in
t
h
e
d
is
tr
ib
u
tio
n
n
et
wo
r
k
s
[
1
6
]
.
Stag
e
1
:
d
is
tr
ib
u
tio
n
n
et
w
o
r
k
s
m
u
s
t
b
e
m
o
d
elled
to
id
en
ti
f
y
v
o
lta
g
e
s
e
n
s
iti
v
ities
o
f
d
i
f
f
er
en
t
n
o
d
es
w
h
en
in
s
ta
lli
n
g
DG.
A
s
tat
ic
m
o
d
el
o
f
ea
ch
p
o
w
er
p
lan
t
u
n
d
er
an
a
l
y
s
i
s
is
co
n
s
id
er
ed
[
1
7
]
.
T
h
e
l
o
ad
is
m
o
d
elle
d
as
co
n
s
ta
n
t
v
al
u
e
m
ai
n
tain
in
g
t
h
e
s
a
m
e
p
o
w
er
f
ac
to
r
an
d
th
e
m
a
in
s
o
u
r
ce
w
a
s
co
n
s
id
er
ed
as th
e
s
lack
n
o
d
e.
Stag
e
2
: t
h
e
o
p
er
atin
g
s
tate
o
f
th
e
d
is
tr
ib
u
tio
n
n
et
w
o
r
k
is
f
o
u
n
d
,
d
eter
m
in
in
g
t
h
e
v
o
lta
g
e
s
e
n
s
it
iv
i
ties
o
f
n
o
d
es
w
it
h
r
esp
ec
t to
th
e
r
ea
l p
o
w
er
in
j
ec
ted
[
1
8
]
.
Stag
e
3
:
ca
n
d
id
ate
n
o
d
es
ar
e
s
elec
ted
u
s
i
n
g
p
o
w
er
f
lo
w
s
a
n
d
an
al
y
ze
d
w
i
th
v
o
ltag
e
s
en
s
iti
v
ities
b
e
f
o
r
e
an
d
af
ter
in
j
ec
tin
g
r
ea
l
p
o
w
er
.
I
n
th
is
r
esear
c
h
,
d
if
f
er
en
t
s
ce
n
ar
io
s
o
f
d
e
m
an
d
ar
e
u
s
ed
to
id
en
ti
f
y
w
ea
k
n
o
d
es.
No
d
es a
r
e
class
if
ied
ac
co
r
d
in
g
to
th
e
v
o
ltag
e
v
ar
iatio
n
s
w
h
e
n
th
e
i
n
j
ec
tio
n
o
f
r
ea
l p
o
w
er
i
n
cr
ea
s
e.
2
.
2
.
S
ce
na
rio
s
f
o
r
t
esting
t
he
m
et
ho
d
T
h
e
f
o
llo
w
i
n
g
s
ce
n
ar
io
s
ar
e
c
o
n
s
id
er
ed
to
test
th
e
m
eth
o
d
a
n
d
an
al
y
ze
th
e
r
es
u
lt
s
:
1.
Scen
ar
io
1
.
I
n
th
i
s
f
ir
s
t
s
ce
n
a
r
io
,
n
o
DG
i
s
co
n
s
id
er
ed
f
o
r
th
e
s
i
m
u
la
tio
n
,
b
ei
n
g
a
b
ase
c
ase
to
co
m
p
ar
e
o
th
er
r
esu
lt
s
o
f
t
h
e
in
te
g
r
atio
n
o
f
s
o
lar
an
d
w
i
n
d
en
er
g
y
g
e
n
er
atio
n
.
2.
Scen
ar
io
2
.
I
n
th
e
s
ec
o
n
d
s
ce
n
ar
io
,
p
h
o
to
v
o
ltaic
en
er
g
y
s
y
s
t
e
m
s
ar
e
i
n
te
g
r
ated
to
th
e
d
is
tr
i
b
u
tio
n
n
et
w
o
r
k
to
d
eter
m
i
n
e
t
h
e
m
a
x
i
m
u
m
p
o
w
er
i
n
j
ec
tio
n
.
3.
Scen
ar
io
3
.
I
n
th
e
th
ir
d
s
ce
n
ar
io
,
w
i
n
d
en
er
g
y
s
y
s
te
m
s
ar
e
in
teg
r
ated
to
th
e
d
is
tr
ib
u
tio
n
n
et
w
o
r
k
t
o
d
eter
m
in
e
t
h
e
m
a
x
i
m
u
m
p
o
w
e
r
in
j
ec
tio
n
.
4.
Scen
ar
io
4
.
I
n
t
h
e
f
o
u
r
t
h
s
ce
n
ar
io
,
p
h
o
to
v
o
ltaic
an
d
w
i
n
d
en
er
g
y
s
y
s
te
m
s
ar
e
i
n
te
g
r
ated
to
th
e
d
is
tr
ib
u
t
io
n
n
et
w
o
r
k
to
d
eter
m
i
n
e
th
e
m
a
x
i
m
u
m
p
o
w
er
in
j
ec
tio
n
.
2
.
3
.
Co
ns
idera
t
io
ns
Fo
r
test
in
g
th
e
m
et
h
o
d
,
th
e
f
o
l
lo
w
i
n
g
co
n
s
id
er
atio
n
s
ar
e
s
tat
ed
:
1.
Mo
r
e
th
an
o
n
e
g
en
er
ato
r
ca
n
b
e
in
s
tal
led
at
ea
ch
n
o
d
e.
2.
A
ll
DG
u
n
it
s
o
p
er
ate
w
it
h
a
u
n
itar
y
p
o
w
er
f
ac
to
r
,
to
av
o
id
in
ter
f
er
en
ce
w
i
th
v
o
ltag
e
co
n
tr
o
l
d
ev
ices
co
n
n
ec
ted
to
th
e
s
y
s
te
m
[
1
9
]
,
[
2
0
]
.
I
n
ad
d
itio
n
,
th
e
p
o
w
er
f
a
cto
r
r
em
ai
n
s
co
n
s
ta
n
t f
o
r
all
te
s
ts
.
3.
W
in
d
s
p
ee
d
an
d
s
o
lar
r
ad
iati
o
n
h
a
v
e
t
h
e
s
a
m
e
v
al
u
es
f
o
r
all
th
e
p
o
i
n
ts
w
h
er
e
t
h
e
g
e
n
er
ato
r
s
ca
n
b
e
in
s
ta
lled
.
4.
So
m
e
r
esear
ch
er
s
r
ec
o
m
m
en
d
th
at
DG
p
en
etr
atio
n
lev
el
s
ar
e
eq
u
al
to
o
r
less
th
an
3
0
%
o
f
th
e
m
a
x
i
m
u
m
lo
ad
[
2
1
]
.
Ho
w
e
v
er
,
in
t
h
is
s
t
u
d
y
it
i
s
u
p
to
6
0
%.
T
h
e
in
cr
ea
s
in
g
p
o
w
er
s
tep
s
ar
e
d
ef
i
n
ed
as
0
,
1
0
,
2
0
,
3
0
,
4
0
,
5
0
,
an
d
6
0
%.
5.
T
h
e
m
o
d
els o
f
t
h
e
ele
m
e
n
ts
p
r
o
p
o
s
ed
in
[
2
2
]
ar
e
u
s
ed
in
th
i
s
r
esear
ch
.
6.
L
o
ad
is
m
o
d
el
led
f
o
r
th
e
f
o
u
r
s
ea
s
o
n
s
o
f
th
e
y
ea
r
,
a
s
co
n
s
id
er
ed
in
o
th
er
s
t
u
d
ies
[
2
2
]
.
T
h
e
p
o
w
er
d
e
m
a
n
d
o
f
ea
ch
d
is
tr
ib
u
tio
n
n
et
w
o
r
k
is
co
n
s
id
er
ed
as th
e
p
ea
k
v
a
lu
e.
7.
So
lar
r
ad
iatio
n
an
d
w
i
n
d
s
p
ee
d
ar
e
m
o
d
eled
u
s
i
n
g
t
h
e
B
eta
an
d
W
eib
u
ll
p
r
o
b
ab
ilit
y
d
en
s
it
y
f
u
n
ctio
n
s
,
r
esp
ec
tiv
el
y
,
a
s
co
n
s
id
er
ed
in
o
th
er
s
tu
d
ie
s
[
2
2
]
,
[
2
3
]
.
8.
DG
u
n
its
ar
e
i
n
s
talled
o
n
a
g
iv
en
n
o
d
e
an
d
th
e
v
o
ltag
e
c
h
a
n
g
es a
r
e
m
o
n
it
o
r
ed
.
9.
E
ac
h
g
e
n
er
ato
r
s
u
p
p
lies
a
co
n
s
tan
t p
o
w
er
o
f
4
,
5
MW
,
w
ith
u
n
it
y
p
o
w
er
f
ac
to
r
.
10.
T
h
e
m
in
i
m
u
m
a
n
d
m
a
x
i
m
u
m
v
o
ltag
e
v
al
u
es
f
o
r
all
t
h
e
d
is
t
r
ib
u
tio
n
n
et
w
o
r
k
s
w
er
e
d
ef
i
n
e
d
as
V
m
i
n
=0
.
9
p
.
u
.
an
d
V
m
a
x
=1
.
1
p
.
u
,
r
esp
ec
tiv
el
y
.
2
.
4
.
L
o
a
d
m
o
del
T
ab
le
1
p
r
esen
ts
t
h
e
lo
ad
p
r
o
f
ile,
as
p
er
ce
n
tag
e
s
o
f
th
e
an
n
u
al
m
a
x
i
m
u
m
lo
ad
[
2
2
]
.
A
n
n
u
a
l
m
ax
i
m
u
m
lo
ad
d
em
a
n
d
is
1
6
.
1
8
MV
A
.
Data
is
u
s
ed
to
m
o
d
el
s
o
lar
r
ad
iatio
n
an
d
w
in
d
s
p
ee
d
w
i
th
t
h
e
B
eta
an
d
W
eib
u
ll p
r
o
b
ab
ilit
y
f
u
n
cti
o
n
s
,
r
esp
ec
tiv
el
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
9
:
5
5
-
65
58
T
ab
le
1
.
Data
C
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n
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id
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o
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th
e
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So
la
r
m
o
del
So
lar
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ad
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n
w
as
m
o
d
eled
u
s
i
n
g
t
h
e
b
eta
p
r
o
b
ab
ilit
y
d
en
s
it
y
f
u
n
ctio
n
[
2
2
]
,
[
2
3
]
,
as
s
h
o
w
n
in
(
1
)
.
W
h
er
e
(
)
is
th
e
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eta
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ab
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en
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it
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n
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S
is
th
e
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o
lar
r
ad
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n
in
k
W
/
m
2
,
co
n
s
id
er
in
g
th
at
0
≤
S
≤
1
.
α
an
d
β
ar
e
p
ar
am
eter
s
o
f
t
h
e
d
is
tr
ib
u
tio
n
f
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n
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n
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co
n
s
i
d
er
in
g
t
h
at
α
>
0
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d
β
>
0
.
(
)
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{
(
+
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)
(
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−
1
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1
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−
1
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(
1
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ar
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eter
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lated
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s
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g
(
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.
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6
.
Wind
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l
W
in
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ar
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ca
n
b
e
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s
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e
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r
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ab
ilit
y
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en
s
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u
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s
h
o
w
n
in
(
4
)
.
W
h
er
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k
is
a
s
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ap
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d
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s
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o
b
ab
ilit
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is
ca
lled
R
a
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g
h
(
)
as
s
h
o
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(
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.
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as
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o
n
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2
.
0
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d
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ar
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eter
α
eq
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to
9
[
2
4
]
,
[
2
5
]
.
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h
e
s
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le
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ar
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o
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h
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c
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1
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8
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m
.
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−
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]
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3
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2
2
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[
−
[
]
2
]
(
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
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8708
V
o
lta
g
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s
en
s
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o
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tim
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r
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f d
is
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ted
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(
K
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C
a
b
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a
)
59
2
.
7
.
P
ro
ble
m
f
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n
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iatio
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ed
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=
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10
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p
ac
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1
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ited
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5
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n
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1
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5
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m
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lo
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ase.
T
h
e
in
cr
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s
e
in
t
h
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lev
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o
f
ch
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is
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r
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ied
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i
th
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a
m
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alu
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λ
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f
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2
.
8
.
O
bje
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iv
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f
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ct
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T
h
e
o
b
j
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tiv
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f
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s
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cr
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s
e
t
h
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g
e
n
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ati
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if
f
er
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n
t
n
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d
es
ac
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v
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m
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it
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d
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o
f
t
h
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n
e
t
w
o
r
k
,
a
s
s
h
o
w
n
in
(
12
)
.
W
h
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i
s
th
e
v
o
lta
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at
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h
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s
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11
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2
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9
.
E
lect
rica
l c
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ra
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T
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ated
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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r
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Vo
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a
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u
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,
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f
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m
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i
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1
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,
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T
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r
.
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
3
.
1
No
de
s
elec
t
io
n
T
ab
le
2
s
h
o
w
s
th
e
r
es
u
lt
s
o
b
tain
ed
w
h
e
n
lo
ca
ti
n
g
a
n
d
s
izi
n
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d
if
f
er
en
t
g
e
n
er
ato
r
s
in
t
h
e
d
is
tr
ib
u
tio
n
n
et
w
o
r
k
w
i
th
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
t
u
d
ied
.
T
h
e
f
ir
s
t
co
lu
m
n
is
t
h
e
d
is
tr
ib
u
tio
n
n
et
w
o
r
k
test
ca
s
e,
t
h
e
s
ec
o
n
d
co
lu
m
n
i
s
t
h
e
n
o
d
e
s
elec
ted
f
o
r
in
s
talli
n
g
DG,
a
n
d
t
h
e
o
th
e
r
co
lu
m
n
s
co
r
r
esp
o
n
d
to
t
h
e
r
esu
lt
s
o
b
tain
ed
w
i
t
h
th
e
al
g
o
r
ith
m
tes
tin
g
t
h
e
f
o
u
r
s
ce
n
ar
io
s
.
T
h
e
h
i
g
h
er
p
o
w
er
i
n
j
ec
tio
n
is
o
b
tain
ed
f
o
r
th
e
n
o
d
es
a
w
a
y
f
r
o
m
t
h
e
m
ai
n
s
o
u
r
ce
.
A
d
d
itio
n
a
ll
y
,
f
r
o
m
t
h
e
tab
le
w
e
ca
n
co
n
cl
u
d
e
th
at
th
e
alg
o
r
it
h
m
s
f
i
n
d
th
e
s
a
m
e
s
o
lu
t
io
n
s
,
b
u
t
S
A
h
a
v
e
a
lar
g
e
ti
m
e
to
co
n
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e
r
g
e
f
o
r
t
h
e
s
o
l
u
tio
n
co
m
p
ar
ed
to
th
e
P
SO.
T
h
e
n
u
m
b
er
o
f
ite
r
atio
n
s
o
f
th
e
S
A
i
s
g
r
ea
ter
th
a
n
t
h
e
u
s
ed
w
it
h
th
e
P
SO.
T
h
e
v
o
ltag
e
s
e
n
s
it
iv
i
t
y
an
al
y
s
is
f
o
u
n
d
w
it
h
P
SO
a
n
d
S
A
p
r
esen
ted
a
co
r
r
elatio
n
co
ef
f
icie
n
t
o
f
0
.
9
,
in
d
icati
n
g
a
s
tr
o
n
g
an
d
p
o
s
iti
v
e
co
r
r
elatio
n
b
et
w
ee
n
th
e
d
ata
o
b
tain
ed
b
y
ea
c
h
alg
o
r
ith
m
.
T
ab
le
2
.
L
o
ca
tio
n
an
d
S
ize
o
f
DG
w
i
th
P
SO
an
d
S
A
P
o
w
e
r
S
y
st
e
m
N
o
d
e
s
S
c
e
n
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r
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o
(
M
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)
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Fig
u
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2
s
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
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&
C
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p
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n
g
I
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N:
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(
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Fig
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.
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2
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1
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3
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–
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tab
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ty
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2
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0
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n
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M
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f.
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2
0
1
3
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1
]
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.
S
.
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p
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,
p
p
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1
–
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2
]
S
.
S
.
M
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tab
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3
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3
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2
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6
I
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6
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In
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0
1
6
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p
p
.
1
–
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[1
4
]
D.
Jia
,
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.
Hu
,
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iu
,
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L
iu
,
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.
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e
n
g
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n
d
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.
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h
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g
,
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im
p
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li
ty
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lu
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le
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ted
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e
n
e
ra
ti
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n
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b
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n
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y
ste
m
s
,
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e
n
e
r.
T
r
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.
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.
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5
]
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,
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s
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rs
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s
,
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.
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6
]
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b
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n
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,
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tatisti
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in
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ib
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m
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teg
ra
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D
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IEE
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L
a
ti
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ra
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.
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7
]
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A
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o
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.
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z
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a
leri
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o
,
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p
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tac
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e
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–
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.
[1
8
]
K.
M
o
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n
,
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.
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n
g
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A
.
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o
sh
re
f
,
a
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d
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.
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ris,
“
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ti
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f
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g
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re
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s
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ti
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se
rv
e
;
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n
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d
v
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m
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n
t
In
On
-
L
in
e
V
o
lt
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g
e
S
e
c
u
rit
y
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se
ss
m
e
n
t,
”
in
2
0
0
8
IEE
E
Po
we
r
a
n
d
En
e
rg
y
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o
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n
e
ra
l
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g
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a
n
d
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li
v
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o
f
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e
c
trica
l
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n
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rg
y
i
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st Cen
t
u
ry
,
2
0
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,
p
p
.
1
–
7.
[1
9
]
R.
A
.
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ll
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g
,
R.
S
a
in
t
,
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C
.
D
u
g
a
n
,
J.
B
u
rk
e
,
a
n
d
L
.
A
.
Ko
j
o
v
ic,
“
S
u
m
m
a
r
y
o
f
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ted
Re
so
u
rc
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s
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p
a
c
t
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n
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o
w
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r
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li
v
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r
y
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m
s,”
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E
T
ra
n
s.
Po
we
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li
v
.
,
v
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l
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4
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l.
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0
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.
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0
]
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Zein
e
ld
in
,
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El
-
sa
a
d
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n
y
,
a
n
d
M
.
A
.
S
a
lam
a
,
“
Distrib
u
ted
G
e
n
e
r
a
ti
o
n
M
icro
-
G
rid
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e
ra
ti
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n
:
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o
n
tr
o
l
a
n
d
P
r
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tec
ti
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n
,
”
in
2
0
0
6
P
o
we
r
S
y
ste
ms
Co
n
fer
e
n
c
e
:
Ad
v
a
n
c
e
d
M
e
te
rin
g
,
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tec
ti
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n
,
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n
tro
l
,
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mm
u
n
ica
ti
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n
,
a
n
d
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ted
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rc
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s
,
2
0
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6
,
p
p
.
1
0
5
–
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1
1
.
[2
1
]
P
.
C.
L
u
Zh
a
n
g
,
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e
i
T
a
n
g
,
M
u
k
e
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i,
“
A
n
a
l
y
si
s
o
f
D
istri
b
u
ted
G
e
n
e
r
a
ti
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n
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f
lu
e
n
c
e
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h
e
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l
tag
e
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i
m
it
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io
latio
n
P
r
o
b
a
b
il
it
y
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ti
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n
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in
e
,
”
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n
e
rg
y
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we
r E
n
g
.
,
v
o
l.
5
,
p
p
.
7
5
6
–
7
6
2
,
2
0
1
3
.
[2
2
]
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M
.
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t
w
a
,
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.
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-
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a
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d
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.
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.
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.
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a
lam
a
,
a
n
d
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S
e
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th
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p
a
th
y
,
“
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ti
m
a
l
Re
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e
w
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b
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so
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s
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ix
f
o
r
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strib
u
ti
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n
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y
ste
m
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e
rg
y
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o
ss
M
in
im
iza
ti
o
n
,
”
IEE
E
T
ra
n
s.
P
o
w
e
r S
y
st.
,
v
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2
5
,
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.
1
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p
p
.
3
6
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.
2
0
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.
[2
3
]
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.
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tw
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,
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.
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.
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lam
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,
a
n
d
R.
S
e
e
th
a
p
a
th
y
,
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u
ti
o
n
S
y
ste
m
L
o
ss
M
in
im
iza
ti
o
n
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in
g
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ti
m
a
l
D
G
M
ix
,
”
in
2
0
0
9
IEE
E
P
o
we
r
&
En
e
rg
y
S
o
c
iety
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e
n
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ra
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ti
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g
,
2
0
0
9
,
p
p
.
1
–
6.
[2
4
]
M
.
H.
A
lb
a
d
i
a
n
d
E
.
F
.
E
l
-
S
a
a
d
a
n
y
,
“
No
v
e
l
M
e
th
o
d
F
o
r
Esti
m
a
ti
n
g
th
e
CF
o
f
V
a
riab
le
S
p
e
e
d
W
in
d
T
u
rb
i
n
e
s
,
”
i
n
2
0
0
9
IE
EE
P
o
we
r
&
En
e
rg
y
S
o
c
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ty Ge
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ra
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ti
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g
,
2
0
0
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p
p
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–
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5
]
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.
H.
A
lb
a
d
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a
n
d
E.
F
.
El
-
S
a
a
d
a
n
y
,
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in
d
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u
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in
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s
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p
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it
y
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c
to
r
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&
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x
2
0
1
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;A
No
v
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l
A
p
p
ro
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h
,
”
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ra
n
s.
P
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r S
y
st.
,
v
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l.
2
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,
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o
.
3
,
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p
.
1
6
3
7
–
1
6
3
8
,
A
u
g
.
2
0
0
9
.
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