I
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ia
n J
o
urna
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rica
l En
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Co
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er
Science
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42
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No
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3
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2
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6
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p
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1
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42
.i
3
.
pp
666
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67
7
666
J
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:
h
ttp
:
//ij
ee
cs.ia
esco
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co
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M
etaheuris
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opt
imiza
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f
wi
nd
turbin
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farm
siti
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in
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rids:
a
co
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f
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a
re
a
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o
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o
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d
tu
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b
in
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s
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th
e
IE
EE
14
-
b
u
s
sy
ste
m
.
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e
p
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lem
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f
o
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u
lat
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d
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m
u
lt
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in
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ts.
S
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su
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sin
g
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AT
LAB/P
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sh
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m
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wo
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k
c
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n
tri
b
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tes
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n
d
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su
p
p
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b
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ty
a
n
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l
y
sis a
n
d
re
a
li
stic win
d
m
o
d
e
l
li
n
g
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K
ey
w
o
r
d
s
:
Gen
etic
alg
o
r
ith
m
Me
tah
eu
r
is
tic
o
p
tim
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za
tio
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Po
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s
m
in
im
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Vo
ltag
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en
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d
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a
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CC B
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SA
li
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.
C
o
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r
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s
p
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uth
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r
:
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ah
a
R
ac
h
d
i
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ab
o
r
ato
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f
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to
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L
AR
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Natio
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g
in
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Sch
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o
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f
T
u
n
is
(
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)
T
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E
l M
an
ar
Un
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r
s
ity
T
u
n
is
,
T
u
n
is
ia
E
m
ail:
tah
a.
r
ac
h
d
i@
en
it.u
tm
.
t
n
1.
I
NT
RO
D
UCT
I
O
N
W
in
d
en
er
g
y
h
as
b
ec
o
m
e
a
k
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co
m
p
o
n
en
t
in
m
o
d
e
r
n
p
o
wer
s
y
s
tem
s
,
s
u
p
p
o
r
te
d
b
y
ad
v
an
ce
s
in
p
o
wer
elec
tr
o
n
ics
an
d
r
en
e
wab
le
tech
n
o
lo
g
ies
th
at
f
ac
ilit
ate
it
s
in
teg
r
atio
n
in
to
ex
is
tin
g
g
r
id
s
[
1
]
−
[
4
]
.
As
d
is
tr
ib
u
ted
en
er
g
y
r
eso
u
r
c
es
co
n
tin
u
e
to
r
esh
a
p
e
tr
a
d
itio
n
al
p
ass
iv
e
n
etwo
r
k
s
in
to
d
y
n
am
ic
a
n
d
ac
tiv
e
s
y
s
tem
s
,
o
p
er
ato
r
s
f
ac
e
in
c
r
ea
s
in
g
co
m
p
le
x
ity
in
v
o
ltag
e
r
e
g
u
latio
n
,
p
o
wer
q
u
ality
,
p
r
o
te
ctio
n
co
o
r
d
in
atio
n
,
an
d
b
id
i
r
ec
tio
n
al
p
o
wer
f
lo
w
m
an
ag
em
e
n
t
[
5
]
−
[
8
]
.
Mo
r
eo
v
er
,
th
e
i
n
ter
m
itten
t
an
d
u
n
p
r
ed
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le
n
atu
r
e
o
f
win
d
g
en
er
atio
n
p
o
s
es
ad
d
itio
n
al
ch
allen
g
es
f
o
r
m
ain
tai
n
in
g
s
u
p
p
ly
–
d
em
an
d
b
alan
ce
,
m
i
tig
atin
g
co
n
g
esti
o
n
,
an
d
en
s
u
r
in
g
f
r
e
q
u
en
cy
s
tab
ilit
y
.
Alth
o
u
g
h
th
e
b
e
n
ef
its
o
f
win
d
in
teg
r
atio
n
s
u
ch
as
r
ed
u
ce
d
tr
an
s
m
is
s
io
n
lo
s
s
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en
h
an
c
ed
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er
g
y
s
ec
u
r
ity
,
an
d
r
e
d
u
ce
d
en
v
ir
o
n
m
en
tal
im
p
ac
t
ar
e
well
ac
k
n
o
wled
g
ed
[
9
]
−
[
1
1
]
,
th
eir
e
f
f
ec
tiv
en
ess
s
tr
o
n
g
ly
d
ep
en
d
s
o
n
ap
p
r
o
p
r
iate
s
itin
g
an
d
s
izin
g
.
I
n
a
d
eq
u
ate
p
lace
m
en
t
o
r
im
p
r
o
p
er
ca
p
ac
ity
s
e
lectio
n
m
ay
lead
to
h
ig
h
e
r
l
o
s
s
es,
v
o
ltag
e
d
e
v
iatio
n
s
,
an
d
d
e
g
r
ad
ed
s
y
s
tem
p
er
f
o
r
m
an
ce
[
1
0
]
−
[
1
2
]
.
T
o
ad
d
r
ess
th
ese
co
n
ce
r
n
s
,
s
ev
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al
s
tu
d
ies
h
av
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ap
p
lied
m
etah
eu
r
is
tic
m
eth
o
d
s
s
u
ch
as
g
en
etic
alg
o
r
ith
m
s
(
GA)
an
d
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
,
d
em
o
n
s
t
r
atin
g
n
o
tab
le
im
p
r
o
v
em
e
n
ts
in
lo
s
s
m
in
im
izatio
n
an
d
v
o
ltag
e
s
u
p
p
o
r
t
o
n
b
en
ch
m
ar
k
n
etwo
r
k
s
,
p
ar
ticu
l
ar
ly
th
e
I
E
E
E
1
4
-
b
u
s
s
y
s
tem
[
1
3
]
−
[
1
7
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
E
lec
E
n
g
&
C
o
m
p
Sci
I
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N:
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-
4
7
5
2
Meta
h
eu
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o
p
timiz
a
tio
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f wi
n
d
t
u
r
b
in
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fa
r
m
s
itin
g
in
p
o
w
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r
id
s
:
a
co
mp
a
r
a
tive
…
(
Ta
h
a
R
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c
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i
)
667
Ho
wev
er
,
d
esp
ite
t
h
is
p
r
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ess
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two
im
p
o
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ta
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ap
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r
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im
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ly
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m
its
th
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p
r
ac
tical
ap
p
licab
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d
PS
O
ar
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o
f
ten
q
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o
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ased
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im
p
lifie
d
s
tab
ilit
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in
d
icat
o
r
s
,
f
ailin
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to
ass
ess
th
eir
p
er
f
o
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m
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ce
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n
d
e
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r
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lis
tic
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g
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t f
o
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win
d
v
ar
iab
ilit
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d
d
etailed
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s
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ilit
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cr
iter
ia.
T
h
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ch
a
d
d
r
ess
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th
ese
s
h
o
r
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m
in
g
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b
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is
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d
PS
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o
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th
e
s
im
u
ltan
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u
s
o
p
tim
al
s
itin
g
an
d
s
izin
g
o
f
win
d
tu
r
b
in
es
in
th
e
I
E
E
E
1
4
-
b
u
s
s
y
s
tem
.
T
h
e
s
tu
d
y
d
is
tin
g
u
is
h
es its
elf
f
r
o
m
p
r
ev
io
u
s
wo
r
k
b
y
:
−
E
m
p
lo
y
in
g
an
a
d
v
an
ce
d
v
o
lta
g
e
s
tab
ilit
y
in
d
ex
b
ased
o
n
t
h
e
r
ed
u
ce
d
Q
–
V
J
ac
o
b
ian
[
9
]
,
−
I
n
teg
r
atin
g
r
ea
lis
tic
win
d
v
ar
iab
ilit
y
th
r
o
u
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a
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u
ll
d
i
s
tr
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u
tio
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m
o
d
el,
as
r
ec
o
m
m
en
d
ed
in
win
d
m
o
d
ellin
g
s
tu
d
ies [
1
8
]
−
[
2
5
]
,
−
I
n
ad
d
itio
n
,
v
alid
atin
g
th
e
o
p
t
im
izatio
n
r
esu
lts
th
r
o
u
g
h
b
o
th
s
tatic
an
d
d
y
n
am
ic
s
im
u
latio
n
s
,
in
alig
n
m
en
t
with
r
ec
en
t r
ec
o
m
m
en
d
atio
n
s
in
r
en
ewa
b
le
en
e
r
g
y
in
teg
r
atio
n
r
esear
ch
[
1
1
]
,
[
1
9
]
−
[
2
1
]
.
T
h
r
o
u
g
h
th
is
a
p
p
r
o
ac
h
,
th
e
s
tu
d
y
aim
s
to
o
f
f
e
r
a
m
o
r
e
ac
cu
r
ate,
r
o
b
u
s
t,
an
d
p
r
ac
tically
r
elev
an
t
ev
alu
atio
n
o
f
win
d
t
u
r
b
in
e
in
t
eg
r
atio
n
s
tr
ateg
ies.
T
h
e
s
tr
u
ctu
r
e
o
f
th
is
p
ap
er
is
as
f
o
llo
ws:
Sectio
n
2
o
u
tlin
es
th
e
r
esear
ch
p
r
o
b
lem
,
d
etailin
g
th
e
o
b
jectiv
e
f
u
n
ctio
n
s
,
d
ec
is
io
n
v
ar
iab
les,
o
p
tim
izatio
n
c
r
iter
ia,
an
d
s
y
s
tem
co
n
s
tr
ain
ts
.
Sectio
n
3
d
escr
ib
es
th
e
m
eth
o
d
o
lo
g
ical
ap
p
r
o
a
ch
,
o
f
f
e
r
in
g
b
ac
k
g
r
o
u
n
d
in
f
o
r
m
atio
n
o
n
t
h
e
GA
an
d
PS
O
tech
n
iq
u
es.
Sectio
n
4
p
r
esen
ts
th
e
r
esu
lts
an
d
d
is
c
u
s
s
es
o
f
th
e
s
im
u
latio
n
o
u
tco
m
es.
Fin
ally
,
s
ec
tio
n
5
co
n
clu
d
es th
e
s
tu
d
y
a
n
d
s
u
g
g
ests
av
en
u
es f
o
r
f
u
tu
r
e
r
esear
ch
.
2.
T
H
E
P
RO
B
L
E
M
S
T
A
T
M
E
NT
T
h
is
s
tu
d
y
ad
d
r
ess
es
th
e
o
p
ti
m
izatio
n
o
f
win
d
tu
r
b
in
e
p
lac
em
en
t
an
d
s
izin
g
in
a
d
is
tr
ib
u
tio
n
g
r
id
.
T
h
e
o
b
jectiv
e
is
to
m
in
im
ize
ac
tiv
e
p
o
wer
lo
s
s
es
an
d
im
p
r
o
v
e
th
e
v
o
lta
g
e
p
r
o
f
ile
t
h
r
o
u
g
h
a
m
u
lti
-
o
b
jectiv
e
f
u
n
ctio
n
.
T
h
e
d
ec
is
io
n
v
a
r
iab
l
es
ar
e
tu
r
b
in
e
lo
ca
tio
n
an
d
ca
p
ac
ity
,
wh
ile
co
n
s
tr
ain
ts
in
clu
d
e
lin
e
lim
its
,
lo
ad
f
lo
w
eq
u
atio
n
s
,
a
n
d
b
u
s
v
o
lt
ag
e
b
o
u
n
d
ar
ies.
T
h
e
g
o
al
is
t
o
en
s
u
r
e
r
eliab
le
o
p
er
atio
n
a
n
d
e
n
h
an
ce
s
y
s
tem
p
er
f
o
r
m
an
ce
with
o
p
tim
al
r
en
ewa
b
le
in
teg
r
atio
n
.
2
.
1
.
O
bje
c
t
iv
es f
un
ct
io
ns
T
h
e
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
aim
s
to
m
in
im
ize
ac
tiv
e
p
o
wer
lo
s
s
es
(
1
)
an
d
im
p
r
o
v
e
t
h
e
v
o
ltag
e
p
r
o
f
ile
(
2
)
.
Activ
e
p
o
wer
lo
s
s
es
ar
e
ca
lcu
lated
as
th
e
s
u
m
o
f
th
e
p
r
o
d
u
cts
o
f
lin
e
r
esis
tan
ce
s
an
d
th
e
s
q
u
ar
es
o
f
th
e
cu
r
r
en
ts
f
lo
win
g
th
r
o
u
g
h
th
em
.
Vo
ltag
e
p
r
o
f
ile
im
p
r
o
v
em
en
t
is
ev
alu
ated
b
y
m
ea
s
u
r
in
g
th
e
d
ev
iatio
n
o
f
b
u
s
v
o
ltag
es
f
r
o
m
th
e
n
o
m
i
n
al
v
alu
e
ac
r
o
s
s
th
e
en
tire
n
et
wo
r
k
.
T
o
b
alan
ce
t
h
ese
two
cr
iter
ia,
a
co
m
b
i
n
ed
o
b
jectiv
e
f
u
n
ctio
n
(
)
is
d
ef
in
e
d
as
a
weig
h
te
d
s
u
m
o
f
1
an
d
2
,
with
r
esp
ec
tiv
e
co
ef
f
icien
ts
o
f
1
an
d
2
.
E
ac
h
ter
m
is
n
o
r
m
alize
d
b
y
i
ts
m
ax
im
u
m
v
alu
e
to
en
s
u
r
e
co
m
p
ar
ab
le
s
ca
les
b
etwe
en
th
e
two
o
b
jectiv
es.
T
h
e
f
u
n
ctio
n
s
ar
e
g
i
v
en
b
y
th
e
f
o
llo
win
g
e
q
u
atio
n
s
:
T
o
m
in
im
ize
th
e
ac
tiv
e
p
o
wer
lo
s
s
es:
1
=
∑
2
(
1
)
W
ith
:
: T
h
e
lin
e
r
esis
tan
ce
th
r
o
u
g
h
t
h
e
lin
e.
: T
h
e
cu
r
r
en
t p
ass
in
g
b
etwe
en
b
u
s
k
an
d
l.
I
n
ad
d
itio
n
,
im
p
r
o
v
in
g
v
o
ltag
e
p
r
o
f
ile
is
g
iv
e
n
b
y
th
e
f
o
llo
win
g
eq
u
atio
n
:
2
=
∑
(
(
)
−
1
)
2
=
1
(
2
)
W
h
er
e:
(
)
: Bu
s
i m
ag
n
itu
d
e
v
o
ltag
e.
: T
h
e
n
u
m
b
er
o
f
b
u
s
in
th
e
n
e
two
r
k
.
L
astl
y
,
th
e
f
o
llo
win
g
s
tatem
en
t p
r
o
v
id
es th
e
co
m
b
i
n
ed
o
b
jec
tiv
e
f
u
n
ctio
n
.
=
1
(
1
)
+
2
(
2
)
(
3
)
W
h
er
e:
T
h
e
weig
h
ted
co
ef
f
icien
ts
f
o
r
(
1
)
an
d
(
2
)
ar
e
d
en
o
t
ed
b
y
1
an
d
2
.
I
n
o
u
r
ca
s
e
we
tak
e
1
=
0
.
4
an
d
2
=
0
.
6
.
T
h
e
weig
h
tin
g
f
ac
to
r
s
wer
e
s
elec
ted
b
ased
o
n
th
e
r
elativ
e
im
p
o
r
tan
ce
o
f
ea
ch
o
b
jectiv
e.
Vo
ltag
e
s
tab
ilit
y
is
th
e
m
o
s
t
c
r
itical
cr
iter
io
n
wh
e
n
in
te
g
r
atin
g
win
d
tu
r
b
i
n
es,
wh
ich
j
u
s
tifie
s
a
h
ig
h
er
weig
h
t
(
w2
=0
.
6
)
.
Pre
lim
in
ar
y
test
s
s
h
o
wed
th
at
m
o
d
er
ate
v
ar
iat
io
n
s
o
f
th
e
weig
h
ts
d
o
n
o
t
af
f
ec
t
th
e
o
p
tim
al
lo
ca
tio
n
s
,
in
d
icatin
g
r
o
b
u
s
tn
ess
o
f
th
e
s
o
lu
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
3
,
J
u
n
e
20
2
6
:
6
6
6
-
67
7
668
2
.
2
.
Dec
is
io
n v
a
ria
bles
a
nd
decisi
o
n
v
ec
t
o
r
I
n
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
,
two
m
ain
d
ec
is
io
n
v
a
r
iab
les
ar
e
co
n
s
id
er
e
d
:
th
e
p
lace
m
en
t
o
f
win
d
tu
r
b
in
es
with
in
th
e
d
is
tr
ib
u
ti
o
n
g
r
id
(
)
an
d
th
e
g
en
e
r
atio
n
ca
p
ac
ity
o
r
s
ize
o
f
ea
ch
in
s
talled
u
n
it
(
)
.
T
h
ese
v
ar
iab
les
d
ir
ec
tly
i
n
f
lu
en
ce
n
etwo
r
k
p
er
f
o
r
m
an
c
e
b
y
af
f
ec
tin
g
p
o
we
r
lo
s
s
es
an
d
v
o
ltag
e
s
tab
ilit
y
.
T
o
f
o
r
m
alize
th
e
o
p
t
im
izatio
n
p
r
o
b
lem
,
a
d
ec
is
io
n
v
ec
to
r
X
is
d
ef
i
n
ed
,
c
o
m
b
in
i
n
g
b
o
t
h
p
ar
a
m
eter
s
.
T
h
u
s
,
th
e
v
ec
t
o
r
is
ex
p
r
ess
ed
a
s
:
=
[
,
]
(
4
)
it
r
ep
r
esen
ts
s
im
u
ltan
eo
u
s
ly
th
e
o
p
tim
al
lo
ca
tio
n
s
an
d
th
e
co
r
r
esp
o
n
d
in
g
s
izes
o
f
wi
n
d
tu
r
b
in
es
in
th
e
d
is
tr
ib
u
tio
n
s
y
s
tem
.
2.
3
.
Co
ns
t
ra
ints
T
h
e
o
p
tim
izatio
n
o
f
th
e
p
lace
m
en
t
an
d
o
p
e
r
atio
n
o
f
wi
n
d
tu
r
b
in
e
g
en
er
atio
n
u
n
its
with
in
a
d
is
tr
ib
u
tio
n
n
etwo
r
k
m
u
s
t
co
m
p
ly
with
s
ev
er
al
tech
n
ical
co
n
s
tr
ain
ts
to
en
s
u
r
e
v
o
ltag
e
s
tab
ilit
y
an
d
m
in
im
ize
p
o
wer
lo
s
s
es.
Firstl
y
,
th
e
ac
tiv
e
p
o
wer
f
lo
win
g
b
etwe
en
t
wo
b
u
s
es
o
n
t
h
e
s
am
e
lin
e
m
u
s
t
n
o
t
ex
ce
e
d
th
e
lin
e’
s
th
er
m
al
p
o
wer
lim
it.
Seco
n
d
ly
,
lo
ad
f
lo
w
eq
u
atio
n
s
m
u
s
t
b
e
s
atis
f
ied
,
ac
co
u
n
tin
g
f
o
r
th
e
in
jecte
d
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
wer
s
at
ea
ch
b
u
s
,
as
well
as
th
e
v
o
ltag
e
m
ag
n
itu
d
es,
p
h
ase
an
g
les,
co
n
d
u
ctan
ce
,
an
d
s
u
s
ce
p
tan
ce
o
f
th
e
lin
es.
=
∑
(
c
os
(
−
+
)
)
+
(
−
+
)
(
5
)
=
∑
(
s
in
(
−
+
)
)
−
(
−
+
)
(
6
)
wh
er
e
:
an
d
ar
e
th
e
in
jecte
d
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
wer
s
,
t
h
e
v
o
ltag
e
v
alu
e
is
p
r
esen
ted
b
y
Vi
an
d
t
h
e
an
g
le
v
alu
e
is
p
r
esen
ted
b
y
at
i
-
b
u
s
.
Als
o
an
d
ar
e
th
e
s
u
s
ce
p
tan
ce
an
d
c
o
n
d
u
ctan
ce
.
F
in
ally
,
th
e
v
o
ltag
e
at
ea
ch
b
u
s
m
u
s
t r
em
a
in
with
in
s
p
ec
if
ied
lim
its
to
en
s
u
r
e
th
e
s
af
e
an
d
r
eliab
le
o
p
e
r
atio
n
o
f
th
e
g
r
id
.
≤
≤
(
7
)
W
h
er
e
Vi
i
s
th
e
i
-
b
u
s
m
ag
n
itu
d
e
v
o
ltag
e,
Vm
ax
an
d
Vm
in
ar
e
th
e
v
o
ltag
e
lim
ite
m
ax
im
u
m
an
d
m
in
im
u
m
.
I
n
ad
d
itio
n
t
o
th
e
n
etwo
r
k
o
p
er
atin
g
lim
its
,
c
o
n
s
tr
ain
ts
r
elate
d
to
th
e
win
d
t
u
r
b
in
e
u
n
its
ar
e
also
im
p
o
s
ed
in
o
r
d
er
to
en
s
u
r
e
r
e
alis
tic
an
d
tech
n
ically
f
ea
s
ib
le
o
p
er
atin
g
c
o
n
d
itio
n
s
.
T
h
e
ac
tiv
e
p
o
wer
in
jecte
d
b
y
ea
ch
win
d
tu
r
b
in
e
m
u
s
t r
e
m
ain
with
in
its
r
ated
ca
p
ac
ity
lim
its
,
wh
ich
ca
n
b
e
ex
p
r
ess
ed
as:
0
≤
,
≤
,
,
=
1
…
(
8
)
wh
er
e
,
is
th
e
ac
tiv
e
p
o
wer
g
en
er
ated
b
y
th
e
i
-
th
win
d
tu
r
b
in
e
an
d
,
d
en
o
tes it
s
m
ax
im
u
m
ad
m
is
s
ib
le
ca
p
ac
ity
.
W
in
d
tu
r
b
in
es
m
u
s
t
also
co
m
p
ly
with
r
ea
ctiv
e
p
o
wer
ca
p
ab
ilit
y
cu
r
v
es.
T
h
eir
r
ea
ctiv
e
p
o
wer
in
jectio
n
o
r
a
b
s
o
r
p
tio
n
is
b
o
u
n
d
ed
b
y
:
0
≤
,
≤
,
,
=
1
…
(
9
)
T
h
ese
co
n
s
tr
ain
ts
en
s
u
r
e
th
at
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
d
o
es
n
o
t
s
elec
t
in
f
ea
s
ib
le
tu
r
b
in
e
s
izes
o
r
u
n
r
ea
lis
tic
p
en
etr
atio
n
lev
els,
an
d
th
at
th
e
r
esu
ltin
g
s
o
lu
ti
o
n
s
r
em
ain
co
m
p
atib
le
with
p
r
ac
tical
d
esig
n
a
n
d
o
p
er
atio
n
al
r
e
q
u
ir
em
en
ts
.
I
n
o
u
r
wo
r
k
,
th
e
“stab
ilit
y
in
d
ex
”
is
d
er
iv
ed
f
r
o
m
th
e
r
ed
u
ce
d
Q
–
V
J
ac
o
b
ian
o
f
th
e
New
to
n
–
R
ap
h
s
o
n
p
o
wer
f
lo
w
.
Af
ter
f
o
r
m
in
g
th
e
r
ed
u
ce
d
J
ac
o
b
ian
,
we
co
m
p
u
te
its
eig
en
v
alu
es
an
d
d
ef
in
e
th
e
s
tab
ilit
y
in
d
e
x
as:
=
∑
1
(
)
=
1
(
1
0
)
th
is
s
ca
lar
in
d
icato
r
m
ea
s
u
r
es
th
e
p
r
o
x
im
ity
o
f
th
e
s
y
s
tem
to
v
o
ltag
e
in
s
tab
ilit
y
:
wh
en
th
e
s
y
s
tem
ap
p
r
o
ac
h
es
a
v
o
ltag
e
co
llap
s
e
p
o
in
t,
o
n
e
o
f
th
e
eig
en
v
alu
es
o
f
ten
d
s
to
ze
r
o
,
wh
ich
ca
u
s
es
th
e
in
v
er
s
e
(
an
d
th
u
s
SI)
to
g
r
o
w
v
e
r
y
lar
g
e
.
C
o
n
v
er
s
ely
,
s
m
aller
v
alu
es
o
f
SI
in
d
icate
a
m
o
r
e
s
tab
le
o
p
er
atin
g
co
n
d
itio
n
with
a
h
ea
lth
ier
v
o
ltag
e
p
r
o
f
ile
[
2
6
]
a
n
d
[
2
7
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Meta
h
eu
r
is
tic
o
p
timiz
a
tio
n
o
f wi
n
d
t
u
r
b
in
e
fa
r
m
s
itin
g
in
p
o
w
er g
r
id
s
:
a
co
mp
a
r
a
tive
…
(
Ta
h
a
R
a
c
h
d
i
)
669
3.
M
E
T
H
O
D
O
L
O
G
Y
-
G
A
AN
D
P
SO
O
P
T
I
M
I
Z
AT
I
O
N
T
H
E
O
RY
T
h
e
g
o
al
is
to
d
eter
m
in
e
th
e
o
p
tim
al
lo
ca
tio
n
an
d
s
ize
o
f
wi
n
d
tu
r
b
in
es
i
n
a
d
is
tr
ib
u
tio
n
n
etwo
r
k
to
m
in
im
ize
ac
tiv
e
p
o
wer
lo
s
s
es a
n
d
en
h
an
ce
th
e
v
o
ltag
e
p
r
o
f
il
e.
3
.
1
.
G
enet
ic
a
lg
o
rit
h
m
GA
ar
e
in
s
p
ir
ed
b
y
Dar
win
’
s
th
eo
r
y
o
f
ev
o
lu
tio
n
a
n
d
th
e
p
r
in
cip
le
o
f
th
e
“su
r
v
iv
al
o
f
t
h
e
f
ittes
t.”
T
h
ey
s
im
u
late
th
e
n
atu
r
al
p
r
o
ce
s
s
es
o
f
s
elec
tio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
tatio
n
to
ev
o
lv
e
o
p
ti
m
al
s
o
lu
tio
n
s
.
E
ac
h
ch
r
o
m
o
s
o
m
e
r
ep
r
esen
ts
a
p
o
te
n
tial
s
o
lu
tio
n
.
T
h
e
GA
s
tep
s
f
o
r
th
is
wo
r
k
ar
e
o
r
g
a
n
ized
as
f
o
llo
ws
;
p
o
p
u
latio
n
in
itializatio
n
:
R
an
d
o
m
g
en
e
r
at
io
n
o
f
an
in
itial
p
o
p
u
latio
n
o
f
s
ize
Np
o
p
,
wh
er
e
ea
ch
c
h
r
o
m
o
s
o
m
e
r
ep
r
esen
ts
a
b
u
s
lo
ca
tio
n
.
−
Fit
n
ess
f
u
n
ctio
n
ev
alu
atio
n
:
t
wo
o
b
jectiv
es
ar
e
co
n
s
id
er
ed
:
R
ed
u
ctio
n
o
f
ac
tiv
e
p
o
wer
lo
s
s
es
F1
an
d
im
p
r
o
v
em
e
n
t o
f
v
o
ltag
e
p
r
o
f
ile
F2
.
−
Par
en
t
s
elec
tio
n
:
u
s
in
g
m
eth
o
d
s
s
u
ch
as
r
o
u
lette
wh
ee
l o
r
to
u
r
n
am
en
t
s
elec
tio
n
.
−
C
r
o
s
s
o
v
er
:
n
ew
o
f
f
s
p
r
i
n
g
ar
e
g
en
er
ated
f
r
o
m
s
elec
ted
p
ar
en
ts
.
−
Mu
tatio
n
:
r
an
d
o
m
g
e
n
e
m
o
d
if
icatio
n
s
to
m
ain
tain
d
iv
e
r
s
ity
an
d
p
r
e
v
en
t lo
ca
l
o
p
tim
a.
−
Nex
t
g
en
er
atio
n
:
o
f
f
s
p
r
i
n
g
ar
e
in
clu
d
ed
i
n
th
e
n
ew
p
o
p
u
latio
n
.
−
Sto
p
p
in
g
iter
atio
n
:
th
e
p
r
o
ce
s
s
s
to
p
s
wh
en
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
n
ite
r
is
r
ea
ch
ed
o
r
co
n
v
er
g
en
ce
is
ac
h
iev
e
d
.
3
.
2
.
P
a
rt
icle
s
wa
rm
o
ptim
iz
a
t
io
n
PS
O
i
s
in
s
p
ir
ed
b
y
th
e
s
o
cial
b
eh
av
io
u
r
o
f
b
i
r
d
s
o
r
f
is
h
.
E
a
ch
p
ar
ticle
r
ep
r
esen
ts
a
p
o
ten
t
ial
s
o
lu
tio
n
an
d
ad
ju
s
ts
its
p
o
s
itio
n
b
ased
o
n
:
I
ts
p
er
s
o
n
al
b
est
p
o
s
itio
n
(
,
)
an
d
th
e
g
l
o
b
al
b
est
p
o
s
itio
n
(
,
).
T
h
e
PS
O
u
p
d
ate
eq
u
atio
n
s
ar
e
g
iv
e
n
in
th
e
f
o
llo
win
g
e
q
u
atio
n
s
:
v
elo
city
u
p
d
ate:
(
+
1
)
=
.
(
)
+
1
.
1
.
(
,
−
(
)
)
+
2
.
2
.
(
,
−
(
)
)
(
1
1
)
Po
s
itio
n
u
p
d
ate:
(
+
1
)
=
(
)
+
(
+
1
)
(
1
2
)
I
n
er
tia
weig
h
t a
d
a
p
tatio
n
:
=
−
(
−
)
.
(
1
3
)
T
h
e
PS
O
s
tep
s
f
o
r
th
e
p
r
o
p
o
s
e
d
alg
o
r
ith
m
ar
e
o
r
g
an
ized
as f
o
llo
ws:
−
L
o
ad
s
y
s
tem
d
ata
an
d
s
et
m
ax
im
u
m
iter
atio
n
s
.
−
I
d
en
tify
t
h
e
s
u
b
s
tatio
n
b
u
s
an
d
p
er
f
o
r
m
lo
ad
f
lo
w
an
al
y
s
is
.
−
C
alcu
late
ac
tiv
e
p
o
wer
lo
s
s
es
.
−
I
n
itialize
a
s
war
m
o
f
p
ar
ticles
r
ep
r
esen
tin
g
b
u
s
p
o
s
itio
n
s
.
−
E
v
alu
ate
f
itn
ess
(
lo
s
s
es)
o
f
ea
ch
p
ar
ticle.
−
Up
d
ate
p
er
s
o
n
al
b
est (
)
if
cu
r
r
en
t f
itn
ess
is
b
etter
.
−
Up
d
ate
g
lo
b
al
b
est (
)
ac
r
o
s
s
th
e
s
war
m
.
−
Up
d
ate
v
elo
cities an
d
p
o
s
itio
n
s
u
s
in
g
eq
u
atio
n
s
(
9
)
an
d
(
1
0
)
.
−
R
ep
ea
t u
n
til co
n
v
er
g
en
ce
o
r
is
r
ea
ch
ed
.
−
Select
th
e
b
est p
ar
ticle
as th
e
o
p
tim
al
p
lace
m
en
t
f
o
r
th
e
win
d
tu
r
b
in
e
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
r
esear
ch
,
T
h
e
GA
an
d
th
e
PS
O
wer
e
ap
p
lied
to
d
et
er
m
in
e
th
e
m
o
s
t
ef
f
ec
tiv
e
lo
c
atio
n
s
f
o
r
in
teg
r
atin
g
win
d
t
u
r
b
in
es
in
t
o
th
e
I
E
E
E
1
4
-
b
u
s
p
o
wer
s
y
s
tem
.
T
h
e
s
im
u
latio
n
wo
r
k
w
as
co
n
d
u
cted
u
s
in
g
MA
T
L
AB
R
2
0
1
0
a.
T
h
e
m
ai
n
g
o
al
was
to
m
in
im
ize
ac
tiv
e
p
o
wer
lo
s
s
es,
im
p
r
o
v
e
th
e
v
o
ltag
e
p
r
o
f
ile,
a
n
d
en
h
an
ce
th
e
o
v
er
all
p
o
wer
f
ac
to
r
o
f
th
e
s
y
s
tem
.
T
h
e
p
o
wer
n
etwo
r
k
was
m
o
d
elled
with
a
b
ase
v
alu
e
o
f
1
0
0
MV
A,
with
v
o
ltag
e
lev
els
o
f
6
9
k
V
in
ce
r
tain
s
ec
tio
n
s
an
d
1
3
.
8
k
V
in
o
t
h
er
s
.
Prio
r
to
th
e
in
teg
r
atio
n
o
f
win
d
en
er
g
y
u
n
its
,
th
e
s
y
s
tem
ex
p
e
r
ien
ce
d
a
to
tal
ac
tiv
e
p
o
wer
lo
s
s
o
f
1
0
.
5
9
9
0
k
W
.
T
ab
le
1
p
r
o
v
id
es
a
s
id
e
-
by
-
s
id
e
co
m
p
a
r
is
o
n
o
f
th
e
GA
an
d
PS
O
tech
n
iq
u
es.
I
t
illu
s
tr
ates
th
e
ex
ten
t
o
f
p
o
wer
lo
s
s
r
ed
u
ctio
n
a
n
d
th
e
co
r
r
esp
o
n
d
in
g
v
o
ltag
e
s
tab
il
ity
in
d
ices r
esu
ltin
g
f
r
o
m
win
d
tu
r
b
in
e
p
lace
m
en
t
at
v
ar
io
u
s
b
u
s
lo
ca
tio
n
s
.
Ad
d
itio
n
ally
,
th
e
ta
b
le
in
d
icate
s
t
h
e
o
p
tim
al
s
ites
d
eter
m
in
ed
b
y
ea
ch
alg
o
r
ith
m
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
3
,
J
u
n
e
20
2
6
:
6
6
6
-
67
7
670
h
ig
h
lig
h
tin
g
s
u
b
s
tan
tial
im
p
r
o
v
em
en
ts
in
p
o
wer
l
o
s
s
m
itig
ati
o
n
an
d
s
y
s
tem
s
tab
ilit
y
.
T
h
ese
f
in
d
in
g
s
d
em
o
n
s
tr
ate
th
e
e
f
f
ec
tiv
en
ess
o
f
s
tr
ateg
ically
in
co
r
p
o
r
atin
g
win
d
en
e
r
g
y
t
o
en
h
a
n
ce
th
e
p
er
f
o
r
m
an
ce
a
n
d
r
eliab
ilit
y
o
f
elec
tr
ical
p
o
wer
n
etwo
r
k
s
.
T
ab
le
1
.
E
f
f
ec
t o
f
win
d
tu
r
b
in
e
lo
ca
tio
n
o
n
s
y
s
tem
p
o
wer
U
si
n
g
g
e
n
e
t
i
c
a
l
g
o
r
i
t
h
m
U
si
n
g
P
S
O
a
l
g
o
r
i
t
h
m
Th
e
p
r
o
p
e
r
l
o
c
a
t
i
o
n
o
f
w
i
n
d
t
u
r
b
i
n
e
s
3
,
5
,
8
a
n
d
1
3
3
,
6
,
7
a
n
d
9
Th
e
W
T
s
i
z
e
[
M
W
]
[
6
8
2
2
.
6
]
[
5
3
.
9
5
5
]
To
t
a
l
i
n
i
t
i
a
l
l
y
p
o
w
e
r
l
o
s
ses
[
K
W
]
1
0
.
5
9
9
0
1
0
.
5
9
9
0
P
o
w
e
r
l
o
ss
e
s
u
si
n
g
a
l
g
o
r
i
t
h
m [
K
W
]
3
.
4
9
2
5
1
.
6
2
5
1
P
o
w
e
r
sa
v
i
n
g
s [
k
W
]
7
.
1
0
6
5
8
.
9
7
3
9
I
n
i
t
i
a
l
l
y
s
t
a
b
i
l
i
t
y
i
n
d
e
x
2
.
0
4
7
7
2
.
0
4
7
7
W
i
t
h
w
i
n
d
s
t
a
b
i
l
i
t
y
i
n
d
e
x
0
.
9
5
7
7
0
.
6
3
8
4
T
h
e
o
u
tco
m
es f
r
o
m
th
e
PS
O
alg
o
r
ith
m
an
d
GA
alg
o
r
ith
m
ar
e
s
u
m
m
ar
ized
:
PS
O
ex
ec
u
tio
n
r
esu
lt:
T
u
r
b
in
e
s
izes
(
MW):
5
.
0
0
0
0
,
5
.
0
0
0
0
,
5
.
0
0
0
0
,
3
.
9
1
1
6
8
W
in
d
tu
r
b
in
e
lo
ca
tio
n
s
: Bu
s
es
3
,
6
,
7
an
d
9
GA
ex
ec
u
tio
n
r
esu
lt:
T
u
r
b
in
e
s
izes
(
MW):
6
.
0
4
6
0
,
8
.
0
7
5
1
8
,
2
.
1
0
1
0
,
2
.
6
9
1
0
W
in
d
tu
r
b
in
e
lo
ca
tio
n
s
: Bu
s
es
3
,
5
,
8
,
an
d
1
3
T
o
ac
co
u
n
t
f
o
r
th
e
s
to
ch
asti
c
n
atu
r
e
o
f
GA
an
d
PS
O,
s
ev
er
al
in
d
ep
e
n
d
en
t
r
u
n
s
wer
e
c
ar
r
ied
o
u
t
d
u
r
in
g
t
h
e
ex
p
e
r
im
en
tatio
n
p
h
ase.
T
h
e
r
esu
ltin
g
o
p
tim
al
lo
ca
tio
n
s
ex
h
ib
ited
v
e
r
y
s
m
all
v
ar
iab
ilit
y
f
r
o
m
o
n
e
r
u
n
to
a
n
o
th
e
r
,
d
em
o
n
s
tr
atin
g
th
e
r
o
b
u
s
tn
ess
an
d
r
ep
ea
tab
ilit
y
o
f
th
e
s
o
lu
tio
n
s
.
Fo
r
t
h
e
s
ak
e
o
f
clar
ity
a
n
d
to
av
o
id
r
ed
u
n
d
a
n
cy
,
o
n
l
y
th
e
m
o
s
t
r
ep
r
esen
tativ
e
(
b
est)
r
u
n
is
r
ep
o
r
ted
in
th
e
m
ain
tex
t
.
T
h
e
co
r
r
esp
o
n
d
in
g
co
n
v
er
g
en
ce
cu
r
v
es a
r
e
p
r
o
v
i
d
ed
in
th
e
f
o
llo
win
g
F
ig
u
r
e
1
.
Fig
u
r
e
1
.
C
o
n
v
er
g
e
n
ce
cu
r
v
e
o
f
th
e
PS
O
an
d
GA
alg
o
r
ith
m
f
o
r
W
T
p
lace
m
en
t
o
p
tim
izatio
n
T
h
e
Fig
u
r
e
2
p
r
esen
ted
in
th
e
f
o
r
m
o
f
a
r
ad
ar
c
h
ar
t,
illu
s
tr
ates
th
e
im
p
ac
t
o
f
v
a
r
io
u
s
wi
n
d
tu
r
b
in
e
p
lace
m
en
t
s
tr
ateg
ies
o
n
two
k
ey
p
e
r
f
o
r
m
an
ce
in
d
icato
r
s
i
n
a
d
is
tr
ib
u
tio
n
n
etwo
r
k
:
p
o
wer
lo
s
s
es
an
d
th
e
s
tab
ilit
y
in
d
ex
.
T
h
r
ee
s
ce
n
ar
io
s
ar
e
co
m
p
ar
ed
:
th
e
b
ase
ca
s
e
with
o
u
t
o
p
tim
izatio
n
I
E
E
E
1
4
b
u
s
,
o
p
tim
izatio
n
u
s
in
g
th
e
GA,
an
d
o
p
tim
izati
o
n
u
s
in
g
t
h
e
PS
O
alg
o
r
ith
m
.
T
h
e
b
ase
ca
s
e
ex
h
ib
its
th
e
h
ig
h
est
p
o
wer
lo
s
s
es,
r
ea
ch
in
g
1
0
.
5
9
9
0
k
W
,
w
h
ich
h
ig
h
lig
h
ts
th
e
n
ee
d
f
o
r
ef
f
ec
tiv
e
o
p
tim
izatio
n
m
eth
o
d
s
.
Am
o
n
g
t
h
e
ap
p
r
o
ac
h
es,
th
e
PS
O
alg
o
r
ith
m
d
em
o
n
s
tr
a
tes
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
b
y
r
ed
u
cin
g
p
o
wer
lo
s
s
es
s
ig
n
if
ican
tly
to
1
.
6
2
5
1
k
W
an
d
im
p
r
o
v
in
g
th
e
s
y
s
tem
s
tab
ilit
y
in
d
ex
to
0
.
6
3
8
4
.
Alth
o
u
g
h
th
e
GA
also
p
r
o
v
es
ef
f
ec
tiv
e,
its
p
er
f
o
r
m
an
c
e
is
s
lig
h
tly
in
f
er
io
r
to
th
at
o
f
PS
O
in
th
i
s
co
n
f
ig
u
r
atio
n
.
T
h
is
co
m
p
ar
is
o
n
em
p
h
asizes
th
e
im
p
o
r
tan
ce
o
f
s
elec
tin
g
an
ap
p
r
o
p
r
iate
o
p
ti
m
izatio
n
tech
n
iq
u
e
to
en
s
u
r
e
ef
f
icien
t
in
teg
r
atio
n
o
f
win
d
tu
r
b
in
es
in
to
p
o
wer
d
is
tr
ib
u
tio
n
s
y
s
tem
s
,
b
o
th
in
ter
m
s
o
f
en
e
r
g
y
e
f
f
icien
cy
a
n
d
n
etwo
r
k
s
tab
ilit
y
.
T
h
e
F
ig
u
r
e
3
illu
s
tr
ates
th
e
v
o
ltag
e
p
r
o
f
iles
ac
r
o
s
s
d
if
f
er
en
t
b
u
s
es
u
n
d
er
two
d
is
tin
ct
s
ce
n
ar
io
s
.
T
h
e
f
ir
s
t
s
ce
n
ar
io
,
s
h
o
wn
in
r
ed
,
r
ep
r
esen
ts
th
e
n
etwo
r
k
with
o
u
t
an
y
o
p
tim
izatio
n
.
I
n
t
h
is
ca
s
e,
th
e
v
o
ltag
es
at
b
u
s
es
9
an
d
1
0
ar
e
1
.
0
4
[
p
.
u
.
]
an
d
1
.
0
3
8
[
p
.
u
.
]
,
r
esp
ec
tiv
ely
.
I
n
c
o
n
tr
ast,
th
e
s
ec
o
n
d
s
ce
n
ar
io
d
e
p
icted
in
g
r
ee
n
r
ef
lects
th
e
u
s
in
g
o
f
th
e
PS
O
alg
o
r
ith
m
.
Un
d
er
th
is
co
n
f
ig
u
r
atio
n
,
a
s
lig
h
t
im
p
r
o
v
em
en
t
i
n
v
o
ltag
e
lev
els is
o
b
s
er
v
ed
at
th
e
s
am
e
b
u
s
es,
in
cr
ea
s
in
g
to
1
.
0
5
a
n
d
1
.
0
4
8
[
p
.
u
.
]
,
r
esp
ec
tiv
el
y.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Meta
h
eu
r
is
tic
o
p
timiz
a
tio
n
o
f wi
n
d
t
u
r
b
in
e
fa
r
m
s
itin
g
in
p
o
w
er g
r
id
s
:
a
co
mp
a
r
a
tive
…
(
Ta
h
a
R
a
c
h
d
i
)
671
Fig
u
r
e
2
.
C
o
m
p
a
r
ativ
e
an
aly
s
i
s
o
f
W
T
p
lace
m
en
t im
p
ac
t
o
n
p
o
wer
lo
s
s
es a
n
d
n
etwo
r
k
s
tab
ilit
y
u
s
in
g
GA
an
d
PS
O
alg
o
r
ith
m
s
Fig
u
r
e
3
.
Vo
ltag
e
p
r
o
f
ile
an
al
y
s
is
o
f
I
E
E
E
1
4
-
b
u
s
s
y
s
tem
w
ith
o
p
tim
al
win
d
tu
r
b
in
e
p
lace
m
en
t v
ia
PS
O
T
h
e
F
ig
u
r
e
4
illu
s
tr
ates
th
e
s
ec
o
n
d
s
ce
n
ar
io
,
wh
er
e
th
e
GA
wa
s
ap
p
lied
.
T
h
e
v
o
ltag
e
lev
els,
d
is
p
lay
ed
in
g
r
ee
n
,
r
ef
lect
th
e
s
y
s
tem
r
esp
o
n
s
e
f
o
llo
win
g
th
e
p
lace
m
en
t
o
f
th
e
win
d
tu
r
b
in
e
at
its
o
p
tim
al
lo
ca
tio
n
.
I
n
th
e
b
ase
ca
s
e,
th
e
v
o
ltag
es
at
b
u
s
es
4
an
d
5
wer
e
in
itially
1
.
0
2
an
d
1
.
0
2
5
[
p
.
u
.
]
,
r
esp
ec
tiv
ely
.
Fo
llo
win
g
o
p
tim
izatio
n
,
th
ese
v
alu
es in
cr
ea
s
ed
s
lig
h
tly
to
1
.
0
3
an
d
1
.
0
3
5
[
p
.
u
.
]
.
Usi
n
g
th
e
PS
AT
/MAT
L
A
B
p
latf
o
r
m
,
th
e
win
d
tu
r
b
i
n
es
wer
e
s
u
cc
ess
f
u
lly
in
teg
r
ated
in
to
th
e
I
E
E
E
14
-
b
u
s
n
etwo
r
k
,
as
illu
s
tr
ated
in
Fig
u
r
e
4
.
T
h
e
i
n
s
talled
win
d
p
ar
k
s
a
r
e
s
h
o
w
n
in
Fig
u
r
e
5
.
T
h
e
win
d
p
ar
k
s
co
n
n
ec
ted
to
b
u
s
es
3
,
6
,
an
d
7
ea
ch
h
av
e
an
ap
p
ar
e
n
t
p
o
wer
r
atin
g
o
f
5
MV
A,
wh
ile
th
e
p
ar
k
lo
ca
ted
at
b
u
s
9
h
as
an
a
p
p
ar
e
n
t
p
o
wer
o
f
3
.
9
MV
A.
I
n
o
r
d
e
r
to
m
ee
t
s
tan
d
a
r
d
r
e
q
u
ir
em
e
n
ts
,
th
e
co
n
n
ec
tio
n
b
etwe
en
b
u
s
es 0
3
an
d
1
7
was
m
ad
e
u
s
in
g
id
en
t
ical
s
tep
-
d
o
wn
tr
an
s
f
o
r
m
er
s
.
T
h
eir
tech
n
ical
s
p
ec
if
icatio
n
s
ar
e
d
etailed
in
th
e
F
ig
u
r
e
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
3
,
J
u
n
e
20
2
6
:
6
6
6
-
67
7
672
Fig
u
r
e
4
.
Vo
ltag
e
p
r
o
f
ile
an
al
y
s
is
o
f
I
E
E
E
1
4
-
b
u
s
s
y
s
tem
w
ith
o
p
tim
al
win
d
tu
r
b
in
e
p
lace
m
en
t v
ia
GA
Fig
u
r
e
5
.
C
o
n
f
ig
u
r
atio
n
o
f
th
e
I
E
E
E
1
4
-
b
u
s
s
y
s
tem
with
f
o
u
r
in
teg
r
ated
win
d
p
ar
k
s
T
o
clo
s
ely
r
ef
lect
r
ea
l
-
w
o
r
ld
c
o
n
d
itio
n
s
,
th
e
W
eib
u
ll d
is
tr
ib
u
tio
n
was e
m
p
lo
y
ed
in
th
is
s
tu
d
y
u
s
in
g
th
e
PS
AT
to
o
l [
1
8
]
−
[
2
0
]
.
T
h
is
d
is
tr
ib
u
tio
n
is
wid
ely
u
s
ed
t
o
s
tatis
t
ically
m
o
d
el
win
d
s
p
ee
d
v
ar
iab
ilit
y
an
d
is
d
ef
in
ed
b
y
th
e
f
o
llo
win
g
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Meta
h
eu
r
is
tic
o
p
timiz
a
tio
n
o
f wi
n
d
t
u
r
b
in
e
fa
r
m
s
itin
g
in
p
o
w
er g
r
id
s
:
a
co
mp
a
r
a
tive
…
(
Ta
h
a
R
a
c
h
d
i
)
673
(
)
=
(
)
−
1
−
(
)
(
1
4
)
with
:
(
)
:
t
h
e
p
r
o
b
ab
ilit
y
d
e
n
s
ity
f
u
n
cti
o
n
o
f
win
d
s
p
ee
d
,
is
th
e
s
h
ap
e
p
ar
am
eter
a
nd
is
th
e
s
ca
le
p
ar
am
eter
[
2
1
]
−
[
2
5
]
.
T
o
v
alid
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
PS
O
alg
o
r
ith
m
,
a
r
ea
lis
tic
win
d
s
p
ee
d
ev
o
lu
ti
o
n
wa
s
g
en
er
ated
u
s
in
g
th
e
PS
AT
en
v
ir
o
n
m
e
n
t
in
MA
T
L
AB
.
As
i
llu
s
tr
ated
i
n
Fig
u
r
e
7
,
th
e
win
d
s
p
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d
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ile
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r
o
m
th
e
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b
u
tio
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o
p
te
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t
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A
n
o
m
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al
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p
ee
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o
f
1
4
m
/s
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ass
u
m
ed
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ea
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at
a
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er
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u
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it
(
p
.
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.
)
v
alu
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o
f
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c
o
r
r
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d
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T
h
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r
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ltin
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p
r
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ile
ex
h
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its
s
ig
n
if
ican
t
s
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o
r
t
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ter
m
f
lu
ct
u
atio
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s
,
wh
ich
ar
e
ch
ar
ac
ter
is
tic
o
f
n
atu
r
al
win
d
v
ar
iab
ilit
y
.
Su
c
h
tem
p
o
r
al
v
ar
iatio
n
s
p
lay
a
cr
u
cial
r
o
le
,
as
th
e
y
d
ir
ec
tly
in
f
lu
e
n
ce
th
e
d
y
n
am
ic
b
eh
av
io
r
o
f
win
d
en
er
g
y
c
o
n
v
e
r
s
io
n
s
y
s
tem
s
an
d
,
co
n
s
eq
u
en
tly
,
im
p
ac
t
b
o
th
th
e
tr
an
s
ien
t
an
d
s
tead
y
-
s
tate
p
er
f
o
r
m
an
ce
o
f
th
e
elec
tr
ical
n
etwo
r
k
.
I
n
c
o
r
p
o
r
atin
g
th
ese
r
ea
lis
tic
win
d
p
atter
n
s
is
th
er
ef
o
r
e
ess
en
tial
to
ac
cu
r
ately
ass
es
s
th
e
r
o
b
u
s
tn
ess
o
f
th
e
p
o
wer
s
y
s
tem
u
n
d
er
r
en
ewa
b
le
e
n
er
g
y
p
en
et
r
atio
n
.
Fig
u
r
e
6
.
Step
-
d
o
wn
tr
a
n
s
f
o
r
m
er
ch
ar
ac
ter
is
tics
Fig
u
r
e
7
.
W
in
d
s
p
ee
d
p
r
o
f
ile
g
en
er
ated
u
s
in
g
th
e
W
eib
u
ll is
tr
ib
u
tio
n
Fig
u
r
e
8
illu
s
tr
ates
th
e
d
y
n
a
m
ic
v
o
ltag
e
r
esp
o
n
s
e
o
f
t
h
e
I
E
E
E
1
4
-
b
u
s
s
y
s
tem
o
v
e
r
a
2
0
-
s
ec
o
n
d
s
im
u
latio
n
h
o
r
izo
n
.
Fig
u
r
e
8
(
a)
s
h
o
ws
th
e
d
y
n
am
ic
ev
o
lu
ti
o
n
o
f
v
o
ltag
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m
ag
n
itu
d
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at
th
e
1
4
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u
s
es
o
f
th
e
I
E
E
E
1
4
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b
u
s
b
e
n
ch
m
ar
k
s
y
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tem
o
v
er
a
s
im
u
latio
n
h
o
r
iz
o
n
o
f
2
0
s
ec
o
n
d
s
.
T
h
e
n
etwo
r
k
d
e
m
o
n
s
tr
ates
a
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ally
s
tab
le
b
eh
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o
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v
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a
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a
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ty
p
ically
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etwe
en
0
.
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.
0
2
4
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8
10
12
14
16
18
20
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7
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.
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0
.
9
1
1
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1
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2
1
.
3
Ti
m
e
(s
)
[p.
u.
]
W
ei
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l
l
di
s
tri
bu
ti
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n
cur
v
e
W
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n
d
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
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2
I
n
d
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n
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J
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p
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,
Vo
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42
,
No
.
3
,
J
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20
2
6
:
6
6
6
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67
7
674
an
d
1
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wh
ich
f
alls
with
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tab
le
o
p
er
atio
n
al
lim
its
f
o
r
d
is
tr
ib
u
tio
n
s
y
s
tem
s
.
An
in
itial
tr
an
s
ien
t
r
esp
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n
s
e
is
o
b
s
er
v
e
d
d
u
r
in
g
t
h
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f
ir
s
t
in
s
tan
ts
o
f
th
e
s
im
u
l
atio
n
,
co
r
r
esp
o
n
d
in
g
to
th
e
e
s
tab
lis
h
m
en
t
o
f
th
e
s
tead
y
-
s
tate
o
p
er
atin
g
p
o
in
t.
Am
o
n
g
t
h
e
b
u
s
es,
B
u
s
7
r
ea
c
h
es
th
e
h
i
g
h
est
v
o
lta
g
e
lev
el,
ap
p
r
o
x
im
ately
1
.
0
3
5
p
.
u
.
,
a
p
h
en
o
m
e
n
o
n
t
h
at
ca
n
b
e
attr
ib
u
ted
to
its
p
r
o
x
im
it
y
to
a
g
en
er
atio
n
s
o
u
r
ce
o
r
f
av
o
r
a
b
le
n
etwo
r
k
to
p
o
lo
g
y
.
I
n
c
o
n
tr
ast,
b
u
s
es
1
0
,
1
1
,
an
d
1
4
ex
h
ib
it
th
e
l
o
west
v
o
ltag
e
am
p
litu
d
es,
cl
o
s
e
to
0
.
9
9
p
.
u
.
,
a
b
eh
av
io
r
co
m
m
o
n
ly
ass
o
ciate
d
with
p
e
r
ip
h
er
al
b
u
s
es
s
u
b
je
cted
to
h
ig
h
er
lo
ad
o
r
lo
n
g
er
elec
tr
ical
d
is
tan
ce
s
f
r
o
m
g
e
n
er
ato
r
n
o
d
es.
Desp
ite
th
ese
v
ar
iatio
n
s
,
th
e
o
v
e
r
all
v
o
ltag
e
p
r
o
f
ile
r
em
ain
s
well
r
eg
u
lated
,
co
n
f
ir
m
in
g
th
at
th
e
n
etwo
r
k
m
ain
tain
s
s
atis
f
ac
to
r
y
s
tatic
an
d
d
y
n
am
ic
s
tab
ilit
y
u
n
d
er
th
e
s
tu
d
ied
c
o
n
d
i
tio
n
s
.
Fig
u
r
e
8
(
b
)
d
ep
icts
th
e
v
o
lta
g
e
tr
ajec
to
r
ies
at
b
u
s
es
1
5
,
1
6
,
1
7
,
a
n
d
1
8
,
wh
ich
co
r
r
esp
o
n
d
to
th
e
ter
m
in
als
o
f
th
e
in
teg
r
ate
d
win
d
tu
r
b
i
n
e
u
n
its
.
T
h
e
c
u
r
v
es
r
ev
ea
l
a
m
o
r
e
p
r
o
n
o
u
n
ce
d
tr
an
s
ien
t
p
er
io
d
at
th
e
o
n
s
et
o
f
th
e
s
im
u
latio
n
,
p
ar
tic
u
lar
ly
n
o
ticea
b
le
at
b
u
s
1
5
,
w
h
er
e
a
s
ig
n
if
ican
t
i
n
itial
f
lu
ctu
atio
n
o
cc
u
r
s
b
ef
o
r
e
s
tab
ilizatio
n
.
As
th
e
s
y
s
tem
e
v
o
lv
es,
th
e
v
o
ltag
es
at
all
f
o
u
r
b
u
s
es
g
r
ad
u
ally
co
n
v
er
g
e
t
o
war
d
s
s
tead
y
-
s
tate
v
alu
es.
B
u
s
1
7
co
n
s
is
ten
tly
m
ain
tain
s
th
e
h
ig
h
est
an
d
m
o
s
t stab
le
v
o
ltag
e
lev
el,
ar
o
u
n
d
1
.
0
5
p
.
u
.
,
in
d
icatin
g
a
fa
v
o
r
a
b
le
elec
tr
ical
en
v
ir
o
n
m
en
t
o
r
s
tr
o
n
g
co
u
p
lin
g
with
th
e
m
ain
g
r
id
.
C
o
n
v
er
s
ely
,
b
u
s
1
5
s
tab
ilizes
at
th
e
lo
west
v
o
ltag
e
lev
el,
ap
p
r
o
x
i
m
ately
1
.
0
1
p
.
u
.
T
h
e
r
em
ain
in
g
b
u
s
es,
1
6
a
n
d
1
8
,
d
is
p
lay
in
t
er
m
ed
iate
b
e
h
av
io
r
s
with
lim
ited
o
s
cillato
r
y
co
n
te
n
t.
T
h
e
co
llectiv
e
t
r
en
d
s
o
b
s
er
v
ed
in
th
ese
v
o
ltag
e
t
r
ajec
to
r
i
es
d
em
o
n
s
tr
ate
t
h
at
d
esp
ite
th
e
in
h
er
en
t
v
ar
iab
ilit
y
o
f
th
e
win
d
r
eso
u
r
ce
th
e
in
t
eg
r
ated
n
etwo
r
k
is
ca
p
ab
le
o
f
m
ain
tain
in
g
r
eliab
le
v
o
ltag
e
lev
els an
d
ac
h
iev
in
g
a
s
tab
le
o
p
er
atin
g
c
o
n
d
itio
n
.
(
a)
(
b
)
Fig
u
r
e
8
.
Dy
n
am
ic
v
o
ltag
e
r
es
p
o
n
s
e
o
f
t
h
e
s
y
s
tem
:
(
a)
v
o
ltag
e
p
r
o
f
iles
o
f
th
e
I
E
E
E
1
4
-
b
us
d
y
s
tem
an
d
(
b
)
win
d
t
u
r
b
in
e
b
us
5.
CO
NCLU
SI
O
N
I
n
s
u
m
m
ar
y
,
th
is
s
tu
d
y
d
em
o
n
s
tr
ates
th
at
th
e
o
p
tim
al
in
te
g
r
atio
n
o
f
win
d
t
u
r
b
in
es
in
t
o
d
is
tr
ib
u
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n
n
etwo
r
k
s
s
ig
n
if
ican
tly
en
h
an
c
es
s
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tem
p
er
f
o
r
m
an
ce
b
y
r
ed
u
cin
g
ac
tiv
e
p
o
wer
lo
s
s
es
an
d
im
p
r
o
v
in
g
v
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ltag
e
s
tab
ilit
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.
T
h
r
o
u
g
h
a
co
m
p
ar
ativ
e
ap
p
licatio
n
o
f
GA
a
n
d
PS
O,
th
e
r
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n
f
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th
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p
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r
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O
o
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u
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d
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2
4
6
8
10
12
14
16
18
20
0
.
9
5
1
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.
0
5
1
.
1
1
.
1
5
1
.
2
Ti
m
e
(s
)
V
o
l
ta
g
e
ev
o
l
uti
o
n
[p.
u.
]
W
i
nd
tur
bi
ne
v
o
l
ta
g
e
ev
o
l
uti
o
n
V
Bu
s
15
V
Bu
s
16
V
Bu
s
17
V
Bu
s
18
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Meta
h
eu
r
is
tic
o
p
timiz
a
tio
n
o
f wi
n
d
t
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itin
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in
p
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r
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Ta
h
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R
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d
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ce
.
T
h
e
wo
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k
in
tr
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d
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ce
s
s
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k
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izatio
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k
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v
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tab
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ased
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n
th
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ce
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J
ac
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T
h
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f
in
d
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n
g
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ar
e
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ir
ec
tly
r
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n
t
to
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tr
ical
en
g
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r
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th
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t
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r
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t
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e
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d
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t
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m
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g
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n
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g
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Fin
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th
e
s
tu
d
y
h
ig
h
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th
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p
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h
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UPFC
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u
m
m
ar
y
,
th
is
s
tu
d
y
h
as
s
h
o
wn
th
at
th
e
o
p
tim
al
in
teg
r
atio
n
o
f
win
d
tu
r
b
in
es
in
to
d
is
tr
ib
u
tio
n
n
etwo
r
k
s
ca
n
s
ig
n
if
ican
tly
en
h
an
ce
s
y
s
tem
p
er
f
o
r
m
a
n
ce
b
y
r
ed
u
cin
g
ac
tiv
e
p
o
wer
lo
s
s
es
an
d
im
p
r
o
v
in
g
v
o
ltag
e
s
tab
ilit
y
.
T
h
r
o
u
g
h
th
e
ap
p
licatio
n
o
f
two
m
etah
eu
r
is
tic
o
p
tim
izatio
n
m
eth
o
d
s
GA
an
d
PS
O
th
e
r
esu
lts
d
em
o
n
s
tr
ate
th
e
ad
v
a
n
tag
e
o
f
h
e
u
r
is
tic
-
b
ased
tech
n
iq
u
es
o
v
e
r
co
n
v
en
tio
n
al
an
aly
tical
ap
p
r
o
a
ch
es,
with
PS
O
p
r
o
v
id
in
g
s
u
p
er
io
r
r
o
b
u
s
tn
ess
an
d
f
aster
co
n
v
er
g
en
ce
.
T
h
is
wo
r
k
co
n
tr
ib
u
tes
s
ev
er
a
l
s
cien
tific
ad
v
an
ce
s
:
a
s
im
u
ltan
eo
u
s
s
itin
g
-
an
d
-
s
izin
g
o
p
t
im
izatio
n
f
r
am
ewo
r
k
f
o
r
win
d
tu
r
b
in
es,
th
e
u
s
e
o
f
an
a
d
v
an
ce
d
s
tab
ilit
y
in
d
ex
d
er
iv
ed
f
r
o
m
th
e
r
ed
u
ce
d
Q
–
V
J
ac
o
b
ian
an
d
a
q
u
a
n
titativ
e
co
m
p
ar
is
o
n
o
f
GA
an
d
PS
O
o
n
th
e
I
E
E
E
1
4
-
b
u
s
s
y
s
tem
.
T
h
e
f
in
d
in
g
s
h
av
e
clea
r
r
elev
an
ce
f
o
r
elec
tr
ical
e
n
g
in
ee
r
i
n
g
p
r
a
ctice
.
L
o
s
s
r
ed
u
ctio
n
an
d
im
p
r
o
v
ed
v
o
ltag
e
s
tab
ilit
y
d
ir
ec
tl
y
en
h
a
n
ce
n
etwo
r
k
r
eliab
ilit
y
an
d
p
o
wer
q
u
ality
,
wh
ile
th
e
p
r
o
p
o
s
ed
o
p
ti
m
izatio
n
s
tr
ateg
y
s
u
p
p
o
r
ts
s
m
ar
ter
p
lan
n
in
g
o
f
r
en
ewa
b
le
in
teg
r
atio
n
in
f
u
tu
r
e
d
is
tr
ib
u
tio
n
n
etwo
r
k
s
.
L
o
o
k
i
n
g
ah
ea
d
,
co
m
b
in
in
g
r
en
ewa
b
l
e
en
er
g
y
u
n
its
with
FAC
T
S
d
ev
ices
s
u
ch
as
STAT
C
OM
o
r
UPFC
r
ep
r
esen
ts
a
p
r
o
m
is
in
g
d
ir
ec
tio
n
f
o
r
s
tr
en
g
th
en
i
n
g
v
o
ltag
e
co
n
tr
o
l,
p
o
wer
f
l
o
w
r
eg
u
latio
n
,
an
d
o
v
er
all
s
m
ar
t g
r
id
f
lex
ib
i
lity
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
e
f
ir
s
t
au
th
o
r
wo
u
ld
lik
e
to
ex
ten
d
h
ea
r
tf
elt
th
an
k
s
to
t
h
e
en
tire
r
esear
ch
team
at
L
T
I
,
C
u
f
f
ies,
Fra
n
ce
,
f
o
r
t
h
eir
in
v
al
u
ab
le
ass
is
tan
ce
an
d
s
u
p
p
o
r
t
th
r
o
u
g
h
o
u
t
th
e
co
u
r
s
e
o
f
th
is
wo
r
k
.
T
h
eir
e
x
p
er
tis
e,
g
u
id
an
ce
,
an
d
c
o
llab
o
r
ativ
e
s
p
ir
it
s
ig
n
if
ican
tly
co
n
t
r
ib
u
ted
to
th
e
d
ev
el
o
p
m
en
t
a
n
d
s
u
cc
e
s
s
o
f
th
is
r
esear
ch
.
T
h
e
au
t
h
o
r
d
ee
p
l
y
a
p
p
r
ec
iate
s
th
e
in
s
ig
h
tf
u
l
d
is
cu
s
s
io
n
s
,
t
ec
h
n
ical
ad
v
ice,
a
n
d
en
co
u
r
a
g
em
en
t
p
r
o
v
id
ed
b
y
th
e
team
,
wh
ich
wer
e
in
s
tr
u
m
en
tal
in
o
v
er
co
m
i
n
g
v
a
r
io
u
s
ch
allen
g
es
en
co
u
n
ter
ed
d
u
r
in
g
th
e
s
tu
d
y
.
T
h
e
co
n
tr
ib
u
tio
n
s
o
f
ea
ch
m
em
b
e
r
o
f
th
e
L
T
I
team
h
av
e
b
ee
n
tr
u
ly
in
d
is
p
en
s
ab
le,
an
d
th
eir
co
m
m
itm
en
t
to
ex
ce
llen
ce
h
as g
r
ea
tly
e
n
r
ich
e
d
th
is
p
r
o
ject.
RE
F
E
R
E
NC
E
S
[
1
]
B
.
N
.
A
l
a
j
m
i
,
M
.
F
.
A
l
H
a
j
r
i
,
N
.
A
.
A
h
m
e
d
,
I
.
A
b
d
e
l
sal
a
m,
a
n
d
M
.
I
.
M
a
r
e
i
,
“
M
u
l
t
i
-
o
b
j
e
c
t
i
v
e
o
p
t
i
mi
z
a
t
i
o
n
o
f
o
p
t
i
m
a
l
p
l
a
c
e
me
n
t
a
n
d
s
i
z
i
n
g
o
f
d
i
s
t
r
i
b
u
t
e
d
g
e
n
e
r
a
t
o
r
s
i
n
d
i
s
t
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s,”
I
EE
J
T
r
a
n
s
a
c
t
i
o
n
s
o
n
El
e
c
t
ri
c
a
l
a
n
d
El
e
c
t
ro
n
i
c
En
g
i
n
e
e
ri
n
g
,
v
o
l
.
1
8
,
n
o
.
6
,
p
p
.
8
1
7
–
8
3
3
,
M
a
y
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
2
/
t
e
e
.
2
3
7
8
4
.
[
2
]
G
.
K
o
e
p
p
e
l
,
“
D
i
s
t
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
:
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
a
n
d
o
u
t
l
i
n
e
o
f
t
h
e
sw
i
ss si
t
u
a
t
i
o
n
,
”
ETH
Z
u
r
i
c
h
Re
s
e
a
r
c
h
C
o
l
l
e
c
t
i
o
n
,
p
p
.
1
–
1
9
,
2
0
0
3
,
d
o
i
:
1
0
.
3
9
2
9
/
e
t
h
z
-
a
-
0
0
4
6
1
9
0
4
2
.
[
3
]
N
.
S
i
n
g
h
,
M
.
A
.
A
n
sari
,
M
.
Tr
i
p
a
t
h
y
,
a
n
d
V
.
P
.
S
i
n
g
h
,
“
F
e
a
t
u
r
e
e
x
t
r
a
c
t
i
o
n
a
n
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
t
e
c
h
n
i
q
u
e
s
f
o
r
p
o
w
e
r
q
u
a
l
i
t
y
d
i
s
t
u
r
b
a
n
c
e
s
i
n
d
i
st
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
:
a
r
e
v
i
e
w
,
”
I
ETE
J
o
u
r
n
a
l
o
f
R
e
se
a
rc
h
,
v
o
l
.
6
9
,
n
o
.
6
,
p
p
.
3
8
3
6
–
3
8
5
1
,
A
u
g
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
8
0
/
0
3
7
7
2
0
6
3
.
2
0
2
1
.
1
9
2
0
8
4
9
.
[
4
]
K
.
T
u
i
t
e
mw
o
n
g
a
n
d
S
.
P
r
e
mr
u
d
e
e
p
r
e
e
c
h
a
c
h
a
r
n
,
“
Ex
p
e
r
t
sy
st
e
m fo
r
p
r
o
t
e
c
t
i
o
n
c
o
o
r
d
i
n
a
t
i
o
n
o
f
d
i
s
t
r
i
b
u
t
i
o
n
sy
s
t
e
m w
i
t
h
d
i
st
r
i
b
u
t
e
d
g
e
n
e
r
a
t
o
r
s
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
t
ri
c
a
l
P
o
w
e
r
a
n
d
E
n
e
r
g
y
S
y
st
e
m
s
,
v
o
l
.
3
3
,
n
o
.
3
,
p
p
.
4
6
6
–
4
7
1
,
M
a
r
.
2
0
1
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
e
p
e
s.
2
0
1
0
.
1
0
.
0
0
9
.
[
5
]
K
.
B
a
sara
n
,
A
.
Ç
e
l
i
k
t
e
n
,
a
n
d
H
.
B
u
l
u
t
,
“
A
s
h
o
r
t
-
t
e
r
m
p
h
o
t
o
v
o
l
t
a
i
c
o
u
t
p
u
t
p
o
w
e
r
f
o
r
e
c
a
s
t
i
n
g
b
a
s
e
d
o
n
e
n
s
e
m
b
l
e
a
l
g
o
r
i
t
h
ms
u
s
i
n
g
h
y
p
e
r
p
a
r
a
me
t
e
r
o
p
t
i
m
i
z
a
t
i
o
n
,
”
E
l
e
c
t
r
i
c
a
l
E
n
g
i
n
e
e
r
i
n
g
,
v
o
l
.
1
0
6
,
n
o
.
5
,
p
p
.
5
3
1
9
–
5
3
3
7
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
0
7
/
s0
0
2
0
2
-
0
2
4
-
0
2
2
8
1
-
3.
[
6
]
B
.
M
o
d
u
,
M
.
P
.
A
b
d
u
l
l
a
h
,
M
.
A
.
S
a
n
u
si
,
a
n
d
M
.
F
.
H
a
mza
,
“
D
C
-
b
a
s
e
d
m
i
c
r
o
g
r
i
d
:
T
o
p
o
l
o
g
i
e
s,
c
o
n
t
r
o
l
s
c
h
e
mes
,
a
n
d
i
mp
l
e
m
e
n
t
a
t
i
o
n
s,”
Al
e
x
a
n
d
r
i
a
E
n
g
i
n
e
e
ri
n
g
J
o
u
r
n
a
l
,
v
o
l
.
7
0
,
p
p
.
6
1
–
9
2
,
M
a
y
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
e
j
.
2
0
2
3
.
0
2
.
0
2
1
.
[
7
]
W
.
Z
h
e
n
g
,
H
.
Lu
,
M
.
Z
h
a
n
g
,
Q
.
W
u
,
Y
.
H
o
u
,
a
n
d
J.
Z
h
u
,
“
D
i
st
r
i
b
u
t
e
d
e
n
e
r
g
y
m
a
n
a
g
e
me
n
t
o
f
m
u
l
t
i
-
e
n
t
i
t
y
i
n
t
e
g
r
a
t
e
d
e
l
e
c
t
r
i
c
i
t
y
a
n
d
h
e
a
t
s
y
st
e
ms
:
a
r
e
v
i
e
w
o
f
a
r
c
h
i
t
e
c
t
u
r
e
s,
o
p
t
i
m
i
z
a
t
i
o
n
a
l
g
o
r
i
t
h
ms
,
a
n
d
p
r
o
s
p
e
c
t
s
,
”
I
EEE
T
r
a
n
s
a
c
t
i
o
n
s
o
n
S
m
a
rt
G
ri
d
,
v
o
l
.
1
5
,
n
o
.
2
,
p
p
.
1
5
4
4
–
1
5
6
1
,
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
TSG
.
2
0
2
3
.
3
3
1
0
9
4
7
.
[
8
]
P
.
U
sh
a
sh
r
e
e
a
n
d
K
.
S
.
K
u
mar,
“
P
o
w
e
r
sy
s
t
e
m
r
e
c
o
n
f
i
g
u
r
a
t
i
o
n
i
n
d
i
st
r
i
b
u
t
i
o
n
s
y
s
t
e
m
f
o
r
l
o
s
s
m
i
n
i
mi
z
a
t
i
o
n
u
si
n
g
o
p
t
i
mi
z
a
t
i
o
n
t
e
c
h
n
i
q
u
e
s:
a
r
e
v
i
e
w
,
”
W
i
re
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