I
nte
rna
t
io
na
l J
o
urna
l o
f
Appl
ied P
o
wer
E
ng
i
neer
ing
(
I
J
AP
E
)
Vo
l.
1
4
,
No
.
1
,
Ma
r
ch
2
0
2
5
,
p
p
.
1
55
~1
62
I
SS
N:
2
2
5
2
-
8
7
9
2
,
DOI
: 1
0
.
1
1
5
9
1
/ijap
e.
v
1
4
.
i1
.
p
p
1
55
-
1
62
155
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
a
p
e.
ia
esco
r
e.
co
m
O
ptima
l
distrib
ut
ed genera
tor p
la
cement
for lo
ss
red
uction
using
f
uzzy
and
ada
ptive g
rey wo
lf
a
lg
o
rithm
Da
ruru
Sa
rik
a
1
,
P
a
lepu Su
re
s
h B
a
b
u
1
,
P
a
s
a
la
G
o
pi
1
,
M
a
nu
bo
lu Da
m
o
da
r
Reddy
2
,
Su
re
s
h
B
a
bu
P
o
t
la
du
rt
y
3
1
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
i
c
a
l
a
n
d
El
e
c
t
r
o
n
i
c
s E
n
g
i
n
e
e
r
i
n
g
,
A
n
n
a
m
a
c
h
a
r
y
a
U
n
i
v
e
r
si
t
y
,
R
a
j
a
m
p
e
t
,
I
n
d
i
a
2
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
i
c
a
l
a
n
d
El
e
c
t
r
o
n
i
c
s E
n
g
i
n
e
e
r
i
n
g
,
S
r
i
V
e
n
k
a
t
e
sw
a
r
a
U
n
i
v
e
r
s
i
t
y
C
o
l
l
e
g
e
o
f
E
n
g
i
n
e
e
r
i
n
g
,
Ti
r
u
p
a
t
i
,
I
n
d
i
a
3
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
o
n
i
c
s a
n
d
C
o
m
mu
n
i
c
a
t
i
o
n
En
g
i
n
e
e
r
i
n
g
,
S
r
i
V
e
n
k
a
t
e
s
w
a
r
a
C
o
l
l
e
g
e
o
f
En
g
i
n
e
e
r
i
n
g
,
Ti
r
u
p
a
t
i
,
I
n
d
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
6
,
2
0
2
4
R
ev
is
ed
Sep
9
,
2
0
2
4
Acc
ep
ted
Oct
2
3
,
2
0
2
4
Th
is
re
se
a
rc
h
p
ro
v
id
e
s
a
n
e
w
m
e
th
o
d
o
lo
g
y
f
o
r
lo
c
a
ti
n
g
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
(
DG
)
u
n
it
s
i
n
d
istri
b
u
ti
o
n
e
lec
tri
c
a
l
n
e
two
r
k
s
u
ti
li
z
in
g
th
e
fu
z
z
y
a
n
d
a
d
a
p
ti
v
e
g
re
y
wo
lf
o
p
ti
m
iza
ti
o
n
a
lg
o
r
it
h
m
(AG
WOA) t
o
d
e
c
re
a
se
p
o
we
r
lo
ss
e
s
a
n
d
e
n
h
a
n
c
e
th
e
v
o
lt
a
g
e
p
ro
fil
e
.
E
v
e
ry
d
a
y
li
v
in
g
re
li
e
s
h
e
a
v
il
y
o
n
e
lec
tri
c
a
l
e
n
e
rg
y
.
Th
e
p
r
o
m
o
ti
o
n
o
f
g
e
n
e
ra
ti
n
g
e
lec
tri
c
a
l
p
o
we
r
fro
m
re
n
e
wa
b
le
e
n
e
rg
y
so
u
rc
e
s
su
c
h
a
s
win
d
,
ti
d
a
l
wa
v
e
,
a
n
d
so
lar
e
n
e
rg
y
h
a
s
a
rise
n
d
u
e
to
t
h
e
sig
n
ifi
c
a
n
t
v
a
l
u
e
p
lac
e
d
o
n
a
ll
p
ro
s
p
e
c
ti
v
e
e
n
e
rg
y
so
u
rc
e
s
c
a
p
a
b
le
o
f
p
r
o
d
u
c
in
g
i
t.
Th
e
re
h
a
s
b
e
e
n
su
b
sta
n
ti
a
l
re
se
a
rc
h
o
n
i
n
teg
ra
ti
n
g
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
i
n
to
th
e
e
l
e
c
tri
c
it
y
sy
ste
m
d
u
e
to
th
e
g
r
o
wi
n
g
in
tere
st
in
re
n
e
wa
b
le
s
o
u
rc
e
s
i
n
re
c
e
n
t
y
e
a
rs.
T
h
e
p
rima
ry
re
a
so
n
f
o
r
a
d
d
i
n
g
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
so
u
rc
e
s
fo
r
th
e
n
e
two
r
k
is
to
s
u
p
p
ly
a
n
e
t
q
u
a
n
ti
t
y
o
f
p
o
we
r,
l
o
we
rin
g
p
o
we
r
l
o
ss
e
s.
D
e
term
in
in
g
th
e
a
m
o
u
n
t
a
n
d
l
o
c
a
ti
o
n
o
f
lo
c
a
l
g
e
n
e
ra
ti
o
n
is
c
ru
c
ial
f
o
r
re
d
u
c
in
g
t
h
e
li
n
e
lo
ss
e
s
o
f
p
o
we
r
sy
ste
m
s.
Nu
m
e
ro
u
s
st
u
d
ies
h
a
v
e
b
e
e
n
c
o
n
d
u
c
te
d
t
o
d
e
term
in
e
t
h
e
b
e
st
lo
c
a
ti
o
n
f
o
r
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
.
In
th
is
stu
d
y
,
DG
u
n
it
p
lac
e
m
e
n
t
is
d
e
term
in
e
d
u
sin
g
a
fu
z
z
y
tec
h
n
iq
u
e
.
In
c
o
n
tras
t,
p
h
o
t
o
v
o
lt
a
ic
(
PV
)
a
n
d
c
a
p
a
c
it
o
r
p
lac
e
m
e
n
t
a
n
d
siz
e
a
re
d
e
term
in
e
d
sim
u
lt
a
n
e
o
u
sl
y
u
sin
g
a
n
a
d
a
p
ti
v
e
g
re
y
wo
lf
tec
h
n
iq
u
e
b
a
se
d
o
n
th
e
c
u
n
n
i
n
g
b
e
h
a
v
i
o
r
o
f
wo
l
v
e
s.
Th
e
p
r
o
p
o
se
d
m
e
th
o
d
is
d
e
v
e
lo
p
e
d
u
sin
g
t
h
e
M
AT
LAB
p
ro
g
ra
m
m
in
g
lan
g
u
a
g
e
;
th
e
re
su
lt
s
a
re
th
e
n
p
ro
v
id
e
d
a
fter t
e
stin
g
o
n
tes
t
sy
st
e
m
s with
3
3
-
b
u
s a
n
d
1
5
-
b
u
s.
K
ey
w
o
r
d
s
:
Allo
ca
tio
n
o
f
DG
Fu
zz
y
tech
n
iq
u
e
Gr
ey
wo
lf
tech
n
i
q
u
e
Po
wer
lo
s
s
Vo
ltag
e
-
p
r
o
f
ile
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Palep
u
Su
r
esh
B
ab
u
Dep
ar
tm
en
t o
f
E
lectr
ical
an
d
E
lectr
o
n
ics E
n
g
in
ee
r
i
n
g
,
An
n
am
ac
h
ar
y
a
Un
i
v
er
s
ity
R
ajam
p
et,
An
d
h
r
a
Pra
d
esh
,
I
n
d
ia
E
m
ail:
s
u
r
esh
r
am
4
8
@
g
m
ail.
c
o
m
1.
I
NT
RO
D
UCT
I
O
N
Sm
all
-
s
ca
le
g
en
er
atio
n
s
itu
ated
at
o
r
clo
s
e
to
th
e
lo
a
d
ce
n
ter
s
is
r
ef
er
r
ed
to
as
"
d
is
tr
ib
u
ted
g
en
er
atio
n
"
[
1
]
.
I
n
th
e
ev
o
lv
i
n
g
lan
d
s
ca
p
e
o
f
p
o
wer
s
y
s
tem
s
,
d
is
tr
ib
u
ted
g
e
n
er
atio
n
(
D
G)
h
as
em
er
g
e
d
as
a
s
ig
n
if
ican
t
co
m
p
o
n
e
n
t
in
en
h
an
cin
g
t
h
e
r
eliab
ilit
y
,
ef
f
icien
cy
,
an
d
s
u
s
tain
ab
ilit
y
o
f
elec
t
r
icity
n
etwo
r
k
s
.
DG
in
v
o
lv
es
th
e
p
lace
m
en
t
o
f
s
m
all
-
s
ca
le
p
o
wer
g
en
er
atio
n
u
n
its
clo
s
e
to
th
e
lo
ad
ce
n
ter
s
,
p
r
o
v
id
in
g
n
u
m
e
r
o
u
s
b
en
ef
its
in
clu
d
in
g
lo
s
s
r
ed
u
c
tio
n
,
v
o
ltag
e
im
p
r
o
v
em
en
t,
a
n
d
d
ef
e
r
r
al
o
f
s
y
s
tem
u
p
g
r
a
d
es.
Ho
wev
er
,
th
e
o
p
tim
al
p
lace
m
en
t
an
d
s
izin
g
o
f
th
ese
g
e
n
er
ato
r
s
ar
e
cr
u
cial
to
m
ax
im
ize
t
h
eir
p
o
ten
tial
b
en
ef
its
.
Dis
tr
ib
u
ted
en
er
g
y
,
d
ec
en
tr
alize
d
e
n
er
g
y
,
em
b
ed
d
e
d
en
er
g
y
,
o
n
-
s
ite
g
en
er
atio
n
,
s
ca
tter
ed
g
en
er
ati
o
n
,
an
d
d
is
p
er
s
ed
en
er
g
y
h
av
e
all
b
ee
n
o
th
e
r
n
a
m
es.
T
h
er
e
ar
e
n
u
m
er
o
u
s
s
m
all
-
s
ca
le
p
o
wer
g
en
er
atio
n
m
et
h
o
d
s
u
s
ed
f
o
r
d
is
tr
ib
u
ted
g
en
e
r
atio
n
.
R
eg
ar
d
less
o
f
wh
eth
er
th
ese
tech
n
o
lo
g
ies
ar
e
lin
k
ed
to
th
e
elec
tr
ical
n
etwo
r
k
,
to
in
cr
ea
s
e
th
e
ef
f
ec
tiv
en
ess
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
4
,
No
.
1
,
Ma
r
ch
2
0
2
5
:
1
55
-
1
62
156
th
e
elec
tr
icity
d
is
tr
ib
u
tio
n
n
etwo
r
k
[
2
]
,
[
3
]
,
a
d
i
v
er
s
e
ar
r
ay
o
f
co
m
p
ac
t,
m
o
d
u
lar
p
o
wer
g
e
n
er
atio
n
tech
n
o
lo
g
ies
ar
e
r
ef
er
r
ed
to
as
"d
is
tr
ib
u
ted
en
er
g
y
r
eso
u
r
ce
s
"
(
DE
R
)
.
T
h
ese
tech
n
o
lo
g
ies
ca
n
b
e
em
p
lo
y
e
d
in
co
n
ju
n
ctio
n
with
en
er
g
y
s
to
r
a
g
e
an
d
m
an
a
g
em
en
t
s
y
s
tem
s
.
B
ec
au
s
e
p
o
wer
is
g
en
er
ated
r
e
lativ
ely
clo
s
e
p
o
in
t
to
th
e
lo
ad
,
o
cc
asio
n
ally
e
v
en
in
s
id
e
th
e
s
am
e
ca
s
e,
d
is
p
er
s
ed
g
en
er
atio
n
is
a
tech
n
iq
u
e
t
h
at
lo
wer
s
th
e
v
alu
e
o
f
p
o
wer
lo
s
t
in
elec
tr
icity
tr
an
s
m
is
s
io
n
.
Ad
d
itio
n
ally
,
f
ewe
r
an
d
s
m
aller
elec
tr
ical
ca
b
les
n
ee
d
to
b
e
b
u
ilt.
DG
u
n
it p
lace
m
en
t h
as b
ee
n
t
h
e
s
u
b
ject
o
f
e
x
ten
s
iv
e
in
v
esti
g
atio
n
.
T
h
e
g
o
al
o
f
th
e
DG
lo
ca
tio
n
ch
allen
g
e
is
to
c
h
o
o
s
e
th
e
p
o
s
itio
n
s
an
d
d
im
en
s
io
n
s
o
f
th
e
DGs
to
r
ed
u
ce
p
o
wer
lo
s
s
.
E
v
e
n
th
o
u
g
h
o
p
tim
al
DG
p
lace
m
en
t
h
as b
ee
n
th
e
s
u
b
ject
o
f
a
s
izab
le
am
o
u
n
t o
f
s
tu
d
y
[
4
]
-
[
1
4
]
,
m
o
r
e
ac
ce
p
ta
b
le
an
d
ef
f
icien
t
s
o
lu
tio
n
s
s
till
n
ee
d
to
b
e
d
ev
elo
p
ed
.
T
h
er
e
ar
e
p
r
ac
ti
ca
l
s
o
lu
tio
n
s
to
t
h
e
o
p
tim
al
DG
p
lace
m
en
t
ch
allen
g
e.
T
h
eir
ef
f
ec
tiv
e
n
ess
is
en
t
ir
ely
d
ep
en
d
e
n
t
o
n
h
o
w
well
th
e
d
ata
is
co
llected
.
T
h
e
u
s
e
o
f
a
f
u
zz
y
tech
n
iq
u
e
co
r
r
ec
ts
an
y
d
ata
th
at
lack
s
u
n
ce
r
tain
ty
.
T
h
e
ad
v
a
n
tag
e
o
f
th
e
f
u
zz
y
-
ap
p
r
o
ac
h
is
th
at
it
m
ay
r
ef
lect
en
g
in
ee
r
in
g
d
ec
is
io
n
s
an
d
in
c
o
r
p
o
r
at
e
h
eu
r
is
tics
in
to
th
e
p
r
o
b
le
m
o
f
o
p
tim
al
DG
p
lace
m
en
t.
I
t
is
s
im
p
le
to
as
s
ess
th
e
r
esu
lts
o
f
a
f
u
zz
y
tech
n
iq
u
e
to
f
i
n
d
th
e
b
est
DG
p
lace
m
en
ts
.
T
h
e
ap
p
r
o
p
r
iate
DG
s
izes
ca
n
b
e
o
b
tain
ed
m
o
r
e
ef
f
ec
tiv
ely
u
s
i
n
g
th
e
g
lo
b
al
o
p
tim
izat
io
n
m
eth
o
d
.
On
e
o
f
th
e
n
ewe
s
t
m
etah
eu
r
is
tic
tech
n
iq
u
es
in
all
tech
n
ical
d
o
m
ain
s
i
s
th
e
g
r
ey
wo
lf
alg
o
r
ith
m
(
G
W
A)
[
1
5
]
-
[
1
7
]
.
T
h
e
f
u
zz
y
tech
n
iq
u
e
d
ev
elo
p
ed
b
y
Pra
s
ad
et
a
l
.
[
1
0
]
an
d
R
ah
ar
jo
et
a
l
.
[
1
1
]
is
em
p
lo
y
e
d
in
th
e
f
ir
s
t
s
tag
e
to
d
eter
m
in
e
th
e
b
est DG
lo
ca
tio
n
s
.
T
h
e
ad
ap
tiv
e
g
r
e
y
wo
lf
alg
o
r
ith
m
(
AGWA)
is
u
tili
ze
d
in
th
e
s
ec
o
n
d
s
tag
e
to
ch
o
o
s
e
th
e
id
ea
l
DG
s
izes
[
1
8
]
.
T
h
e
r
esu
lts
o
f
test
in
g
th
e
s
u
g
g
ested
s
tr
ateg
y
o
n
test
s
y
s
tem
s
with
1
5
an
d
3
3
b
u
s
es a
r
e
r
ep
o
r
ted
.
2.
P
RO
B
L
E
M
F
O
R
M
U
L
AT
I
O
N
T
h
e
en
tire
ac
tiv
e
p
o
wer
-
lo
s
s
(
P
L
)
in
a
d
is
tr
ib
u
tio
n
n
et
wo
r
k
h
a
v
in
g
‘
n
’
n
u
m
b
er
o
f
lin
es
is
g
iv
en
b
y
(
1
)
.
=
∑
2
=
1
(
1
)
Her
e,
‘
I
i
’
d
en
o
tes
th
e
s
ize
o
f
th
e
i
th
lin
e
cu
r
r
en
t,
a
n
d
‘
R
i
’
d
en
o
tes
its
r
esis
tan
ce
,
r
esp
ec
tiv
ely
.
T
h
e
lo
ad
f
lo
w
s
o
lu
tio
n
ca
n
b
e
u
s
ed
to
d
eter
m
in
e
th
e
b
r
an
c
h
cu
r
r
en
t.
T
h
e
ac
tu
al
co
m
p
o
n
e
n
t
(
I
a
)
a
n
d
i
m
ag
in
ar
y
co
m
p
o
n
e
n
t
ar
e
th
e
two
h
al
v
es
o
f
th
e
b
r
a
n
ch
c
u
r
r
en
t
(
I
r
)
.
T
h
e
lo
s
s
co
n
n
ec
ted
to
th
e
r
ea
ctiv
e
a
n
d
ac
tiv
e
p
ar
ts
o
f
b
r
an
c
h
cu
r
r
en
ts
is
ex
p
r
ess
ed
as
(
2
)
an
d
(
3
)
.
=
∑
2
=
1
(
2
)
=
∑
2
=
1
(
3
)
B
ec
au
s
e
all
ac
tiv
e
p
o
wer
m
u
s
t
co
m
e
f
r
o
m
th
e
s
o
u
r
ce
s
o
f
th
e
r
o
o
t b
u
s
,
th
e
lo
s
s
v
alu
e
‘
P
L
a’
c
o
n
s
id
er
ed
with
th
e
r
ea
l
co
m
p
o
n
en
t o
f
lin
e
c
u
r
r
en
t
s
ca
n
n
o
t
b
e
d
ec
r
ea
s
ed
f
o
r
o
n
e
s
o
u
r
ce
r
ad
ial
lin
e.
T
h
e
p
o
wer
l
o
s
s
‘
P
Lr
’
r
elate
d
to
th
e
im
ag
in
ar
y
c
o
m
p
o
n
en
t
o
f
l
in
e
cu
r
r
e
n
ts
ca
n
b
e
r
e
d
u
ce
d
b
y
lo
ca
lly
s
u
p
p
ly
in
g
s
o
m
e
o
f
th
e
r
ea
ctiv
e
p
o
we
r
d
em
an
d
.
T
h
is
s
tu
d
y
o
u
tlin
es
a
m
eth
o
d
o
lo
g
y
th
at,
b
y
p
o
s
itio
n
in
g
th
e
ca
p
ac
ito
r
s
id
ea
lly
,
m
in
im
izes
th
e
lo
s
s
ca
u
s
ed
b
y
th
e
r
ea
ctiv
e
c
o
m
p
o
n
en
t
o
f
th
e
b
r
an
ch
c
u
r
r
en
t
an
d
,
as
a
r
esu
lt,
r
e
d
u
ce
s
th
e
o
v
er
all
lo
s
s
o
f
th
e
d
is
tr
ib
u
tio
n
s
y
s
tem
.
3.
F
UZ
Z
Y
AP
P
RO
A
CH
F
O
R
I
DE
N
T
I
F
I
CA
T
I
O
N
O
F
O
P
T
I
M
AL
D
I
S
T
RI
B
U
T
E
D
G
E
NE
RATOR
L
O
CAT
I
O
NS
T
h
is
wo
r
k
u
s
ed
th
e
f
u
zz
y
tec
h
n
iq
u
e
s
u
g
g
ested
in
[
8
]
,
Go
p
i
et
a
l.
[1
2
]
t
o
ch
o
o
s
e
ap
p
r
o
p
r
iate
p
lace
s
f
o
r
DG
in
s
tallatio
n
.
T
w
o
g
o
als
ar
e
co
n
s
id
er
e
d
wh
en
cr
ea
tin
g
a
f
u
zz
y
m
eth
o
d
f
o
r
p
in
p
o
in
t
in
g
th
e
i
d
ea
l
s
itin
g
f
o
r
DGs.
T
h
e
two
g
o
als
ar
e
to
i)
r
ed
u
ce
ac
tu
al
p
o
wer
lo
s
s
as
m
u
ch
as
p
o
s
s
ib
le
an
d
ii)
k
ee
p
th
e
v
o
ltag
e
with
in
allo
wab
le
r
an
g
es.
Dis
tr
ib
u
tio
n
s
y
s
tem
p
o
wer
l
o
s
s
in
d
ice
s
an
d
n
o
d
e
v
o
ltag
es
ar
e
m
o
d
eled
u
s
in
g
f
u
zz
y
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
.
E
v
er
y
n
o
d
e
in
t
h
e
d
is
tr
ib
u
tio
n
n
etwo
r
k
'
s
s
u
itab
ilit
y
f
o
r
DG
p
lace
m
en
t
is
th
en
ev
alu
ated
u
s
in
g
a
f
u
zz
y
in
f
e
r
e
n
ce
s
y
s
tem
(
FIS)
th
at
h
as a
s
et
o
f
cr
iter
ia.
T
h
e
n
o
d
es with
th
e
h
ig
h
est s
u
itab
ilit
y
ca
n
ac
co
m
m
o
d
ate
DGs.
T
h
e
o
r
ig
in
al
s
y
s
tem
's
lo
ad
f
lo
w
s
o
lu
tio
n
m
u
s
t
b
e
u
s
ed
in
th
e
f
ir
s
t
s
tag
e
to
d
eter
m
in
e
th
e
r
ea
l
an
d
r
ea
ctiv
e
p
o
wer
l
o
s
s
es.
L
o
ad
f
lo
w
s
o
lu
tio
n
s
ar
e
o
n
ce
ag
ain
n
ec
ess
ar
y
to
r
e
d
u
ce
p
o
wer
l
o
s
s
b
y
ad
ju
s
tin
g
th
e
en
tire
r
ea
ctiv
e
p
o
wer
lo
a
d
at
ea
ch
d
is
tr
ib
u
tio
n
s
y
s
tem
n
o
d
e.
Af
ter
th
at,
th
e
l
o
s
s
d
ed
u
ctio
n
s
ar
e
lin
ea
r
ly
n
o
r
m
alize
d
in
to
a
s
ca
le
o
f
(
1
–
0
)
,
wh
er
e
th
e
h
ig
h
est
lo
s
s
r
ed
u
ctio
n
h
as
a
v
alu
e
o
f
1
,
an
d
t
h
e
s
m
allest
lo
s
s
d
ed
u
ctio
n
h
as a
v
alu
e
o
f
0
.
T
o
ca
lcu
late
th
e
p
o
wer
-
l
o
s
s
in
d
ex
v
alu
e
f
o
r
th
e
n
th
n
o
d
e,
u
ti
lize
as (
4
).
PL
I
(
n
)
=
(
(
)
−
(
m
i
n
)
)
(
(
m
ax
)
−
(
m
i
n
)
)
(
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2
2
5
2
-
8
7
9
2
Op
tima
l d
is
tr
ib
u
ted
g
en
era
to
r
p
la
ce
men
t fo
r
lo
s
s
r
ed
u
ctio
n
u
s
in
g
fu
z
z
y
a
n
d
a
d
a
p
tive
… (
Da
r
u
r
u
S
a
r
ika
)
157
T
h
ese
n
o
d
al
v
o
ltag
e
in
d
ex
es
an
d
th
e
r
ed
u
ctio
n
in
p
.
u
.
P
o
wer
lo
s
s
is
th
e
attr
ib
u
te
o
f
th
e
f
u
zz
y
-
in
f
er
en
ce
s
y
s
tem
(
FIS)
,
wh
ic
h
ca
lcu
lates
wh
ich
n
o
d
e
is
m
o
s
t
s
u
itab
le
f
o
r
a
d
d
in
g
ca
p
ac
ito
r
s
.
T
wo
in
p
u
t
v
ar
iab
les
an
d
o
n
e
o
u
tp
u
t
v
ar
i
ab
le
ar
e
u
s
ed
f
o
r
t
h
is
p
ap
er
.
Po
wer
-
lo
s
s
in
d
ex
(
PLI
)
an
d
p
er
u
n
it
n
o
d
al
v
o
ltag
e
ar
e
th
e
two
in
p
u
t
v
ar
ia
b
les
(
V)
.
DG
s
u
itab
ilit
y
in
d
ex
is
an
o
u
tp
u
t
v
ar
ia
b
le
(
DGSI
)
.
T
h
e
r
an
g
e
o
f
t
h
e
p
o
wer
lo
s
s
in
d
ex
is
0
to
1
,
t
h
e
p
er
u
n
it
n
o
d
e
v
o
ltag
e
r
an
g
e
is
1
.
1
t
o
0
.
9
,
an
d
th
e
s
ca
le
o
f
th
e
DG
s
u
itab
ilit
y
in
d
ex
is
0
to
1
.
Fo
r
PLI
,
f
i
v
e
m
em
b
er
s
h
i
p
r
o
les
h
av
e
b
ee
n
ch
o
s
en
.
L
M
,
L
,
M
,
H,
an
d
HM
ar
e
th
eir
n
am
es.
Acc
o
r
d
in
g
to
Fig
u
r
e
1
,
ea
ch
o
f
th
e
f
i
v
e
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
is
a
tr
ian
g
le.
Fo
r
v
o
ltag
e,
f
i
v
e
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
s
h
av
e
b
ee
n
ch
o
s
en
.
L
N,
L
,
N,
HN,
an
d
H
ar
e
th
eir
n
am
es.
Acc
o
r
d
in
g
to
Fig
u
r
e
2
,
t
h
ese
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
ar
e
tr
ian
g
u
lar
an
d
tr
ap
ez
o
id
al.
Fo
r
DGSI
,
f
iv
e
m
em
b
er
s
h
i
p
r
o
l
es
h
av
e
b
ee
n
ch
o
s
en
.
H,
HM
,
M,
L
M,
an
d
L
ar
e
th
eir
n
am
es.
T
h
e
t
r
ian
g
u
la
r
s
h
ap
e
o
f
Fig
u
r
e
3
also
r
ep
r
esen
ts
th
ese
f
iv
e
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
s
.
A
s
et
o
f
m
u
ltip
le
-
an
tece
d
en
t
f
u
zz
y
r
u
les
h
as
b
ee
n
d
ev
el
o
p
ed
to
ass
ess
th
e
s
u
itab
ilit
y
o
f
DG
d
ep
lo
y
m
e
n
t
at
a
s
p
ec
if
ic
n
o
d
e.
T
h
e
v
o
ltag
e
an
d
p
o
wer
l
o
s
s
in
d
ices
ar
e
th
e
r
u
les'
in
p
u
ts
,
an
d
th
eir
r
esu
lt
is
wh
eth
er
th
e
lo
ca
tio
n
o
f
t
h
e
D
G
is
ap
p
r
o
p
r
iate.
T
ab
le
1
f
u
zz
y
d
ec
is
io
n
m
a
tr
ix
p
r
o
v
id
es
a
s
u
m
m
ar
y
o
f
th
e
laws.
T
h
e
d
a
r
k
a
r
ea
o
f
t
h
e
m
atr
i
x
co
n
tain
s
th
e
r
u
le’
s
c
o
n
s
eq
u
e
n
ce
s
.
B
ased
o
n
t
h
e
m
o
s
t
ex
ce
l
len
t
DG
s
u
itab
ilit
y
in
d
ex
v
al
u
es,
id
ea
l D
G
lo
ca
tio
n
s
ar
e
f
o
u
n
d
.
Fig
u
r
e
1
.
Plo
t o
f
th
e
PLI
m
em
b
er
s
h
ip
f
u
n
ctio
n
Fig
u
r
e
2
.
Plo
t
o
f
th
e
p
.
u
.
n
o
d
al
v
o
ltag
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
Fig
u
r
e
3
.
Plo
t o
f
DGSI
m
em
b
er
s
h
ip
f
u
n
ctio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
4
,
No
.
1
,
Ma
r
ch
2
0
2
5
:
1
55
-
1
62
158
T
ab
le
1
.
L
o
ca
tio
n
-
b
ased
d
ec
is
io
n
-
m
ak
i
n
g
m
atr
i
x
D
G
S
I
P
LI
H
HM
M
LM
L
V
o
l
t
a
g
e
H
LM
L
L
L
L
HN
M
LM
LM
L
L
N
M
M
LM
LM
L
LN
HM
HM
M
LM
LM
L
H
HM
HM
M
LM
4.
ADAP
T
I
VE
G
R
E
Y
WO
L
F
O
P
T
I
M
I
Z
AT
I
O
N
AL
G
O
R
I
T
H
M
(
AG
WO
A)
T
h
e
g
r
ey
wo
lf
o
p
tim
izatio
n
(
GW
O)
is
a
m
eta
-
h
eu
r
is
tic
alg
o
r
ith
m
d
ev
elo
p
ed
b
y
th
e
r
esear
ch
er
s
in
[
1
5
]
-
[
1
7
]
an
d
is
b
ased
o
n
h
o
w
g
r
ey
wo
lv
es
h
u
n
t
in
th
e
wi
ld
.
T
h
ey
ar
e
class
if
ied
as
alp
h
a,
b
eta,
o
m
eg
a,
a
n
d
d
elta
v
ar
ieties
o
f
g
r
ey
wo
lv
es
in
th
e
s
o
cial
d
o
m
in
atin
g
h
ier
ar
ch
y
.
T
h
e
g
r
ey
wo
lv
es
f
o
r
m
s
ev
er
al
g
r
o
u
p
s
f
o
r
v
ar
io
u
s
task
s
,
s
u
ch
as
s
tay
in
g
to
g
eth
er
an
d
s
ea
r
ch
i
n
g
f
o
r
p
r
e
y
.
Fig
u
r
e
4
d
ep
icts
th
e
g
r
e
y
wo
lf
'
s
life
cy
cle
an
d
Fig
u
r
e
5
illu
s
tr
ates th
e
h
ier
ar
c
h
y.
Fig
u
r
e
4
.
L
if
e
cy
cle
o
f
g
r
e
y
w
o
lv
es
Fig
u
r
e
5
.
Hier
ar
c
h
ical
r
ep
r
ese
n
tatio
n
o
f
g
r
ey
wo
lv
es
Gr
o
u
p
h
u
n
tin
g
is
an
o
t
h
er
i
n
tr
ig
u
in
g
s
o
cial
ch
ar
ac
te
r
is
tic
o
f
g
r
e
y
wo
lv
es,
in
ad
d
itio
n
to
t
h
eir
s
o
cial
h
ier
ar
ch
y
.
T
h
e
f
o
llo
win
g
a
r
e
t
h
e
cr
itical
s
tag
es o
f
g
r
ey
wo
lf
h
u
n
tin
g
,
ac
co
r
d
in
g
to
Mir
jal
i
li
et
a
l.
[
1
7
]
:
a.
Seek
in
g
o
u
t
p
r
e
y
:
s
tar
tin
g
t
h
e
s
ea
r
ch
p
r
o
ce
d
u
r
e
o
f
f
at
r
an
d
o
m
with
p
o
ten
tial
s
o
lu
tio
n
s
(
also
k
n
o
w
n
as
wo
lv
es)
f
r
o
m
th
e
s
ea
r
ch
s
p
ac
e
.
g
r
ey
wo
lv
es
lo
o
k
f
o
r
p
r
ey
a
p
ar
t
f
r
o
m
o
n
e
an
o
th
e
r
b
ef
o
r
e
c
o
m
in
g
to
g
et
h
er
wh
en
th
ey
d
o
.
b.
Su
r
r
o
u
n
d
in
g
p
r
ey
:
g
r
e
y
wo
lv
es
cir
cle
th
eir
p
r
ey
af
ter
s
ea
r
ch
in
g
f
o
r
it,
an
d
th
is
b
eh
av
io
r
ca
n
b
e
m
ath
em
atica
lly
d
escr
ib
ed
b
y
(
5
)
an
d
(
6
)
.
⃗
=
|
⃗
.
(
)
−
(
)
|
(
5
)
⃗
⃗
⃗
⃗
(
+
1
)
=
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
−
.
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
6
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2
2
5
2
-
8
7
9
2
Op
tima
l d
is
tr
ib
u
ted
g
en
era
to
r
p
la
ce
men
t fo
r
lo
s
s
r
ed
u
ctio
n
u
s
in
g
fu
z
z
y
a
n
d
a
d
a
p
tive
… (
Da
r
u
r
u
S
a
r
ika
)
159
I
n
th
is
ca
s
e,
th
e
co
ef
f
icien
t v
e
cto
r
s
d
en
o
te
th
e
cu
r
r
en
t iter
ati
o
n
.
T
h
e
y
ar
e
em
p
l
o
y
ed
to
k
ee
p
s
ea
r
ch
er
s
'
g
r
ey
wo
lv
es
(
GW
)
awa
y
f
r
o
m
th
eir
p
r
ey
.
Dep
icts
im
p
e
d
im
en
ts
in
th
e
p
r
ey
'
s
p
ath
d
u
r
in
g
a
h
u
n
t
[
1
8
]
.
Her
e,
th
e
lo
ca
tio
n
v
ec
to
r
o
f
th
e
g
r
ey
wo
lf
is
s
h
o
wn
b
y
‘
X’
wh
ile
th
e
lo
ca
tio
n
ar
r
ay
o
f
its
p
r
ey
is
s
h
o
wn
b
y
‘
Xp
.
’
T
h
e
ar
r
ay
s
ar
e
ca
lcu
lated
in
t
h
e
m
an
n
er
s
p
ec
if
ie
d
in
(
7
)
an
d
(
9
)
.
⃗
⃗
⃗
⃗
=
2
×
×
1
−
(
7
)
⃗
=
2
×
2
(
8
)
c.
Hu
n
tin
g
th
e
p
r
e
y
:
g
r
e
y
wo
lv
es
cir
cle
th
eir
p
r
e
y
an
d
th
en
f
o
cu
s
o
n
h
u
n
tin
g
.
T
y
p
es
o
f
wo
lv
es
ty
p
ically
d
ir
ec
t
th
e
h
u
n
ts
.
Deliv
e
r
s
th
e
b
est
p
o
te
n
tial
an
s
wer
o
u
t
o
f
th
o
s
e
lis
ted
.
T
h
e
g
r
ey
w
o
lf
'
s
h
u
n
tin
g
h
a
b
it
f
o
r
m
u
la
is
(
7
)
-
(
1
5
)
.
=
|
(
⃗
1
∗
(
)
)
−
(
)
|
(
9
)
=
|
(
⃗
2
∗
(
)
)
−
(
)
|
(
1
0
)
=
|
(
⃗
3
∗
(
)
)
−
(
)
|
(
1
1
)
1
=
(
)
−
(
⃗
1
∗
⃗
)
(
1
2
)
2
=
(
)
−
(
⃗
2
∗
⃗
)
(
1
3
)
3
=
(
)
−
(
⃗
3
∗
⃗
)
(
1
4
)
⃗
⃗
⃗
⃗
(
+
1
)
=
(
⃗
1
+
⃗
2
+
⃗
3
)
3
(
1
5
)
d.
Attack
in
g
p
r
ey
:
g
r
e
y
wo
lv
es
attac
k
th
eir
p
r
ey
o
n
ce
th
e
h
u
n
t
is
o
v
er
.
T
h
e
GW
O
p
r
o
ce
d
u
r
e
en
ab
les
th
e
wo
lv
es
to
m
o
d
if
y
th
eir
lo
ca
tio
n
s
to
h
it
th
e
p
r
ey
b
ased
o
n
th
e
p
lace
o
f
th
e
g
r
o
u
p
o
f
g
r
ey
w
o
lv
es.
T
h
er
e
ar
e
two
f
ac
to
r
s
to
tak
e
in
t
o
ac
co
u
n
t w
h
en
ap
p
r
o
ac
h
in
g
th
e
p
r
e
y
.
4
.
1
.
AG
WO
A
i
m
plem
ent
a
t
i
o
n t
o
a
dd
re
s
s
t
he
is
s
ue
o
f
po
wer
lo
s
s
m
ini
m
iza
t
io
n
T
h
e
s
tep
s
th
e
AGWOA
ad
o
p
ted
to
allev
iate
th
is
s
tu
d
y
'
s
p
o
wer
lo
s
s
m
in
im
izatio
n
p
r
o
b
le
m
ar
e
lis
ted
b
elo
w
[
1
9
]
-
[
27]
.
I
n
th
is
a
d
ap
ted
,
th
e
m
u
tatio
n
p
r
o
ce
s
s
to
co
n
v
en
tio
n
al
GW
O
alg
o
r
ith
m
f
o
r
e
x
p
lo
r
in
g
n
ew
s
o
lu
tio
n
s
,
an
d
av
o
id
in
g
lo
ca
l
o
p
tim
a,
th
er
e
b
y
en
h
a
n
cin
g
t
h
e
o
v
er
all
e
f
f
ec
tiv
en
ess
o
f
t
h
e
s
ea
r
ch
p
r
o
ce
s
s
.
AGWO a
lg
o
r
ith
m
im
p
lem
en
ta
tio
n
p
r
o
ce
d
u
r
es f
o
r
p
o
wer
lo
s
s
m
in
im
izatio
n
is
s
u
es
:
-
Step
1
:
I
n
itializatio
n
.
a)
R
ea
d
th
e
B
co
ef
f
icien
t,
th
e
c
o
s
t c
o
ef
f
icien
t,
an
d
th
e
em
is
s
io
n
co
ef
f
icien
ts
.
b)
Set e
ac
h
g
en
er
ato
r
'
s
o
u
tp
u
t p
o
wer
lim
its
.
c)
Ma
x
im
u
m
v
alu
es
o
f
s
ea
r
ch
v
a
r
iab
les ar
e
p
r
ed
eter
m
in
ed
.
d)
T
h
e
lo
wer
an
d
u
p
p
er
s
ea
r
ch
s
p
ac
e
r
estrictio
n
s
in
th
e
GW
O
s
e
ttin
g
s
.
-
Step
2
:
Place
th
e
in
itial f
itn
ess
v
alu
es'
p
lace
m
en
ts
at
r
an
d
o
m
.
Alp
h
aa
_
p
o
s
t=z
er
o
s
(
d
i
m
,
1
)
’
;
Alp
h
aa
_
s
co
r
es=in
f
;
B
etaa
_
p
o
s
t=z
er
o
s
(
d
im
,
1
)
’
;
B
etaa
_
s
co
r
es=in
f
;
Om
eg
aa
_
p
o
s
t=z
er
o
s
(
d
i
m
,
1
)
’
;
Om
eg
aa
_
s
co
r
es=in
f
;
Po
s
itio
n
=r
an
d
(
Sear
ch
Ag
e
n
ts
_
n
o
.
,
d
im
.
)
.
*
(
u
.
b
-
l.b
)
+l.
b
;
-
Step
3
:
Ass
ig
n
th
e
tim
e
s
tep
tt=0
.
-
Step
4
:
Dete
r
m
in
e
t
h
e
o
b
jectiv
e
f
u
n
ctio
n
'
s
s
tar
tin
g
lo
ca
tio
n
s
.
Set
ea
ch
alp
h
aa
to
h
is
c
u
r
r
e
n
t
p
o
s
itio
n
f
r
o
m
h
is
p
r
ev
io
u
s
b
est lo
ca
tio
n
.
-
Step
5
:
L
et
tt=tt+1
.
-
Step
6
:
Dete
r
m
in
e
th
e
n
ei
g
h
b
o
r
o
f
ev
er
y
alp
h
aa
an
d
th
en
d
et
er
m
in
e
its
g
o
al
f
u
n
ctio
n
.
-
Step
7
:
Up
d
ate
ev
er
y
alp
h
aa
p
r
io
r
f
in
est lo
ca
tio
n
an
d
t
h
e
h
is
to
r
ical
b
est p
o
s
itio
n
am
o
n
g
th
e
s
ea
r
ch
ag
en
ts
.
-
Step
8
:
C
o
n
tin
u
e
f
r
o
m
Step
6
u
n
til th
e
o
b
jectiv
e
f
u
n
ctio
n
'
s
b
ea
t v
alu
e
is
attain
ed
b
y
s
ettin
g
th
e
co
n
v
er
g
en
ce
er
r
o
r
(
0
.
0
0
0
0
0
0
1
)
,
n
o
t
b
e
f
o
r
e
co
m
p
letin
g
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
.
-
Step
9
:
Fin
d
th
e
b
est
-
g
e
n
er
atin
g
p
o
wer
s
to
ac
h
iev
e
th
e
o
b
ject
iv
e
f
u
n
ctio
n
'
s
id
ea
l v
alu
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
4
,
No
.
1
,
Ma
r
ch
2
0
2
5
:
1
55
-
1
62
160
5.
RE
SU
L
T
S
T
h
e
b
est
DG
lo
ca
tio
n
s
a
r
e
d
is
co
v
er
ed
u
s
in
g
a
f
u
zz
y
tech
n
i
q
u
e,
an
d
t
h
e
b
est
DG
p
o
s
itio
n
s
an
d
s
izes
ar
e
f
o
u
n
d
u
s
in
g
AGWOA
an
d
it
is
co
n
tin
u
o
u
s
o
p
tim
izatio
n
p
r
o
b
lem
.
T
h
e
s
u
g
g
ested
m
eth
o
d
is
u
s
ed
with
15
-
b
u
s
an
d
33
-
b
u
s
m
o
d
el
n
etwo
r
k
s
u
s
in
g
MA
T
L
AB
s
o
f
twar
e,
an
d
th
e
s
o
lu
tio
n
s
ar
e
lis
ted
in
T
ab
les
2
an
d
3
ac
co
r
d
in
g
l
y
.
5
.
1
.
T
he
1
5
a
nd
3
3
-
bu
s
s
y
s
t
em
's
re
s
ults
T
h
e
r
esu
l
ts
s
h
o
w
th
at
th
e
AGWOA
o
p
tim
ized
th
e
DG
v
alu
es
to
o
b
tain
least
lo
s
s
es,
an
im
p
r
o
v
e
d
v
o
ltag
e
p
r
o
f
ile
co
m
p
ar
ed
with
th
e
r
ef
e
r
r
ed
alg
o
r
ith
m
.
I
n
th
e
15
-
b
u
s
s
y
s
tem
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
r
ed
u
ce
d
lo
s
s
to
2
.
5
7
4
f
r
o
m
i
n
tact
ca
s
e
lo
s
s
6
1
.
7
4
an
d
Nak
ed
m
o
le
r
a
t
alg
o
r
i
th
m
4
.
6
6
8
.
I
n
3
3
-
b
u
s
s
y
s
tem
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
r
ed
u
ce
d
l
o
s
s
to
3
9
.
4
4
f
r
o
m
in
tact
ca
s
e
lo
s
s
2
1
0
.
9
9
an
d
Nak
e
d
m
o
le
r
at
alg
o
r
ith
m
6
1
.
4
3
,
at
th
e
s
am
e
tim
e
im
p
r
o
v
e
d
v
o
ltag
e
p
r
o
f
ile
s
also
.
T
ab
le
2
.
Sizes o
f
th
e
DG
u
n
its
at
th
e
p
r
o
p
o
s
ed
b
u
s
p
o
s
itio
n
s
f
o
r
th
e
1
5
-
b
u
s
n
etwo
r
k
Ty
p
e
o
f
a
l
g
o
r
i
t
h
m
O
p
t
i
mal
D
G
Le
a
s
t
b
u
s
v
o
l
t
a
g
e
(
p
u
)
C
o
m
p
l
e
t
e
p
o
w
e
r
l
o
sse
s (k
W
)
B
u
s
n
o
.
D
G
si
z
e
(
M
V
A
)
B
e
f
o
r
e
D
G
s
A
f
t
e
r
D
G
s
B
e
f
o
r
e
D
G
s
A
f
t
e
r
D
G
s
%
r
e
d
u
c
t
i
o
n
i
n
p
o
w
e
r
l
o
sses
N
a
k
e
d
M
o
l
e
R
a
t
4
0
.
6
7
0
0
.
9
4
5
0
.
9
9
4
6
1
.
7
3
4
2
.
5
7
4
9
5
.
8
3
6
0
.
5
6
1
11
0
.
4
1
4
N
a
k
e
d
M
o
l
e
R
a
t
[
1
2
]
3
0
.
7
6
8
0
.
9
4
5
0
.
9
9
2
6
1
.
7
3
4
4
.
6
6
8
9
2
.
4
2
6
0
.
5
4
5
11
0
.
3
6
5
T
ab
le
3
.
Sizes o
f
th
e
DG
u
n
its
at
th
e
p
r
o
p
o
s
ed
b
u
s
s
to
p
s
f
o
r
t
h
e
3
3
-
b
u
s
s
y
s
tem
Ty
p
e
o
f
a
l
g
o
r
i
t
h
m
O
p
t
i
mal
D
G
Le
a
s
t
b
u
s
v
o
l
t
a
g
e
(
p
u
)
C
o
m
p
l
e
t
e
p
o
w
e
r
l
o
sse
s (k
W
)
B
u
s
n
o
.
D
G
si
z
e
(
M
V
A
)
B
e
f
o
r
e
D
G
s
A
f
t
e
r
D
G
s
B
e
f
o
r
e
D
G
s
A
f
t
e
r
D
G
s
%
r
e
d
u
c
t
i
o
n
i
n
p
o
w
e
r
l
o
sses
A
G
W
O
A
6
1
.
7
7
8
5
0
.
8
7
9
0
.
9
7
2
2
1
0
.
9
9
3
9
.
4
4
8
1
.
3
0
5
28
0
.
0
8
9
1
29
0
.
0
6
7
4
30
1
.
7
2
3
5
N
a
k
e
d
M
o
l
e
R
a
t
[
1
2
]
6
1
.
8
4
4
0
.
8
7
9
0
.
9
6
6
2
1
0
.
9
9
6
1
.
4
3
7
0
.
8
8
28
0
.
0
9
3
29
0
.
1
0
7
6.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
in
tr
o
d
u
ce
s
a
d
u
al
-
p
h
ase
p
r
o
ce
d
u
r
e
f
o
r
ca
lcu
lati
n
g
th
e
id
ea
l
DG
s
izes
an
d
lo
ca
tio
n
s
f
o
r
lo
s
s
r
ed
u
ctio
n
in
d
is
tr
ib
u
tio
n
s
y
s
tem
s
.
Ad
ap
tiv
e
g
r
ey
wo
lf
alg
o
r
ith
m
an
d
f
u
zz
y
tech
n
iq
u
e
ar
e
p
r
o
p
o
s
ed
to
ch
o
o
s
e
th
e
b
est
PV
an
d
ca
p
ac
ito
r
s
izes
an
d
p
lace
m
en
ts
,
r
esp
ec
tiv
ely
.
T
h
ese
in
f
er
en
ce
s
a
r
e
m
ad
e
in
lig
h
t
o
f
th
e
s
im
u
latio
n
r
esu
lts
:
t
h
e
co
m
p
lete
ac
tiv
e
p
o
wer
lo
s
s
o
f
th
e
n
etwo
r
k
h
as
b
ee
n
d
r
am
ati
ca
lly
d
ec
r
ea
s
ed
b
y
in
s
tallin
g
DG
at
a
ll
th
e
id
ea
l
p
lace
s
,
an
d
b
u
s
v
o
ltag
es
h
av
e
im
p
r
o
v
ed
s
ig
n
if
ica
n
tly
.
T
h
ey
ar
e
co
n
s
id
er
in
g
th
e
DGSI
v
alu
e
th
e
f
u
zz
y
tech
n
iq
u
e,
wh
ich
ca
n
d
eter
m
i
n
e
th
e
b
est
DG
p
o
s
itio
n
s
.
T
h
e
id
ea
l s
it
es a
n
d
DG
s
ize
s
ar
e
s
o
u
g
h
t a
f
ter
iter
ativ
el
y
b
y
th
e
s
u
g
g
ested
g
r
ey
wo
lf
alg
o
r
ith
m
.
RE
F
E
R
E
NC
E
S
[
1
]
T.
A
c
k
e
r
ma
n
n
,
G
.
A
n
d
e
r
sso
n
,
a
n
d
L
.
S
ö
d
e
r
,
“
D
i
s
t
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
:
a
d
e
f
i
n
i
t
i
o
n
,
”
E
l
e
c
t
ri
c
Po
w
e
r
S
y
st
e
m
s
Re
s
e
a
rc
h
,
v
o
l
.
5
7
,
n
o
.
3
,
p
p
.
1
9
5
–
2
0
4
,
A
p
r
.
2
0
0
1
,
d
o
i
:
1
0
.
1
0
1
6
/
S
0
3
7
8
-
7
7
9
6
(
0
1
)
0
0
1
0
1
-
8.
[
2
]
Y
.
G
.
B
a
e
,
“
A
n
a
l
y
t
i
c
a
l
met
h
o
d
o
f
c
a
p
a
c
i
t
o
r
a
l
l
o
c
a
t
i
o
n
o
n
d
i
st
r
i
b
u
t
i
o
n
p
r
i
mary
f
e
e
d
e
r
s,
”
I
EE
E
T
r
a
n
s
a
c
t
i
o
n
s
o
n
Po
w
e
r
A
p
p
a
r
a
t
u
s
a
n
d
S
y
s
t
e
m
s
,
v
o
l
.
P
A
S
-
9
7
,
n
o
.
4
,
p
p
.
1
2
3
2
–
1
2
3
8
,
Ju
l
.
1
9
7
8
,
d
o
i
:
1
0
.
1
1
0
9
/
T
P
A
S
.
1
9
7
8
.
3
5
4
6
0
5
.
[
3
]
J.
G
r
a
i
n
g
e
r
a
n
d
S
.
L
e
e
,
“
O
p
t
i
m
u
m
s
i
z
e
a
n
d
l
o
c
a
t
i
o
n
o
f
s
h
u
n
t
c
a
p
a
c
i
t
o
r
s
f
o
r
r
e
d
u
c
t
i
o
n
o
f
l
o
sses
o
n
d
i
st
r
i
b
u
t
i
o
n
f
e
e
d
e
r
s,
”
I
E
EE
T
ra
n
s
a
c
t
i
o
n
s
o
n
Po
w
e
r
A
p
p
a
r
a
t
u
s
a
n
d
S
y
st
e
m
s
,
v
o
l
.
P
A
S
-
1
0
0
,
n
o
.
3
,
p
p
.
1
1
0
5
–
1
1
1
8
,
M
a
r
.
1
9
8
1
,
d
o
i
:
1
0
.
1
1
0
9
/
TPA
S
.
1
9
8
1
.
3
1
6
5
7
7
.
[
4
]
M
.
E.
B
a
r
a
n
a
n
d
F
.
F
.
W
u
,
“
O
p
t
i
m
a
l
c
a
p
a
c
i
t
o
r
p
l
a
c
e
me
n
t
o
n
r
a
d
i
a
l
d
i
st
r
i
b
u
t
i
o
n
s
y
st
e
ms,
”
I
E
EE
T
ra
n
s
a
c
t
i
o
n
s
o
n
Po
w
e
r
D
e
l
i
v
e
ry
,
v
o
l
.
4
,
n
o
.
1
,
p
p
.
7
2
5
–
7
3
4
,
1
9
8
9
,
d
o
i
:
1
0
.
1
1
0
9
/
6
1
.
1
9
2
6
5
.
[
5
]
G
.
G
a
n
g
i
l
,
S
.
K
.
G
o
y
a
l
,
a
n
d
M
.
S
r
i
v
a
s
t
a
v
a
,
“
O
p
t
i
ma
l
p
l
a
c
e
m
e
n
t
o
f
D
G
f
o
r
p
o
w
e
r
l
o
sses
m
i
n
i
mi
z
a
t
i
o
n
i
n
r
a
d
i
a
l
d
i
s
t
r
i
b
u
t
i
o
n
s
y
st
e
m
u
si
n
g
b
a
c
k
w
a
r
d
f
o
r
w
a
r
d
sw
e
e
p
a
l
g
o
r
i
t
h
m
,
”
i
n
2
0
2
0
I
EE
E
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
A
d
v
a
n
c
e
s
a
n
d
D
e
v
e
l
o
p
m
e
n
t
s
i
n
El
e
c
t
ri
c
a
l
a
n
d
El
e
c
t
r
o
n
i
c
s E
n
g
i
n
e
e
ri
n
g
(
I
C
AD
E
E)
,
I
EEE,
D
e
c
.
2
0
2
0
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
A
D
EE5
1
1
5
7
.
2
0
2
0
.
9
3
6
8
9
4
1
.
[
6
]
M
.
C
h
i
s,
M
.
M
.
A
.
S
a
l
a
m
a
,
a
n
d
S
.
J
a
y
a
r
a
m
,
“
C
a
p
a
c
i
t
o
r
p
l
a
c
e
m
e
n
t
i
n
d
i
st
r
i
b
u
t
i
o
n
s
y
st
e
ms
u
si
n
g
h
e
u
r
i
s
t
i
c
se
a
r
c
h
st
r
a
t
e
g
i
e
s,”
I
E
E
Pro
c
e
e
d
i
n
g
s
-
G
e
n
e
r
a
t
i
o
n
,
T
ra
n
sm
i
ss
i
o
n
a
n
d
D
i
st
r
i
b
u
t
i
o
n
,
v
o
l
.
1
4
4
,
n
o
.
3
,
p
p
.
2
2
5
–
2
3
0
,
1
9
9
7
,
d
o
i
:
1
0
.
1
0
4
9
/
i
p
-
g
t
d
_
1
9
9
7
0
9
4
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2
2
5
2
-
8
7
9
2
Op
tima
l d
is
tr
ib
u
ted
g
en
era
to
r
p
la
ce
men
t fo
r
lo
s
s
r
ed
u
ctio
n
u
s
in
g
fu
z
z
y
a
n
d
a
d
a
p
tive
… (
Da
r
u
r
u
S
a
r
ika
)
161
[
7
]
M
.
H
.
H
a
q
u
e
,
“
C
a
p
a
c
i
t
o
r
p
l
a
c
e
me
n
t
i
n
r
a
d
i
a
l
d
i
st
r
i
b
u
t
i
o
n
sy
s
t
e
ms
f
o
r
l
o
s
s
r
e
d
u
c
t
i
o
n
,
”
I
EE
Pr
o
c
e
e
d
i
n
g
s
-
G
e
n
e
ra
t
i
o
n
,
T
ra
n
sm
i
ssi
o
n
a
n
d
D
i
st
r
i
b
u
t
i
o
n
,
v
o
l
.
1
4
6
,
n
o
.
5
,
p
p
.
5
0
1
–
5
0
5
,
1
9
9
9
,
d
o
i
:
1
0
.
1
0
4
9
/
i
p
-
g
t
d
:
1
9
9
9
0
4
9
5
.
[
8
]
H
.
N
.
N
g
,
M
.
M
.
A
.
S
a
l
a
m
a
,
a
n
d
A
.
Y
.
C
h
i
k
h
a
n
i
,
“
C
a
p
a
c
i
t
o
r
a
l
l
o
c
a
t
i
o
n
b
y
a
p
p
r
o
x
i
ma
t
e
r
e
a
s
o
n
i
n
g
:
f
u
z
z
y
c
a
p
a
c
i
t
o
r
p
l
a
c
e
m
e
n
t
,
”
I
EEE
T
r
a
n
s
a
c
t
i
o
n
s
o
n
P
o
w
e
r De
l
i
v
e
r
y
,
v
o
l
.
1
5
,
n
o
.
1
,
p
p
.
3
9
3
–
3
9
8
,
2
0
0
0
,
d
o
i
:
1
0
.
1
1
0
9
/
6
1
.
8
4
7
2
7
9
.
[
9
]
R
.
R
a
n
j
a
n
a
n
d
D
a
s,
“
S
i
m
p
l
e
a
n
d
e
f
f
i
c
i
e
n
t
c
o
m
p
u
t
e
r
a
l
g
o
r
i
t
h
m
t
o
s
o
l
v
e
r
a
d
i
a
l
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s
,
”
E
l
e
c
t
ri
c
Po
w
e
r
C
o
m
p
o
n
e
n
t
s
a
n
d
S
y
s
t
e
m
s
,
v
o
l
.
3
1
,
n
o
.
1
,
p
p
.
9
5
–
1
0
7
,
2
0
0
3
,
d
o
i
:
d
o
i
.
o
r
g
/
1
0
.
1
0
8
0
/
1
5
3
2
5
0
0
0
3
9
0
1
1
2
0
9
9
.
[
1
0
]
C
.
H
.
P
r
a
sa
d
,
K
.
S
u
b
b
a
r
a
m
a
i
a
h
,
a
n
d
P
.
S
u
j
a
t
h
a
,
“
O
p
t
i
m
a
l
D
G
u
n
i
t
p
l
a
c
e
m
e
n
t
i
n
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s
b
y
mu
l
t
i
-
o
b
j
e
c
t
i
v
e
w
h
a
l
e
o
p
t
i
m
i
z
a
t
i
o
n
a
l
g
o
r
i
t
h
m
&
i
t
s
t
e
c
h
n
o
-
e
c
o
n
o
mi
c
a
n
a
l
y
s
i
s,”
El
e
c
t
r
i
c
P
o
w
e
r
S
y
st
e
m
s
Re
s
e
a
r
c
h
,
v
o
l
.
2
1
4
,
p
.
1
0
8
8
6
9
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
p
sr
.
2
0
2
2
.
1
0
8
8
6
9
.
[
1
1
]
J.
R
a
h
a
r
j
o
,
K
.
B
.
A
d
a
m
,
W
.
P
r
i
h
a
r
t
i
,
H
.
Ze
i
n
,
J.
H
a
s
u
d
u
n
g
a
n
,
a
n
d
E.
S
u
h
a
r
t
o
n
o
,
“
O
p
t
i
mi
z
a
t
i
o
n
o
f
p
l
a
c
e
me
n
t
a
n
d
s
i
z
i
n
g
o
n
d
i
s
t
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
u
s
i
n
g
t
e
c
h
n
i
q
u
e
o
f
sma
l
l
i
n
g
a
r
e
a
,
”
i
n
2
0
2
1
I
E
E
E
El
e
c
t
ri
c
a
l
P
o
w
e
r
a
n
d
E
n
e
r
g
y
C
o
n
f
e
re
n
c
e
(
EPE
C
)
,
To
r
o
n
t
o
,
2
0
2
1
,
p
p
.
4
7
5
–
4
7
9
,
d
o
i
:
1
0
.
1
1
0
9
/
EPEC
5
2
0
9
5
.
2
0
2
1
.
9
6
2
1
6
1
0
.
[
1
2
]
P
.
G
o
p
i
,
P
.
S
.
B
a
b
u
,
D
.
S
a
r
i
k
a
,
B
.
M
.
R
e
d
d
y
,
C
.
N
.
S
a
i
K
a
l
y
a
n
,
a
n
d
M
.
M
a
h
d
a
v
i
,
“
O
p
t
i
m
a
l
p
l
a
c
e
me
n
t
o
f
D
G
a
n
d
m
i
n
i
mi
z
a
t
i
o
n
o
f
p
o
w
e
r
l
o
ss
u
s
i
n
g
n
a
k
e
d
mo
l
e
r
a
t
a
l
g
o
r
i
t
h
m,”
i
n
2
0
2
3
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
T
e
c
h
n
o
l
o
g
y
a
n
d
P
o
l
i
c
y
i
n
E
n
e
rg
y
a
n
d
E
l
e
c
t
ri
c
Po
w
e
r (
I
C
T
-
P
EP)
,
I
EEE,
O
c
t
.
2
0
2
3
,
p
p
.
3
5
–
40
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
T
-
P
EP
6
0
1
5
2
.
2
0
2
3
.
1
0
3
5
1
1
5
0
.
[
1
3
]
D
.
D
a
s,
D
.
P
.
K
o
t
h
a
r
i
,
a
n
d
A
.
K
a
l
a
m,
“
S
i
m
p
l
e
a
n
d
e
f
f
i
c
i
e
n
t
met
h
o
d
f
o
r
l
o
a
d
f
l
o
w
s
o
l
u
t
i
o
n
o
f
r
a
d
i
a
l
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
E
l
e
c
t
r
i
c
a
l
Po
w
e
r
&
E
n
e
r
g
y
S
y
st
e
m
s
,
v
o
l
.
1
7
,
n
o
.
5
,
p
p
.
3
3
5
–
3
4
6
,
O
c
t
.
1
9
9
5
,
d
o
i
:
1
0
.
1
0
1
6
/
0
1
4
2
-
0
6
1
5
(
9
5
)
0
0
0
5
0
-
0.
[
1
4
]
M
.
E.
B
a
r
a
n
a
n
d
F
.
F
.
W
u
,
“
N
e
t
w
o
r
k
r
e
c
o
n
f
i
g
u
r
a
t
i
o
n
i
n
d
i
s
t
r
i
b
u
t
i
o
n
s
y
st
e
ms
f
o
r
l
o
ss
r
e
d
u
c
t
i
o
n
a
n
d
l
o
a
d
b
a
l
a
n
c
i
n
g
,
”
I
EEE
T
ra
n
s
a
c
t
i
o
n
s
o
n
P
o
w
e
r De
l
i
v
e
r
y
,
v
o
l
.
4
,
n
o
.
2
,
p
p
.
1
4
0
1
–
1
4
0
7
,
A
p
r
.
1
9
8
9
,
d
o
i
:
1
0
.
1
1
0
9
/
6
1
.
2
5
6
2
7
.
[
1
5
]
S
.
M
i
r
j
a
l
i
l
i
,
S
.
M
.
M
i
r
j
a
l
i
l
i
,
a
n
d
A
.
L
e
w
i
s,
“
G
r
e
y
w
o
l
f
o
p
t
i
m
i
z
e
r
,
”
A
d
v
a
n
c
e
s
i
n
E
n
g
i
n
e
e
r
i
n
g
S
o
f
t
w
a
r
e
,
v
o
l
.
6
9
,
p
p
.
4
6
–
6
1
,
M
a
r
.
2
0
1
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
d
v
e
n
g
s
o
f
t
.
2
0
1
3
.
1
2
.
0
0
7
.
[
1
6
]
S
.
S
a
r
e
m
i
,
S
.
Z.
M
i
r
j
a
l
i
l
i
,
a
n
d
S
.
M
.
M
i
r
j
a
l
i
l
i
,
“
E
v
o
l
u
t
i
o
n
a
r
y
p
o
p
u
l
a
t
i
o
n
d
y
n
a
mi
c
s
a
n
d
g
r
e
y
w
o
l
f
o
p
t
i
mi
z
e
r
,
”
N
e
u
r
a
l
C
o
m
p
u
t
i
n
g
a
n
d
Ap
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
2
6
,
n
o
.
5
,
p
p
.
1
2
5
7
–
1
2
6
3
,
J
u
l
.
2
0
1
5
,
d
o
i
:
1
0
.
1
0
0
7
/
s0
0
5
2
1
-
0
1
4
-
1
8
0
6
-
7.
[
1
7
]
S
.
M
i
r
j
a
l
i
l
i
,
S
.
S
a
r
e
m
i
,
S
.
M
.
M
i
r
j
a
l
i
l
i
,
a
n
d
L.
d
o
s
S
.
C
o
e
l
h
o
,
“
M
u
l
t
i
-
o
b
j
e
c
t
i
v
e
g
r
e
y
w
o
l
f
o
p
t
i
mi
z
e
r
:
A
n
o
v
e
l
a
l
g
o
r
i
t
h
m
f
o
r
mu
l
t
i
-
c
r
i
t
e
r
i
o
n
o
p
t
i
mi
z
a
t
i
o
n
,
”
Ex
p
e
r
t
S
y
s
t
e
m
s w
i
t
h
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
4
7
,
p
p
.
1
0
6
–
1
1
9
,
A
p
r
.
2
0
1
6
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
sw
a
.
2
0
1
5
.
1
0
.
0
3
9
.
[
1
8
]
S
.
S
e
i
f
h
o
sse
i
n
i
,
M
.
H
o
sse
i
n
i
S
h
i
r
v
a
n
i
,
a
n
d
Y
.
R
a
m
z
a
n
p
o
o
r
,
“
M
u
l
t
i
-
o
b
j
e
c
t
i
v
e
c
o
st
-
a
w
a
r
e
b
a
g
-
of
-
t
a
s
k
s
s
c
h
e
d
u
l
i
n
g
o
p
t
i
m
i
z
a
t
i
o
n
mo
d
e
l
f
o
r
I
o
T
a
p
p
l
i
c
a
t
i
o
n
s
r
u
n
n
i
n
g
o
n
h
e
t
e
r
o
g
e
n
e
o
u
s
f
o
g
e
n
v
i
r
o
n
m
e
n
t
,
”
C
o
m
p
u
t
e
r N
e
t
w
o
rks
,
v
o
l
.
2
4
0
,
p
.
1
1
0
1
6
1
,
F
e
b
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
mn
e
t
.
2
0
2
3
.
1
1
0
1
6
1
.
[
1
9
]
S
.
B
.
P
a
l
e
p
u
a
n
d
M
.
D
.
R
e
d
d
y
,
“
B
i
n
a
r
y
sp
i
d
e
r
m
o
n
k
e
y
a
l
g
o
r
i
t
h
m
a
p
p
r
o
a
c
h
f
o
r
o
p
t
i
mal
si
t
i
n
g
o
f
t
h
e
p
h
a
so
r
mea
s
u
r
e
me
n
t
f
o
r
p
o
w
e
r
s
y
st
e
m
s
t
a
t
e
e
st
i
ma
t
i
o
n
,
”
I
AE
S
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
Art
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
(
I
J
-
AI
)
,
v
o
l
.
1
1
,
n
o
.
3
,
p
p
.
1
0
3
3
–
1
0
4
0
,
S
e
p
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
a
i
.
v
1
1
.
i
3
.
p
p
1
0
3
3
-
1
0
4
0
.
[
2
0
]
A
.
V
.
S
.
R
e
d
d
y
,
M
.
D
.
R
e
d
d
y
,
a
n
d
M
.
S
.
K
.
R
e
d
d
y
,
“
N
e
t
w
o
r
k
r
e
c
o
n
f
i
g
u
r
a
t
i
o
n
o
f
d
i
s
t
r
i
b
u
t
i
o
n
sy
s
t
e
m
f
o
r
l
o
s
s
r
e
d
u
c
t
i
o
n
u
s
i
n
g
G
W
O
a
l
g
o
r
i
t
h
m,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
E
l
e
c
t
r
i
c
a
l
a
n
d
C
o
m
p
u
t
e
r
En
g
i
n
e
e
ri
n
g
(
I
J
EC
E)
,
v
o
l
.
7
,
n
o
.
6
,
p
p
.
3
2
2
6
–
323
4
,
2
0
1
7
.
[
2
1
]
P
.
S
.
B
a
b
u
,
P
.
B
.
C
h
e
n
n
a
i
a
h
,
a
n
d
M
.
S
r
e
e
h
a
r
i
,
“
O
p
t
i
mal
p
l
a
c
e
m
e
n
t
o
f
S
V
C
u
s
i
n
g
f
u
z
z
y
a
n
d
f
i
r
e
f
l
y
a
l
g
o
r
i
t
h
m
,
”
I
AES
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
Art
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
(
I
J
-
A
I
)
,
v
o
l
.
4
,
n
o
.
4
,
p
p
.
1
1
3
–
1
1
7
,
D
e
c
.
2
0
1
5
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
a
i
.
v
4
.
i
4
.
p
p
1
1
3
-
1
1
7
.
[
2
2
]
M
.
R
.
Za
i
d
a
n
a
n
d
S
.
I
.
To
o
s,
“
Emer
g
e
n
c
y
c
o
n
g
e
st
i
o
n
ma
n
a
g
e
me
n
t
o
f
p
o
w
e
r
s
y
st
e
ms
b
y
st
a
t
i
c
sy
n
c
h
r
o
n
o
u
s
s
e
r
i
e
s
c
o
m
p
e
n
s
a
t
o
r
,
”
I
n
d
o
n
e
si
a
n
J
o
u
r
n
a
l
o
f
El
e
c
t
ri
c
a
l
E
n
g
i
n
e
e
r
i
n
g
a
n
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
2
5
,
n
o
.
3
,
p
p
.
1
2
5
8
–
1
2
6
5
,
M
a
r
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
e
e
c
s.
v
2
5
.
i
3
.
p
p
1
2
5
8
-
1
2
6
5
.
[
2
3
]
M
.
C
h
i
r
a
n
j
i
v
i
a
n
d
K
.
S
w
a
r
n
a
sr
i
,
“
A
n
o
v
e
l
o
p
t
i
mi
z
a
t
i
o
n
-
b
a
s
e
d
p
o
w
e
r
q
u
a
l
i
t
y
e
n
h
a
n
c
e
me
n
t
u
si
n
g
d
y
n
a
m
i
c
v
o
l
t
a
g
e
r
e
s
t
o
r
e
r
a
n
d
d
i
s
t
r
i
b
u
t
i
o
n
st
a
t
i
c
c
o
mp
e
n
s
a
t
o
r
,
”
I
n
d
o
n
e
si
a
n
J
o
u
rn
a
l
o
f
E
l
e
c
t
r
i
c
a
l
E
n
g
i
n
e
e
ri
n
g
a
n
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
2
6
,
n
o
.
1
,
p
p
.
1
6
0
–
1
7
1
,
A
p
r
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
e
e
c
s
.
v
2
6
.
i
1
.
p
p
1
6
0
-
1
7
1
.
[
2
4
]
Z.
G
.
S
a
n
c
h
e
z
,
J.
A
.
G
o
n
z
á
l
e
z
,
G
.
C
r
e
sp
o
,
H
.
H
.
H
e
r
r
e
r
a
,
a
n
d
J
.
I
.
O
.
S
i
l
v
a
,
“
V
o
l
t
a
g
e
c
o
l
l
a
p
se
p
o
i
n
t
e
v
a
l
u
a
t
i
o
n
c
o
n
si
d
e
r
i
n
g
t
h
e
l
o
a
d
d
e
p
e
n
d
e
n
c
e
i
n
a
p
o
w
e
r
sy
s
t
e
m
s
t
a
b
i
l
i
t
y
p
r
o
b
l
e
m,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
E
l
e
c
t
r
i
c
a
l
a
n
d
C
o
m
p
u
t
e
r
E
n
g
i
n
e
e
ri
n
g
(
I
J
EC
E)
,
v
o
l
.
1
0
,
n
o
.
1
,
p
.
6
1
,
F
e
b
.
2
0
2
0
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
e
c
e
.
v
1
0
i
1
.
p
p
6
1
-
7
1
.
[
2
5
]
P
.
B
.
C
h
e
n
n
a
i
a
h
,
P
.
N
a
g
e
n
d
r
a
,
a
n
d
K
.
V
a
i
s
a
k
h
,
“
S
O
S
B
a
se
d
S
o
l
u
t
i
o
n
f
o
r
O
p
t
i
m
i
z
a
t
i
o
n
o
f
I
n
d
i
a
n
P
o
w
e
r
S
y
s
t
e
m
,
”
2
0
2
0
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
Em
e
rg
i
n
g
Fr
o
n
t
i
e
rs
i
n
El
e
c
t
ri
c
a
l
a
n
d
El
e
c
t
ro
n
i
c
T
e
c
h
n
o
l
o
g
i
e
s
(
I
C
E
FEET)
,
2
0
2
0
,
p
p
.
1
-
5
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
EFEET
4
9
1
4
9
.
2
0
2
0
.
9
1
8
7
0
1
2
.
[
2
6
]
P
.
S
.
B
a
b
u
a
n
d
D
.
M
.
D
.
R
e
d
d
y
,
“
O
p
t
i
m
a
l
p
l
a
c
e
me
n
t
o
f
P
M
U
s
i
n
sm
a
r
t
g
r
i
d
f
o
r
v
o
l
t
a
g
e
s
t
a
b
i
l
i
t
y
m
o
n
i
t
o
r
i
n
g
u
si
n
g
A
M
P
S
O
a
n
d
P
S
A
T,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
t
r
i
c
a
l
a
n
d
El
e
c
t
r
o
n
i
c
s
R
e
se
a
r
c
h
,
v
o
l
.
1
1
,
n
o
.
1
,
p
p
.
3
1
–
3
8
,
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
3
7
3
9
1
/
i
j
e
e
r
.
1
1
0
1
0
5
.
[
2
7
]
M
.
H
.
S
h
i
r
v
a
n
i
,
“
A
n
o
v
e
l
d
i
s
c
r
e
t
e
g
r
e
y
w
o
l
f
o
p
t
i
m
i
z
e
r
f
o
r
sc
i
e
n
t
i
f
i
c
w
o
r
k
f
l
o
w
s
c
h
e
d
u
l
i
n
g
i
n
h
e
t
e
r
o
g
e
n
e
o
u
s
c
l
o
u
d
c
o
mp
u
t
i
n
g
p
l
a
t
f
o
r
ms
,
”
S
c
i
e
n
t
i
a
I
r
a
n
i
c
a
,
v
o
l
.
2
9
,
n
o
.
5
,
p
p
.
2
3
7
5
–
2
3
9
3
,
M
a
y
2
0
2
2
,
d
o
i
:
1
0
.
2
4
2
0
0
/
sc
i
.
2
0
2
2
.
5
7
2
6
2
.
5
1
4
4
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Da
r
u
r
u
S
a
r
ik
a
is an
a
ss
istan
t
p
ro
fe
ss
o
r
i
n
th
e
Co
m
p
u
ter S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
De
p
a
rtme
n
t
a
t
An
n
a
m
a
c
h
a
ry
a
In
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
a
n
d
S
c
ie
n
c
e
s
(Au
to
n
o
m
o
u
s),
Ra
jam
p
e
t.
S
h
e
h
a
s
8
y
e
a
rs
o
f
e
x
p
e
rien
c
e
in
tea
c
h
in
g
a
t
t
h
e
G
ra
d
u
a
te
lev
e
l.
S
h
e
re
c
e
iv
e
d
h
e
r
B.
Tec
h
.
a
n
d
M
.
Tec
h
.
in
c
o
m
p
u
ter
sc
ien
c
e
a
n
d
e
n
g
i
n
e
e
rin
g
fr
o
m
JN
TUA,
An
a
n
th
a
p
u
ra
m
u
,
In
d
ia,
in
2
0
1
6
a
n
d
p
u
rsu
e
d
h
e
r
P
h
.
D.
a
t
M
o
n
a
d
Un
iv
e
rsity
,
Ne
w De
lh
i.
S
h
e
p
u
b
li
sh
e
d
6
in
tern
a
ti
o
n
a
l
jo
u
rn
a
l
s
a
n
d
p
a
rt
icip
a
ted
i
n
1
0
in
tern
a
t
io
n
a
l
a
n
d
n
a
ti
o
n
a
l
w
o
rk
sh
o
p
s.
He
r
re
se
a
rc
h
a
re
a
is
a
rt
ifi
c
ial
in
telli
g
e
n
c
e
tec
h
n
i
q
u
e
s,
c
l
o
u
d
c
o
m
p
u
ti
n
g
,
b
i
g
d
a
ta,
a
n
d
m
a
c
h
in
e
le
a
rn
in
g
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sa
rik
a
d
a
ru
ru
7
7
9
0
@g
m
a
i
l.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
4
,
No
.
1
,
Ma
r
ch
2
0
2
5
:
1
55
-
1
62
162
Pa
lepu
S
u
r
e
sh
Ba
b
u
re
c
e
iv
e
d
h
is
B.
Tec
h
.
d
e
g
re
e
in
e
lec
tri
c
a
l
a
n
d
e
lec
tro
n
ics
e
n
g
in
e
e
rin
g
fr
o
m
An
n
a
m
a
c
h
a
ry
a
In
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
a
n
d
S
c
ien
c
e
s,
Ra
jam
p
e
t,
An
d
h
ra
P
ra
d
e
sh
,
In
d
ia
2
0
0
6
,
a
n
d
h
is
M
.
Tec
h
.
d
e
g
re
e
in
p
o
we
r
sy
ste
m
s
fro
m
S
ri
Ve
n
k
a
tes
wa
ra
Un
iv
e
rsity
Co
ll
e
g
e
o
f
En
g
in
e
e
ri
n
g
,
Ti
ru
p
a
ti
,
a
n
d
An
d
h
ra
P
ra
d
e
sh
,
In
d
ia,
in
2
0
1
0
.
He
p
u
rs
u
e
d
h
is
P
h
.
D.
i
n
e
lec
tri
c
a
l
e
n
g
in
e
e
ri
n
g
a
t
S
ri
Ve
n
k
a
tes
wa
ra
Un
iv
e
rs
it
y
Co
ll
e
g
e
o
f
En
g
in
e
e
ri
n
g
,
Ti
ru
p
a
ti
,
An
d
h
ra
P
ra
d
e
sh
,
I
n
d
ia.
His
re
se
a
r
c
h
in
tere
sts
in
c
lu
d
e
c
a
p
a
c
it
o
rs,
DG
p
lac
e
m
e
n
t
a
n
d
re
c
o
n
fig
u
ra
ti
o
n
o
f
d
istri
b
u
ti
o
n
sy
ste
m
s,
v
o
lt
a
g
e
sta
b
il
it
y
st
u
d
ies
,
c
o
m
p
re
h
e
n
siv
e
a
re
a
m
o
n
it
o
r
in
g
sy
ste
m
s,
a
n
d
sm
a
rt
g
ri
d
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
su
re
sh
ra
m
4
8
@g
m
a
il
.
c
o
m
.
Pa
sa
l
a
G
o
p
i
re
c
e
iv
e
d
a
P
h
.
D.
d
e
g
re
e
i
n
e
lec
tri
c
a
l
e
n
g
in
e
e
ri
n
g
fro
m
K
o
n
e
ru
Lak
sh
m
a
iah
E
d
u
c
a
ti
o
n
F
o
u
n
d
a
ti
o
n
(De
e
m
e
d
to
b
e
Un
i
v
e
rsity
),
Vij
a
y
a
wa
d
a
,
in
2
0
1
7
,
B.
Tec
h
.
d
e
g
re
e
in
e
lec
tri
c
a
l
a
n
d
e
lec
tro
n
ic
s
e
n
g
in
e
e
rin
g
fr
o
m
JN
T
Un
iv
e
rsit
y
,
Hy
d
e
ra
b
a
d
,
i
n
2
0
0
6
,
a
n
d
M
.
Tec
h
.
d
e
g
re
e
in
e
lec
tri
c
a
l
p
o
we
r
e
n
g
in
e
e
rin
g
fr
o
m
JN
T
Un
iv
e
rsity
,
An
a
n
th
a
p
u
ra
m
u
i
n
2
0
1
0
.
He
is
a
n
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
ri
n
g
De
p
a
rt
m
e
n
t
a
ss
o
c
iate
p
ro
f
e
ss
o
r
a
t
An
n
a
m
a
c
h
a
ry
a
Un
iv
e
rsit
y
,
Ra
jam
p
e
t.
He
h
a
s
1
7
y
e
a
rs
o
f
e
x
p
e
ri
e
n
c
e
tea
c
h
in
g
a
t
th
e
UG
a
n
d
P
G
lev
e
ls.
He
a
u
th
o
rize
s
two
b
o
o
k
s,
t
h
re
e
c
h
a
p
ters
,
a
n
d
m
o
re
t
h
a
n
4
5
jo
u
rn
a
l
a
n
d
c
o
n
fe
re
n
c
e
p
a
p
e
rs.
His
re
se
a
rc
h
in
tere
sts
i
n
c
lu
d
e
d
istr
ib
u
ti
o
n
n
e
two
r
k
re
c
o
n
fi
g
u
ra
ti
o
n
,
p
o
we
r
s
y
ste
m
o
p
e
ra
ti
o
n
,
re
n
e
wa
b
le
e
n
e
rg
ies
,
d
i
strib
u
te
d
g
e
n
e
ra
ti
o
n
,
n
e
two
rk
re
l
iab
il
it
y
,
a
n
d
a
p
p
li
c
a
ti
o
n
o
f
c
o
m
p
u
tati
o
n
a
l
a
l
g
o
r
it
h
m
s
i
n
o
p
ti
m
iza
ti
o
n
.
He
h
a
s
p
e
rfo
rm
e
d
m
o
r
e
th
a
n
1
5
re
v
iew
s
fo
r
IEE
E
P
o
ten
ti
a
ls
a
n
d
S
p
rin
g
e
r
Na
tu
re
,
Ad
v
a
n
c
e
s
in
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
E
n
g
in
e
e
rin
g
J
o
u
r
n
a
ls
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
p
a
sa
la.ep
e
0
7
@g
m
a
il
.
c
o
m
.
Pro
f.
M
a
n
u
b
o
lu
Da
m
o
d
a
r
Re
d
d
y
p
o
ss
e
ss
e
s
2
8
y
e
a
rs
o
f
e
x
p
e
rti
se
in
tea
c
h
i
n
g
a
t
th
e
g
ra
d
u
a
te
lev
e
l
a
n
d
2
3
y
e
a
rs
o
f
e
x
p
e
rien
c
e
in
re
se
a
rc
h
.
He
o
b
tai
n
e
d
a
b
a
c
h
e
lo
r'
s
d
e
g
re
e
a
n
d
a
d
o
c
t
o
ra
te
i
n
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
fro
m
S
.
V.
Un
i
v
e
rsity
Co
l
leg
e
o
f
En
g
in
e
e
ri
n
g
,
Ti
r
u
p
a
ti
,
In
d
ia
i
n
1
9
9
2
a
n
d
2
0
0
8
,
re
sp
e
c
ti
v
e
ly
.
He
h
o
l
d
s
a
Li
fe
M
e
m
b
e
rsh
ip
in
IS
TE
.
He
is
e
m
p
lo
y
e
d
a
s
a
p
ro
fe
ss
o
r
o
f
e
lec
tri
c
a
l
e
n
g
in
e
e
ri
n
g
a
t
S
.
V.
U
n
iv
e
rsit
y
in
T
iru
p
a
ti
,
In
d
ia.
He
h
a
s
a
u
th
o
re
d
o
n
e
Au
stra
li
a
n
P
a
ten
t
a
n
d
1
1
9
re
se
a
rc
h
p
a
p
e
rs,
i
n
c
lu
d
in
g
8
2
i
n
in
tern
a
ti
o
n
a
l
jo
u
rn
a
ls
a
n
d
3
9
i
n
in
tern
a
ti
o
n
a
l
c
o
n
fe
re
n
c
e
s.
C
u
rre
n
tl
y
,
h
e
is
su
p
e
rv
isi
n
g
1
6
P
h
.
D.
s
c
h
o
lars
a
n
d
h
a
s
s
u
c
c
e
ss
fu
ll
y
a
wa
rd
e
d
9
stu
d
e
n
ts.
T
h
e
fo
c
u
s
o
f
h
is
stu
d
y
li
e
s
in
th
e
o
p
ti
m
iza
ti
o
n
o
f
p
o
we
r
sy
ste
m
s
a
n
d
th
e
c
o
m
p
e
n
sa
ti
o
n
o
f
re
a
c
ti
v
e
p
o
we
r.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
d
re
d
d
y
9
9
9
@re
d
iffma
il
.
c
o
m
.
S
u
r
e
sh
Ba
b
u
P
o
tla
d
u
r
ty
re
c
e
iv
e
d
B.
Tec
h
.
d
e
g
re
e
i
n
e
lec
tro
n
ic
a
n
d
c
o
m
m
u
n
ica
ti
o
n
e
n
g
i
n
e
e
rin
g
fr
o
m
S
ri
Ve
n
k
a
tes
wa
ra
Un
iv
e
rsity
,
Ti
ru
p
a
ti
,
An
d
h
ra
P
ra
d
e
sh
,
In
d
ia
in
2
0
0
5
.
He
re
c
e
iv
e
d
M
.
Te
c
h
.
d
e
g
re
e
in
e
lec
tro
n
ics
in
str
u
m
e
n
tatio
n
a
n
d
c
o
m
m
u
n
ica
ti
o
n
sy
ste
m
s
fro
m
S
ri
Ve
n
k
a
tes
wa
ra
Un
iv
e
rsity
,
T
iru
p
a
ti
,
An
d
h
ra
P
ra
d
e
sh
,
In
d
ia
i
n
2
0
0
8
a
n
d
re
c
e
iv
e
d
a
P
h
.
D.
i
n
t
h
e
De
p
a
rtme
n
t
o
f
El
e
c
tr
o
n
ics
a
n
d
Co
m
m
u
n
i
c
a
ti
o
n
E
n
g
i
n
e
e
rin
g
fr
o
m
S
ri
Ve
n
k
a
tes
wa
ra
Un
iv
e
rsity
in
2
0
2
4
.
He
p
u
b
li
sh
e
d
m
o
re
th
a
n
2
3
p
a
p
e
rs
in
v
a
rio
u
s
re
p
u
ted
jo
u
r
n
a
ls
a
n
d
c
o
n
fe
re
n
c
e
s
.
Cu
rre
n
tl
y
wo
rk
in
g
a
s
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
in
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tro
n
i
c
s
a
n
d
Co
m
m
u
n
ica
ti
o
n
En
g
in
e
e
ri
n
g
,
a
t
S
ri
Ve
n
k
a
tes
wa
ra
Co
ll
e
g
e
o
f
En
g
in
e
e
ri
n
g
(Au
to
n
o
m
o
u
s),
Ti
r
u
p
a
ti
.
His
a
re
a
s
o
f
in
tere
st
in
c
lu
d
e
ra
d
a
r
sig
n
a
l
p
ro
c
e
ss
in
g
,
VL
S
I
d
e
sig
n
,
d
ig
it
a
l
ima
g
e
p
ro
c
e
ss
in
g
,
e
m
b
e
d
d
e
d
s
y
ste
m
s
a
n
d
Io
T
,
a
n
d
p
o
we
r
e
lec
tro
n
ics
.
He
c
a
n
b
e
co
n
tac
ted
a
t
e
m
a
il
:
su
re
sh
b
a
b
u
.
4
1
3
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.