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n
e
s
y
s
te
m
.
A
n
e
w
n
at
u
r
e
-
in
s
p
ir
ed
m
eta
-
h
e
u
r
is
t
ic
o
p
tim
izatio
n
al
g
o
r
ith
m
ca
l
led
W
h
ale
Op
ti
m
izatio
n
Alg
o
r
it
h
m
(
W
O
A
)
i
s
p
r
o
p
o
s
ed
[
9
]
.
T
h
is
m
et
h
o
d
m
i
m
ic
s
th
e
s
o
cial
b
eh
a
v
io
r
o
f
h
u
m
p
b
a
ck
w
h
ales
w
h
ich
is
ch
ar
ac
ter
i
ze
d
b
y
t
h
eir
u
n
iq
u
e
m
et
h
o
d
o
f
h
u
n
tin
g
k
n
o
w
n
as
th
e
b
u
b
b
le
-
n
et
f
ee
d
in
g
m
et
h
o
d
.
I
t
b
r
o
u
g
h
t
b
etter
p
er
f
o
r
m
a
n
ce
th
a
n
E
P
an
d
A
I
S
i
n
ca
lc
u
lati
n
g
th
e
o
p
ti
m
al
s
o
lu
tio
n
.
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h
is
p
ap
er
p
r
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ts
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icie
n
t
tec
h
n
iq
u
e
to
d
eter
m
i
n
e
th
e
o
p
tim
a
l
p
ar
a
m
eter
s
o
f
SV
C
-
P
I
d
a
m
p
in
g
co
n
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o
ller
in
s
o
l
v
in
g
a
n
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le
s
t
ab
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p
r
o
b
le
m
s
.
B
o
th
K
P
an
d
K
I
v
ar
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les
ar
e
d
eter
m
i
n
ed
u
s
i
n
g
W
O
A
an
d
it
w
a
s
co
m
p
ar
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d
w
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h
E
P
an
d
A
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S
o
p
ti
m
izatio
n
m
eth
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d
s
.
T
h
e
o
b
j
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tiv
e
is
to
p
r
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d
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ce
th
e
m
o
s
t
s
tab
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tech
n
iq
u
e
i
n
th
e
s
h
o
r
test
t
i
m
e.
2.
P
RO
B
L
E
M
F
O
R
M
UL
AT
I
O
N
I
n
th
i
s
s
t
u
d
y
,
th
e
P
h
ill
ip
s
-
Hef
f
r
o
n
b
lo
ck
d
iag
r
a
m
m
o
d
el
f
o
r
th
e
s
in
g
le
m
ac
h
in
e
i
n
f
in
ite
b
u
s
(
SMI
B
)
s
y
s
te
m
th
at
eq
u
ip
p
ed
w
it
h
SV
C
an
d
P
I
co
n
tr
o
ller
(
SVC
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I
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is
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n
s
id
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.
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h
e
co
n
ce
p
t
o
f
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B
is
eq
u
iv
ale
n
t
w
it
h
o
n
e
s
y
n
c
h
r
o
n
o
u
s
m
ac
h
in
e
co
n
n
ec
ted
to
o
n
e
b
i
g
b
u
s
w
i
th
i
n
f
in
i
te
lo
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h
e
in
p
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o
f
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-
P
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f
ed
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r
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m
th
e
s
p
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d
ev
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r
an
d
t
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n
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∆
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h
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b
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d
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r
a
m
m
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o
f
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w
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th
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s
h
o
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Fi
g
u
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1
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V
V
sT
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1
D
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s
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r
min
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3
3
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6
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R
A
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1
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d
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Hs
2
1
1
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Fig
u
r
e
1.
T
h
e
b
lo
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d
iag
r
am
m
o
d
el
o
f
SMI
B
w
i
th
S
VC
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PI
SVC
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P
I
ca
n
b
e
s
eg
r
e
g
ated
t
o
SVC
co
m
p
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n
e
n
t
a
n
d
P
I
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m
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en
t.
SV
C
co
m
p
o
n
e
n
t
co
n
s
is
ts
o
f
cir
cu
it
an
d
ti
m
e
co
n
s
ta
n
t,
K
V
an
d
T
V
.
T
h
e
P
I
c
o
m
p
o
n
e
n
t
i
s
d
er
iv
ed
f
r
o
m
p
r
o
p
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d
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g
r
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g
ai
n
o
f
t
h
e
P
I
co
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tr
o
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w
h
ich
s
i
m
p
l
if
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r
esp
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tiv
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y
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P
a
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d
K
I
.
T
h
e
eq
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atio
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s
r
ep
r
esen
t SMI
B
s
y
s
te
m
i
n
s
talled
w
it
h
SV
C
-
P
I
ar
e
as f
o
llo
w
ed
:
(
1
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(
2
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(
3
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(
4
)
(
5
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4
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ca
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u
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d
i
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[
1
0
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.
T
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atio
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(
1
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6
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m
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7
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W
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er
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[
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(
8
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[
⁄
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(
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1
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Her
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A
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d
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g
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s
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r
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r
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t
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t
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it
h
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s
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its
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s
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.
0
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1
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0
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5
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1
6
Ex
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5
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K
V
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V
=
0
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0
5
3.
CO
M
P
UT
AT
I
O
NAL
I
NT
E
L
L
I
G
E
NC
E
M
E
T
H
O
D
S
L
atel
y
,
t
h
e
u
s
e
o
f
A
r
ti
f
icia
l
I
n
telli
g
e
n
ce
(
A
I
)
tech
n
o
lo
g
y
i
s
s
y
n
o
n
y
m
o
u
s
i
n
s
o
lv
i
n
g
p
o
w
er
s
y
s
te
m
p
r
o
b
lem
s
.
A
I
tec
h
n
iq
u
e
s
u
tili
ze
th
e
lo
g
ic
a
n
d
k
n
o
w
led
g
e
r
ep
r
esen
tatio
n
s
o
f
ex
p
er
t
s
y
s
te
m
s
,
ar
ti
f
icia
l
n
e
u
r
al
n
et
w
o
r
k
(
A
N
N)
[
1
1
]
an
d
ev
o
l
u
tio
n
ar
y
co
m
p
u
tatio
n
(
E
C
)
.
T
h
e
E
C
f
ield
in
c
lu
d
es
P
ar
ticle
S
w
ar
m
Op
ti
m
izatio
n
(
P
SO)
[
1
2
]
,
E
v
o
lu
tio
n
ar
y
P
r
o
g
r
a
m
m
i
n
g
(
E
P
)
[
7
]
,
A
r
tif
icial
I
m
m
u
n
e
S
y
s
te
m
s
(
A
I
S)
[
8
]
an
d
Fire
f
l
y
A
l
g
o
r
ith
m
(
FA
)
[
1
3
]
.
I
n
th
is
s
tu
d
y
,
t
h
e
p
r
o
p
o
s
ed
W
OA
is
co
m
p
ar
ed
w
it
h
E
P
an
d
A
I
S
in
o
r
d
er
to
h
ig
h
lig
h
t
th
eir
m
er
it.
T
h
e
alg
o
r
ith
m
s
f
o
r
all
m
eth
o
d
s
ar
e
d
is
cu
s
s
ed
b
elo
w
.
3
.
1
Wha
le
O
pti
m
iza
t
io
n Alg
o
rit
h
m
W
h
ale
Op
ti
m
izatio
n
Alg
o
r
it
h
m
(
W
O
A
)
i
s
a
n
o
v
el
n
at
u
r
e
-
in
s
p
ir
ed
m
eta
-
h
e
u
r
is
tic
o
p
ti
m
izat
io
n
alg
o
r
ith
m
p
r
o
p
o
s
ed
b
y
Se
y
ed
ali
Mir
j
alili
an
d
An
d
r
e
w
L
e
wis
in
2
0
1
6
,
w
h
ich
m
i
m
ic
s
th
e
s
o
cial
b
eh
av
io
r
o
f
h
u
m
p
b
ac
k
w
h
ales.
T
h
e
h
u
m
p
b
ac
k
w
h
ales
d
i
v
e
d
ee
p
ly
,
cr
ea
te
b
u
b
b
les
in
a
s
p
ir
al
s
h
ap
e
ar
o
u
n
d
th
e
p
r
e
y
,
an
d
s
w
i
m
to
t
h
e
s
u
r
f
ac
e
d
u
r
in
g
th
e
m
a
n
o
eu
v
r
e.
T
h
e
y
u
s
u
all
y
atta
ck
th
e
s
m
all
f
is
h
es
clo
s
e
to
th
e
s
u
r
f
ac
e.
B
ased
o
n
th
is
b
e
h
av
io
r
,
t
h
e
m
o
d
elli
n
g
o
f
W
OA
ca
n
b
e
d
iv
id
ed
i
n
to
th
r
ee
o
p
er
ato
r
s
:
th
e
s
ea
r
ch
f
o
r
p
r
ey
(
ex
p
lo
r
atio
n
p
h
ase)
,
th
e
en
c
ir
clin
g
p
r
e
y
,
a
n
d
th
e
b
u
b
b
le
-
n
et
f
o
r
ag
i
n
g
(
e
x
p
lo
itatio
n
p
h
ase)
.
I
n
t
h
is
p
ap
er
,
th
e
W
O
A
w
o
r
k
s
as f
o
llo
w
s
:
a)
Step
1
(
I
n
it
ia
lizatio
n
)
:
T
h
e
w
h
ale
p
o
s
itio
n
x
i
o
f
N
s
o
lu
tio
n
(
i=1
,
…,
N
)
ar
e
r
an
d
o
m
l
y
cr
ea
ted
to
f
o
r
m
i
n
itia
l
w
h
ale
p
o
p
u
latio
n
.
E
ac
h
w
h
ale
is
ev
alu
ated
u
s
i
n
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Evaluation Warning : The document was created with Spire.PDF for Python.
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ate
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ated
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f
p
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5
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d
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p
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ate
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r
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5
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ate
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w
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1
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ased
o
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th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
5
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i
tializatio
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s
tatis
tical
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alu
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f
it
n
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s
ca
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m
u
tatio
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F
u
r
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etails ab
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t t
h
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tec
h
n
iq
u
e
ca
n
b
e
f
o
u
n
d
i
n
[
7
]
.
3
.
3
Art
if
icia
l I
m
m
u
ne
Sy
s
t
e
m
A
r
ti
f
icial
I
m
m
u
n
e
S
y
s
te
m
(
AI
S)
an
d
E
P
s
h
ar
e
m
an
y
co
m
m
o
n
asp
ec
ts
o
f
o
p
ti
m
izatio
n
tech
n
iq
u
es.
E
P
is
b
ased
o
n
t
h
e
n
atu
r
al
ev
o
lu
tio
n
m
o
d
el,
w
h
ile
A
I
S
tr
ie
s
to
b
en
e
f
it
f
r
o
m
th
e
ch
ar
ac
te
r
is
tics
o
f
a
h
u
m
a
n
i
m
m
u
n
e
s
y
s
te
m
.
B
asic
al
g
o
r
ith
m
f
o
r
A
I
S
-
b
ased
o
p
ti
m
izat
io
n
is
ca
lled
th
e
C
lo
n
al
Selec
tio
n
A
lg
o
r
it
h
m
(
C
S
A
)
.
T
h
e
alg
o
r
ith
m
o
f
A
I
S
t
h
at
i
n
v
o
l
v
ed
ar
e
in
itializat
io
n
,
s
ta
tis
tical
e
v
alu
a
tio
n
,
f
i
tn
e
s
s
ca
l
cu
latio
n
,
m
u
tatio
n
,
co
m
b
i
n
atio
n
,
s
elec
tio
n
a
n
d
o
n
e
ex
tr
a
co
m
p
o
n
e
n
t,
n
a
m
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y
clo
n
in
g
.
T
h
e
f
lo
w
ch
ar
t
w
h
ich
r
ep
r
esen
ts
A
I
S
alg
o
r
ith
m
ca
n
b
e
f
o
u
n
d
in
[
8
]
.
3
.
4
F
it
nes
s
E
q
ua
t
io
n
T
h
e
im
p
le
m
e
n
tatio
n
o
f
SV
C
-
P
I
in
th
e
SMI
B
s
y
s
te
m
is
ca
p
ab
le
to
im
p
r
o
v
e
t
h
e
o
s
cillat
io
n
s
d
a
m
p
i
n
g
an
d
m
i
n
i
m
ize
th
e
p
o
w
er
an
g
l
e
d
ev
iatio
n
af
ter
a
d
is
tu
r
b
an
c
e.
I
n
th
is
p
ap
er
,
a
f
itn
es
s
eq
u
atio
n
b
ased
o
n
th
e
m
i
n
i
m
u
m
d
a
m
p
i
n
g
r
atio
ξ
min
is
p
r
o
p
o
s
ed
as f
o
llo
w
s
[
1
4
]
:
(
√
)
(
1
5
)
w
h
er
e,
ξ
i
is
th
e
d
a
m
p
in
g
r
atio
o
f
th
e
i
th
elec
tr
o
m
ec
h
a
n
ical
m
o
d
es
o
f
o
s
cillatio
n
,
r
esp
ec
ti
v
el
y
.
ξ
EM
is
t
h
e
s
et
o
f
d
am
p
i
n
g
r
atio
s
o
f
th
e
elec
tr
o
m
ec
h
a
n
ical
m
o
d
es
o
f
o
s
cillati
o
n
.
σ
i
an
d
ω
i
ar
e
th
e
r
ea
l
a
n
d
i
m
ag
in
ar
y
p
ar
t
o
f
t
h
e
i
th
eig
e
n
v
al
u
e
at
t
h
e
lo
ad
in
g
co
n
d
itio
n
,
r
esp
ec
ti
v
el
y
.
W
ith
th
e
o
p
ti
m
izatio
n
o
f
ξ
min
,
th
e
s
y
s
te
m
p
o
les
ar
e
co
n
s
i
s
te
n
tl
y
p
u
s
h
ed
f
u
r
t
h
er
lef
t
o
f
th
e
i
m
ag
in
ar
y
(
jω
)
ax
is
.
Als
o
,
th
e
d
ec
r
ea
s
i
n
g
v
al
u
e
o
f
i
m
ag
i
n
ar
y
p
ar
t
o
f
th
e
ei
g
e
n
v
al
u
e
ω
w
ill
s
h
i
f
t
eig
en
v
al
u
e
r
eg
io
n
to
w
ar
d
s
t
h
e
r
ea
l
a
x
i
s
.
T
h
e
ar
ea
o
f
ei
g
en
v
al
u
es
o
n
t
h
e
p
h
a
s
e
p
lan
f
o
r
co
m
p
ar
is
o
n
ca
s
e
b
et
w
ee
n
s
y
s
te
m
w
it
h
an
d
w
it
h
o
u
t
p
r
o
p
o
s
ed
ap
p
r
o
a
ch
is
s
h
o
w
n
i
n
F
ig
u
r
e
3
.
T
h
e
ar
ea
b
o
u
n
d
ed
b
y
th
i
s
e
f
f
ec
t
ca
n
b
e
s
h
o
w
n
a
s
a
tr
a
p
ez
o
id
-
s
h
ap
ed
s
ec
to
r
o
n
th
e
p
h
ase
p
lan
.
R
e
a
l
(
ω
)
I
m
a
g
i
n
a
r
y
(
j
ω
)
B
e
f
o
r
e
o
p
t
i
m
i
z
a
t
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o
n
A
f
t
e
r
o
p
t
i
m
i
z
a
t
i
o
n
Fig
u
r
e
3.
C
o
m
p
ar
is
o
n
o
f
ei
g
e
n
v
alu
e
ar
ea
s
o
n
th
e
co
m
p
le
x
s
-
p
lan
e
(
w
i
th
a
n
d
w
ith
o
u
t
J
)
.
T
h
er
ef
o
r
e,
th
e
d
esig
n
p
r
o
b
le
m
ca
n
b
e
f
o
r
m
u
lated
as:
Ma
x
i
m
ize
J
T
h
is
is
s
u
b
j
ec
t to
:
K
P
max
≤
K
P
≤
K
P
min
,
K
I
max
≤
K
I
≤
K
I
min
Her
e,
K
P
an
d
K
I
ar
e
o
p
ti
m
ize
d
b
y
E
P
,
A
I
S
a
n
d
W
OA
ap
p
r
o
ac
h
.
T
h
e
f
it
n
es
s
v
a
lu
e
s
an
d
p
ar
am
eter
s
in
v
o
l
v
ed
in
t
h
ese
t
h
r
ee
tech
n
iq
u
es a
r
e
tab
u
lated
i
n
T
ab
le
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
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lec
E
n
g
&
C
o
m
p
Sci
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SS
N:
2502
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4752
Op
tima
l Tu
n
in
g
o
f
S
V
C
-
P
I
C
o
n
tr
o
ller
u
s
in
g
W
h
a
le
Op
timi
z
a
tio
n
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lg
o
r
ith
m
fo
r
A
n
g
le
…
(
N
.
A
.
M.
K
a
ma
r
i
)
617
T
ab
le
2
.
T
h
e
P
a
r
am
eter
s
f
o
r
AI
S,
E
P
an
d
W
OA
A
l
g
o
r
ith
m
s
M
e
t
h
o
d
s
A
I
S
EP
W
O
A
L
i
st
o
f
P
a
r
a
me
t
e
r
s
β
ai
s
=
0
.
0
5
β
ep
=
0
.
0
5
A
=
0
.
9
,
b
=
1
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
i
s
p
ap
er
,
s
i
m
u
latio
n
s
t
u
d
i
es
o
f
SV
C
-
P
I
b
ased
SMI
B
p
o
w
er
s
y
s
te
m
ar
e
ca
r
r
ied
o
u
t
i
n
MA
T
L
A
B
en
v
ir
o
n
m
e
n
t.
T
w
o
p
ar
a
m
eter
s
:
p
r
o
p
o
r
tio
n
al
g
ai
n
K
P
a
n
d
in
t
eg
r
al
g
ai
n
K
I
ar
e
o
p
ti
m
ized
u
n
til
m
a
x
i
m
u
m
v
al
u
e
o
f
th
e
f
it
n
ess
eq
u
atio
n
J
i
s
d
ef
i
n
ed
.
I
n
t
h
is
s
t
u
d
y
,
t
h
e
p
er
f
o
r
m
a
n
c
e
o
f
s
y
s
te
m
w
it
h
co
n
v
e
n
tio
n
a
l
SV
C
-
P
I
s
y
s
te
m
(
C
-
P
I
)
is
co
m
p
ar
ed
to
SVC
-
P
I
s
y
s
te
m
o
p
ti
m
ized
b
y
W
OA
(
W
O
A
-
P
I
)
,
SVC
-
P
I
s
y
s
te
m
o
p
ti
m
ized
b
y
E
P
(
E
P
-
P
I
)
an
d
SVC
-
P
I
s
y
s
te
m
o
p
tim
ized
b
y
A
I
S (
A
I
S
-
P
I
)
.
Fo
llo
w
i
n
g
t
w
o
d
i
f
f
er
e
n
t lo
ad
in
g
co
n
d
itio
n
s
ar
e
s
i
m
u
lated
:
a)
C
ase
1
(
P
=
0
.
3
5
p
.
u
.
,
Q
=
0
.
7
p
.
u
.
)
b)
C
ase
2
(
P
=
0
.
1
5
p
.
u
.
,
Q
=
0
.
3
p
.
u
.
)
T
h
e
r
esp
o
n
s
e
o
f
s
p
ee
d
d
e
v
iati
o
n
f
o
r
C
ase
1
i
s
s
h
o
w
n
i
n
Fi
g
u
r
e
4
.
T
h
e
s
y
s
te
m
w
it
h
C
-
P
I
is
p
o
o
r
ly
d
am
p
ed
an
d
b
ec
o
m
e
s
s
tab
le
f
o
r
m
o
r
e
th
a
n
3
s
ec
o
n
d
s
.
Fo
r
E
P
-
P
I
,
A
I
S
-
P
I
an
d
W
OA
-
P
I
,
all
th
r
ee
s
y
s
te
m
s
ar
e
i
m
p
r
o
v
i
n
g
t
h
e
d
a
m
p
i
n
g
ca
p
ab
ilit
y
.
Fro
m
t
h
e
s
p
ee
d
r
esp
o
n
s
e,
its
s
h
o
w
s
th
at
W
O
A
-
P
I
m
a
n
ag
e
to
d
eliv
er
t
h
e
f
aste
s
t a
n
d
s
m
o
o
th
e
s
t d
a
m
p
i
n
g
p
er
f
o
r
m
a
n
ce
,
f
o
llo
w
ed
b
y
E
P
-
P
I
an
d
A
I
S
-
P
I
.
Fig
u
r
e
4
.
Sp
ee
d
r
esp
o
n
s
e
f
o
r
C
ase
1
T
h
e
r
eg
io
n
s
o
f
ei
g
e
n
v
a
lu
e
s
lo
ca
tio
n
in
co
m
p
lex
s
-
p
lan
e
f
o
r
all
f
o
u
r
tec
h
n
iq
u
e
s
in
C
ase
1
ar
e
s
h
o
wn
in
Fi
g
u
r
e
5
.
I
t
in
d
icate
s
t
h
at
W
OA
m
eth
o
d
is
m
o
r
e
ca
p
ab
le
to
im
p
r
o
v
e
t
h
e
s
tab
ilit
y
o
f
t
h
e
s
y
s
te
m
b
y
p
u
s
h
i
n
g
th
e
eig
e
n
v
alu
e
s
lo
ca
tio
n
f
ar
f
u
r
th
er
to
th
e
lef
t
-
h
an
d
s
id
e
o
f
t
h
e
co
m
p
lex
s
-
p
la
n
e
an
d
clo
s
er
to
th
e
r
ea
l,
σ
ax
is
.
I
t
also
s
h
o
w
s
th
a
t
C
-
P
I
h
a
v
e
t
w
o
ei
g
e
n
v
a
lu
e
s
t
h
at
p
lace
n
ea
r
to
th
e
lef
t
-
h
a
n
d
s
id
e
o
f
t
h
e
j
ω
ax
is
,
in
d
icate
t
h
at
th
e
s
y
s
te
m
i
s
th
e
m
o
s
t le
s
s
s
ta
b
le.
Fig
u
r
e
5
.
C
o
m
p
lex
s
-
p
la
n
e
f
o
r
C
ase
1
T
h
e
r
esu
lts
o
f
f
it
n
es
s
p
r
o
f
iles
(
w
h
ich
b
ased
o
n
m
i
n
i
m
u
m
d
a
m
p
in
g
r
atio
ξ
min
)
,
n
u
m
b
er
o
f
it
er
atio
n
N
i
an
d
co
m
p
u
ta
tio
n
ti
m
e
u
s
in
g
C
-
P
I
,
A
I
S
-
P
I
,
E
P
-
P
I
an
d
W
OA
-
P
I
f
o
r
C
ase
1
ar
e
tab
u
lated
in
T
ab
le
3
.
Fro
m
t
h
e
r
esu
lt
s
,
W
O
A
-
P
I
o
p
ti
m
ized
t
h
e
h
ig
h
est
v
al
u
e
o
f
J
f
o
llo
w
e
d
b
y
E
P
-
P
I
,
A
I
S
-
P
I
an
d
C
-
P
I
.
Fro
m
th
e
iter
atio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
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2
-
4752
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2
,
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e
m
b
er
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1
8
:
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1
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–
6
1
9
618
p
er
s
p
ec
tiv
e,
A
I
S
i
s
ter
m
i
n
ate
d
in
t
h
e
s
h
o
r
tes
t
iter
atio
n
,
wh
ic
h
is
4
iter
atio
n
s
,
f
o
llo
w
ed
b
y
W
O
A
w
i
th
1
2
iter
atio
n
s
,
w
h
ile
t
h
e
E
P
w
as
s
to
p
p
ed
at
iter
atio
n
2
2
.
T
h
is
s
h
o
w
s
th
a
t
A
I
S
g
i
v
e
th
e
s
h
o
r
tes
t
co
m
p
u
ta
tio
n
ti
m
e
co
m
p
ar
ed
to
W
OA
an
d
E
P
.
E
v
en
th
o
u
g
h
s
lo
w
er
t
h
a
n
A
I
S
i
n
co
m
p
u
tatio
n
ti
m
e,
W
O
A
s
til
l
th
e
b
est
ap
p
r
o
ac
h
,
as
it g
i
v
es
f
ar
m
o
r
e
g
o
o
d
r
esu
l
t in
i
m
p
r
o
v
in
g
d
a
m
p
i
n
g
ca
p
ab
ilit
y
o
f
th
e
s
y
s
te
m
.
T
ab
le
3
.
C
o
m
p
ar
is
o
n
o
f
C
-
P
I
,
A
I
S
-
P
I
,
E
P
-
P
I
an
d
W
OA
-
P
I
Sy
s
te
m
f
o
r
C
ase
1
Ty
p
e
K
P
K
I
J
(
ξ
m
i
n
)
N
i
C
-
PI
0
.
5
3
.
0
0
.
0
2
7
2
-
A
I
S
-
PI
0
.
6
1
2
1
1
6
.
8
8
6
5
0
.
1
9
0
8
4
EP
-
PI
1
.
5
0
9
0
2
5
.
1
9
4
1
0
.
4
0
3
1
14
W
O
A
-
PI
1
.
6
0
0
3
2
8
.
0
3
4
3
0
.
5
4
6
2
12
T
h
e
r
esp
o
n
s
e
o
f
s
p
ee
d
d
ev
ia
ti
o
n
f
o
r
C
a
s
e
2
is
s
h
o
w
n
i
n
F
ig
u
r
e
6
.
Her
e
also
,
th
e
p
r
o
p
o
s
ed
W
OA
-
P
I
s
y
s
te
m
s
h
o
w
s
lo
w
er
o
s
cillati
o
n
an
d
b
etter
d
a
m
p
in
g
co
m
p
ar
ed
to
o
th
er
f
o
u
r
tech
n
iq
u
es.
T
h
e
r
eg
io
n
s
o
f
eig
en
v
al
u
es
lo
ca
tio
n
i
n
co
m
p
l
ex
s
-
p
lan
e
f
o
r
C
ase
2
as
s
h
o
wn
in
Fi
g
u
r
e
7
.
Fro
m
t
h
e
r
esu
lt
s
,
W
OA
i
s
th
e
m
o
s
t
s
u
f
f
icie
n
t
ap
p
r
o
ac
h
in
s
h
i
f
ti
n
g
t
h
e
eig
e
n
v
al
u
es
to
w
ar
d
s
σ
a
x
is
,
a
s
w
ell
a
s
f
u
r
t
h
er
to
th
e
l
ef
t
-
h
a
n
d
s
id
e
o
f
jω
ax
is
at
th
e
lo
ad
i
n
g
co
n
d
itio
n
c
o
m
p
ar
ed
to
o
th
er
t
h
r
ee
tech
n
i
q
u
es.
T
ab
le
4
tab
u
lates th
e
r
es
u
lts
f
o
r
co
m
p
ar
ati
v
e
s
tu
d
ie
s
u
s
i
n
g
C
-
P
I
,
A
I
S
-
P
I
,
E
P
-
P
I
an
d
W
OA
-
P
I
f
o
r
C
ase
2
.
R
es
u
lts
o
b
tai
n
ed
s
h
o
w
s
t
h
at
W
OA
-
P
I
ac
h
ie
v
e
h
ig
h
er
f
itn
e
s
s
co
m
p
ar
ed
to
o
th
er
th
r
ee
tech
n
iq
u
es.
Fig
u
r
e
6
.
S
p
ee
d
r
esp
o
n
s
e
f
o
r
C
ase
2
Fig
u
r
e
7
.
C
o
m
p
lex
s
-
p
la
n
e
f
o
r
C
ase
2
T
ab
le
4
.
C
o
m
p
ar
is
o
n
o
f
C
-
P
I
,
A
I
S
-
P
I
,
E
P
-
P
I
an
d
W
OA
-
P
I
Sy
s
te
m
f
o
r
C
ase
2
Ty
p
e
K
P
K
I
J
(
ξ
m
i
n
)
N
i
C
-
PI
0
.
5
3
.
0
0
.
0
6
0
4
-
A
I
S
-
PI
0
.
8
1
1
9
7
.
8
7
9
7
0
.
1
9
8
7
5
EP
-
PI
1
.
5
6
7
3
1
2
.
1
4
7
2
0
.
3
6
2
9
15
W
O
A
-
PI
1
.
8
5
7
3
1
4
.
4
7
2
0
.
4
4
7
2
10
5.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
p
r
o
p
o
s
ed
a
n
e
w
o
p
ti
m
izat
io
n
ap
p
r
o
ac
h
f
o
r
tu
n
i
n
g
SVC
w
it
h
P
I
co
n
tr
o
ller
.
T
h
r
e
e
m
eth
o
d
s
b
ased
o
n
A
I
S,
E
P
a
n
d
W
O
A
c
o
m
p
u
tatio
n
i
n
telli
g
e
n
ce
m
et
h
o
d
s
f
o
r
o
p
ti
m
izin
g
K
P
an
d
K
I
o
f
P
I
co
n
tr
o
ller
h
a
v
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
Op
tima
l Tu
n
in
g
o
f
S
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C
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P
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C
o
n
tr
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u
s
in
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W
h
a
le
Op
timi
z
a
tio
n
A
lg
o
r
ith
m
fo
r
A
n
g
le
…
(
N
.
A
.
M.
K
a
ma
r
i
)
619
b
ee
n
d
ev
elo
p
ed
.
R
esu
lt
s
o
b
tain
ed
f
r
o
m
t
h
e
s
t
u
d
y
s
h
o
w
th
at
W
OA
o
u
tp
er
f
o
r
m
ed
E
P
a
n
d
A
I
S
i
n
ter
m
s
o
f
g
iv
in
g
b
etter
d
a
m
p
i
n
g
a
n
d
lo
w
er
o
s
cil
latio
n
.
T
h
e
p
er
f
o
r
m
a
n
ce
s
ar
e
v
alid
ated
w
it
h
r
esp
ec
t
to
s
p
ee
d
d
ev
iatio
n
r
esp
o
n
s
e
as
w
ell
as
m
i
n
i
m
u
m
d
am
p
i
n
g
r
atio
ξ
min
an
d
eig
en
v
alu
es.
RE
F
E
R
E
NC
E
S
[1
]
M
.
M
.
El
Ad
a
n
y
,
A.
A
.
El
De
so
u
k
y
,
A.
A.
S
a
ll
a
m
.
P
o
w
e
r
S
y
ste
m
T
ra
n
sie
n
t
S
tab
il
it
y
:
A
n
A
lg
o
rit
h
m
f
o
r
As
se
ss
m
e
n
t
a
n
d
E
n
h
a
n
c
e
m
e
n
t
Ba
se
d
o
n
Ca
tas
tro
p
h
e
T
h
e
o
ry
a
n
d
F
A
CT
S
De
v
ic
e
s
.
IEE
E
Acc
e
ss
.
2
0
1
8
;
Early
A
c
c
e
ss
:
1
–
1
2
.
[2
]
S
.
Ch
ir
a
n
t
a
n
,
S
.
C
.
S
w
a
in
,
P.
C.
Pa
n
d
a
,
R.
J
e
n
a
.
En
h
a
n
c
e
me
n
t
o
f
Po
we
r
Pro
fi
les
b
y
Va
ri
o
u
s
F
ACT
S
De
v
ice
s
in
Po
we
r S
y
ste
m.
In
ter
n
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Co
m
m
u
n
ica
ti
o
n
a
n
d
El
e
c
tro
n
ics
S
y
ste
m
.
Co
i
m
b
a
to
re
.
2
0
1
7
;
8
9
6
–
9
0
1
.
[3
]
S
.
Da
s,
D.
Ch
a
tt
e
rjee
,
S
.
K.
G
o
s
w
a
m
i.
T
u
n
e
d
-
T
S
C
Ba
se
d
S
V
C
fo
r
Re
a
c
ti
v
e
P
o
w
e
r
Co
m
p
e
n
sa
ti
o
n
a
n
d
Ha
rm
o
n
ic
Re
d
u
c
ti
o
n
i
n
Un
b
a
lan
c
e
d
Distri
b
u
ti
o
n
S
y
ste
m
.
IET
Ge
n
e
ra
ti
o
n
,
T
ra
n
sm
issio
n
&
Distrib
u
ti
o
n
.
2
0
1
8
;
1
2
(
3
):
5
7
1
-
5
8
5
.
[4
]
N.
A
.
M
.
Ka
m
a
ri,
I.
M
u
sirin
,
Z
.
Oth
m
a
n
,
S
.
A
.
Ha
li
m
.
P
S
S
Ba
se
d
A
n
g
le
S
tab
il
it
y
I
m
p
ro
v
e
m
e
n
t
Us
in
g
W
h
a
l
e
Op
ti
m
iza
ti
o
n
A
p
p
ro
a
c
h
.
I
n
d
o
n
e
si
a
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
r
in
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
.
2
0
1
7
;
8
(2
):
3
8
2
-
3
9
0
.
[5
]
H.
E.
Ke
sh
ta,
A
.
A
.
A
li
,
E.
M
.
S
a
ied
,
F
.
M
.
Be
n
d
a
ry
.
A
p
p
li
c
a
ti
o
n
o
f
S
tatic
V
a
r
Co
m
p
e
n
sa
to
r
(S
V
C)
W
it
h
P
I
Co
n
tr
o
ll
e
r
f
o
r
G
rid
In
teg
ra
ti
o
n
o
f
W
in
d
F
a
r
m
U
sin
g
Ha
r
m
o
n
y
S
e
a
rc
h
.
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
E
me
rg
in
g
El
e
c
tric
Po
we
r S
y
ste
ms
.
2
0
1
6
;
1
7
(5
)
:
5
5
5
-
5
6
6
.
[6
]
N.
A
.
M
o
h
a
m
e
d
K
a
m
a
ri
,
I.
M
u
sirin
,
M
.
M
.
Ot
h
m
a
n
.
IP
S
O
b
a
se
d
S
V
C
-
P
ID
f
o
r
a
n
g
le
sta
b
il
it
y
e
n
h
a
n
c
e
m
e
n
t
.
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
S
imu
l
a
ti
o
n
:
S
y
ste
ms
,
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
.
2
0
1
7
;
1
7
(4
1
):
2
0
.
1
-
2
0
.
7
[7
]
A
.
F
.
A
.
Ka
d
ir,
A
.
M
o
h
a
m
e
d
,
H.
S
h
a
re
e
f
,
M
.
Z.
C.
W
a
n
ik
.
Op
ti
m
a
l
P
lac
e
m
e
n
t
a
n
d
S
izin
g
o
f
Distrib
u
ted
G
e
n
e
ra
ti
o
n
s
in
Distri
b
u
ti
o
n
S
y
ste
m
s
f
o
r
M
in
i
m
izin
g
L
o
ss
e
s
a
n
d
T
HD
v
Us
in
g
Ev
o
lu
t
io
n
a
ry
P
ro
g
ra
m
m
in
g
.
T
u
rk
ish
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
s
.
2
0
1
3
;
2
1
:
2
2
6
9
-
2
2
8
2
.
[8
]
A
.
A
.
Ib
ra
h
im
,
A
.
M
o
h
a
m
e
d
,
H.
S
h
a
re
e
f
,
S
.
P
.
G
h
o
s
h
a
l.
Op
ti
m
a
l
P
o
we
r
Qu
a
li
ty
M
o
n
it
o
r
Pl
a
c
e
me
n
t
in
Po
we
r
S
y
ste
ms
Ba
se
d
o
n
P
a
rticle
S
wa
r
m
Op
ti
miza
ti
o
n
a
n
d
Arti
fi
c
ia
l
Imm
u
n
e
S
y
ste
m
.
3
rd
C
o
n
f
e
re
n
c
e
o
n
Da
ta
M
in
in
g
a
n
d
Op
ti
m
iza
ti
o
n
.
P
u
traja
y
a
.
2
0
1
1
:
1
4
1
–
1
4
5
.
[9
]
A
d
a
d
a
d
B.
Be
n
to
u
a
ti
,
L
.
Ch
a
ib
,
S
.
Ch
e
tt
ih
.
A
Hy
b
rid
W
h
a
le
Al
g
o
rith
m
a
n
d
Pa
tt
e
rn
S
e
a
rc
h
T
e
c
h
n
i
q
u
e
fo
r
Op
ti
ma
l
Po
we
r
Fl
o
w
Pro
b
lem
.
8
t
h
I
n
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
M
o
d
e
ll
in
g
,
Id
e
n
t
if
ica
ti
o
n
a
n
d
Co
n
tro
l.
A
lg
iers
.
2
0
1
6
:
1
0
4
8
-
1
0
5
3
.
[1
0
]
P
.
Ku
n
d
u
r.
P
o
w
e
r
S
y
ste
m
S
tab
il
it
y
a
n
d
Co
n
tro
l.
Ne
w
Yo
rk
:
M
c
G
r
a
w
-
Hill
,
1
9
9
4
:
7
6
6
-
7
7
5
.
[1
1
]
M
.
H.
M
.
Zam
a
n
,
M
.
M
.
M
u
st
a
fa
,
A
.
Hu
ss
a
in
.
Crit
ica
l
Eq
u
iv
a
len
t
S
e
ries
Re
sista
n
c
e
Esti
m
a
ti
o
n
f
o
r
V
o
lt
a
g
e
Re
g
u
lato
r
S
ta
b
il
it
y
Us
in
g
H
y
b
ri
d
S
y
ste
m
Id
e
n
ti
f
ica
ti
o
n
a
n
d
Ne
u
ra
l
Ne
t
w
o
rk
.
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
n
A
d
v
a
n
c
e
d
S
c
ien
c
e
,
En
g
in
e
e
rin
g
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
.
2
0
1
7
;
7
:
1
3
8
1
–
1
3
8
8
.
[1
2
]
J.
A
.
A
li
,
M
.
A
.
Ha
n
n
a
n
,
A
.
M
o
h
a
m
e
d
.
Im
p
ro
v
e
d
In
d
irec
t
F
iel
d
-
Orie
n
ted
Co
n
tr
o
l
o
f
In
d
u
c
ti
o
n
M
o
to
r
Driv
e
Ba
se
d
P
S
O A
lg
o
rit
h
m
.
J
u
rn
a
l
T
e
k
n
o
lo
g
i
.
2
0
1
6
;
7
8
(6
-
2
):
2
7
-
3
2
.
[1
3
]
L
.
A
.
W
o
n
g
,
H.
S
h
a
re
e
f
,
A
.
M
o
h
a
m
e
d
a
n
d
A
.
A
.
Ib
ra
h
im
,
A
n
En
h
a
n
c
e
d
O
p
p
o
siti
o
n
-
b
a
se
d
F
iref
ly
A
l
g
o
rit
h
m
f
o
r
S
o
lv
in
g
C
o
m
p
lex
Op
ti
m
iza
ti
o
n
P
ro
b
lem
s,
J
u
rn
a
l
Ke
j
u
ru
ter
a
a
n
.
2
0
1
4
;
2
6
:
8
9
-
9
6
.
[1
4
]
N.
N.
Isla
m
,
M
.
A
.
Ha
n
n
a
n
,
A
.
M
o
h
a
m
e
d
,
H.
S
h
a
re
e
f
.
Da
m
p
in
g
P
o
w
e
r
S
y
ste
m
Os
c
il
a
a
ti
o
n
Us
in
g
El
i
ti
st
Dif
f
e
r
e
n
ti
a
l
S
e
a
rc
h
A
l
g
o
rit
h
m
in
M
u
lt
i
M
a
c
h
in
e
P
o
w
e
r
S
y
ste
m
.
J
o
u
rn
a
l
o
f
T
h
e
o
re
ti
c
a
l
a
n
d
Ap
p
li
e
d
In
fo
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
.
2
0
1
6
;
9
3
(
1
):
4
1
-
4
8
.
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