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e
r
s
y
s
te
m
[
1
2
]
.
W
h
ile,
p
o
w
er
s
y
s
te
m
co
m
p
a
n
ies
t
h
at
s
er
v
e
co
n
s
u
m
er
s
o
n
a
w
id
e
-
ar
ea
co
n
ti
n
u
o
u
s
l
y
w
it
h
s
ta
n
d
ar
d
q
u
alit
y
as
s
u
r
a
n
ce
a
n
d
w
it
h
o
u
t
in
ter
r
u
p
tio
n
.
So
m
e
g
en
er
atio
n
u
n
i
ts
s
u
ch
a
s
:
H
y
d
r
o
,
th
er
m
al,
n
u
clea
r
a
n
d
g
a
s
p
o
w
er
p
lan
t
s
ar
e
co
n
n
ec
ted
to
b
u
lk
p
o
w
er
s
y
s
te
m
s
v
ia
i
n
ter
co
n
n
ec
ti
n
g
tr
an
s
m
is
s
io
n
l
in
e
i
n
cl
u
d
in
g
h
i
g
h
v
o
ltag
e
d
ir
ec
t
cu
r
r
en
t
(
HVD
C
)
s
y
s
te
m
.
On
t
h
e
o
th
er
h
a
n
d
,
al
m
o
s
t
o
f
co
n
s
u
m
er
s
ar
e
lo
ca
ted
in
cit
y
ar
ea
s
s
u
c
h
as:
T
r
ad
e/c
o
m
m
er
cial
an
d
o
f
f
i
cial
co
m
p
l
e
x
ar
ea
s
,
s
u
b
u
r
b
an
ar
ea
,
in
d
u
s
tr
ial
ar
ea
an
d
r
esid
en
tial
ar
ea
.
An
al
y
s
i
s
o
f
a
lar
g
e
-
s
ca
le
p
o
w
er
s
y
s
te
m
(
L
SP
S)
n
et
w
o
r
k
i
s
u
s
u
all
y
v
er
y
co
m
p
lica
ted
an
d
d
if
f
ic
u
lt.
T
h
e
s
i
m
p
l
if
y
i
n
g
s
ch
e
m
e
s
h
o
u
ld
b
e
d
o
n
e
to
r
eso
lv
e
th
i
s
co
m
p
licated
n
et
w
o
r
k
p
r
o
b
lem
.
I
n
th
i
s
r
esea
r
ch
,
t
h
e
L
SP
S
is
d
i
v
id
e
in
to
th
r
ee
p
ar
ts
o
f
s
m
all
o
p
er
atio
n
ar
ea
s
[
1
3
]
.
I
n
o
p
er
atio
n
ti
m
e
,
i
m
b
alan
ce
o
f
e
n
er
g
y
i
n
r
esp
ec
tiv
e
s
y
n
c
h
r
o
n
o
u
s
m
ac
h
in
e
m
a
k
es
t
h
e
r
o
to
r
m
ac
h
in
e
o
s
cillate
d
u
e
to
lo
ad
f
l
u
ct
u
atio
n
a
t
o
n
e
o
r
s
o
m
e
lo
ad
b
u
s
es.
W
h
e
n
t
h
e
r
o
to
r
m
ac
h
in
e
s
ar
e
o
s
c
illate
co
n
ti
n
u
o
u
s
l
y
w
it
h
o
u
t
a
n
y
co
n
tr
o
l
s
ch
e
m
e,
t
h
is
s
i
tu
at
io
n
ca
n
ca
u
s
e
t
h
e
L
SP
S
g
o
i
n
g
in
to
in
s
tab
il
it
y
p
r
o
b
le
m
.
T
o
co
v
er
th
is
r
o
to
r
o
s
cillatio
n
p
r
o
b
lem
,
p
o
w
er
s
y
s
te
m
s
ta
b
ilizer
(
P
SS
)
is
p
r
o
p
o
s
ed
at
ex
citatio
n
s
y
s
te
m
o
f
s
y
n
c
h
r
o
n
o
u
s
m
ac
h
i
n
e
to
d
am
p
t
h
e
r
o
to
r
o
s
cillatio
n
.
W
h
er
e,
lo
ca
l
m
o
d
e
s
tab
ilit
y
o
f
a
p
u
m
p
s
to
r
ag
e
p
o
w
er
p
lan
t
i
s
s
i
g
n
i
f
ican
tl
y
i
m
p
r
o
v
ed
b
y
u
s
i
n
g
th
e
P
S
S
[
1
4
]
.
Fu
r
th
er
m
o
r
e,
p
er
f
o
r
m
a
n
c
e
o
f
a
co
n
v
en
tio
n
al
P
SS
is
m
ai
n
tai
n
ed
b
y
u
s
i
n
g
a
d
d
itio
n
al
o
r
au
x
iliar
y
lo
o
p
[
1
5
]
.
P
SS
b
ased
o
n
o
p
ti
m
al
a
n
d
s
u
b
-
o
p
ti
m
al
li
n
ea
r
q
u
ad
r
atic
r
eg
u
lato
r
d
esig
n
ar
e
p
r
o
p
o
s
ed
to
im
p
r
o
v
e
s
tab
il
it
y
o
f
p
o
w
er
s
y
s
te
m
.
I
n
th
i
s
d
esig
n
,
o
n
l
y
s
p
ee
d
d
ev
iatio
n
a
n
d
P
SS
s
tates
ar
e
co
n
s
id
er
ed
as
t
h
e
i
n
p
u
t
s
,
w
h
ile
t
h
e
co
u
p
lin
g
g
ai
n
b
et
w
ee
n
m
ac
h
in
e
s
ar
e
n
eg
lec
ted
[
1
6
]
.
T
h
e
f
u
n
c
tio
n
o
f
co
n
v
e
n
tio
n
al
co
n
tr
o
l
is
i
n
s
t
ea
d
b
y
t
h
e
n
e
u
r
al
n
e
t
w
o
r
k
,
f
u
zz
y
o
r
n
eu
r
o
-
f
u
zz
y
co
n
tr
o
ls
.
P
r
o
p
o
r
tio
n
al
in
te
g
r
al
an
d
d
er
i
v
ati
v
e
-
s
tatic
v
ar
co
m
p
e
n
s
ato
r
b
ased
o
n
r
ec
u
r
r
en
t
n
e
u
r
al
n
et
w
o
r
k
i
s
ap
p
lied
to
co
n
tr
o
l
ch
ao
s
an
d
v
o
ltag
e
co
llap
s
e
in
a
p
o
w
er
s
y
s
te
m
.
T
h
is
c
o
n
tr
o
l
s
ch
e
m
e
i
s
ab
le
to
s
u
p
p
r
es
s
ch
ao
s
an
d
v
o
lta
g
e
co
llap
s
e
in
p
o
w
er
s
y
s
te
m
s
[
1
7
]
.
A
d
ap
ti
v
e
cr
itic d
esig
n
b
ased
(
A
DC
)
n
e
u
r
o
-
f
u
zz
y
co
n
tr
o
ller
is
ap
p
lied
to
co
n
tr
o
l
th
e
s
tatic
co
m
p
e
n
s
ato
r
in
a
L
SP
S.
I
t
is
s
h
o
w
n
th
a
t
th
e
A
D
C
n
e
u
r
o
-
f
u
zz
y
co
n
tr
o
ller
is
m
o
r
e
ef
f
ec
ti
v
e
th
a
n
P
I
co
n
tr
o
ller
in
r
esp
o
n
d
i
n
g
to
s
m
al
l
d
is
tu
r
b
an
ce
a
n
d
s
h
o
r
t
cir
c
u
it
f
a
u
lt
[
1
8
]
.
A
ls
o
,
a
Ma
m
d
an
i
f
u
zz
y
co
n
tr
o
ller
is
ap
p
lied
to
h
ar
d
w
ar
e
i
m
p
le
m
e
n
tatio
n
to
s
tatic
co
m
p
en
s
ato
r
in
a
L
SP
S
[
1
9
]
.
T
h
e
A
N
FIS
-
b
ased
co
m
p
o
s
i
te
co
n
tr
o
ller
-
SV
C
an
d
P
I
D
-
lo
o
p
a
r
e
a
p
p
lied
to
co
n
tr
o
l
ch
ao
s
an
d
v
o
ltag
e
co
llap
s
e
an
d
to
r
eg
u
late
t
h
e
v
o
lta
g
e
at
lo
ad
b
u
s
w
it
h
lo
ad
in
g
f
l
u
ct
u
atio
n
[
2
0
]
,
[
2
1
]
.
A
ls
o
,
th
e
A
N
FIS
c
o
n
tr
o
ller
is
tr
ied
to
m
ai
n
tai
n
d
y
n
a
m
ic
r
e
s
p
o
n
s
e
o
f
HVD
C
s
y
s
te
m
[
2
2
]
an
d
to
p
r
o
tect
th
e
H
VDC
d
ev
ic
es
f
r
o
m
t
h
e
s
h
o
r
t
cir
cu
it
[
2
3
]
.
Fu
r
th
er
m
o
r
e,
A
N
FIS
p
o
w
er
s
y
s
te
m
s
tab
ilizer
(
P
SS
)
h
as
b
ee
n
ap
p
lied
to
i
m
p
r
o
v
e
th
e
s
tab
ilit
y
o
f
s
in
g
le
m
ac
h
in
e
b
ased
o
n
f
ee
d
b
ac
k
lin
ea
r
izat
io
n
[
2
4
]
.
A
cc
o
r
d
in
g
to
w
id
e
r
an
g
e
u
s
a
b
ilit
y
an
d
p
r
ev
io
u
s
r
esear
ch
o
f
th
e
ANFI
S
a
n
d
T
2
FL
S
ap
p
licatio
n
s
i
n
p
o
w
er
s
y
s
te
m
.
W
e
ai
m
to
ev
a
lu
ate
t
h
e
co
n
tr
o
l
s
tr
ateg
y
o
f
ANFI
S
an
d
T
2
FL
S
-
b
ased
p
o
w
e
r
s
y
s
te
m
s
tab
il
izer
to
i
m
p
r
o
v
e
s
tab
ilit
y
o
f
a
lar
g
e
-
s
ca
le
p
o
w
er
s
y
s
te
m
i
n
t
h
is
r
e
s
ea
r
ch
.
W
h
er
e,
th
e
ANFI
S
m
e
th
o
d
is
u
s
ed
in
t
h
i
s
r
esear
ch
b
ec
au
s
e
th
e
A
NFI
S
is
ab
le
to
tr
a
i
n
f
r
o
m
ti
m
e
-
s
er
ies
d
ata.
P
ar
am
eter
s
o
f
ANFI
S
co
n
tr
o
ller
ar
e
o
b
tain
ed
b
y
tr
ain
i
n
g
p
r
o
ce
s
s
es
in
o
f
f
-
li
n
e
m
o
d
e.
T
h
e
p
ar
am
eter
s
o
f
ANFI
S
co
n
tr
o
ller
ar
e
a
d
j
u
s
ted
au
to
m
at
icall
y
d
u
r
in
g
t
h
e
tr
ai
n
in
g
p
r
o
ce
s
s
e
s
u
s
i
n
g
d
ata
tr
ai
n
in
g
.
T
h
e
d
ata
tr
ain
i
n
g
is
o
b
tai
n
ed
b
y
s
i
m
u
lati
n
g
th
e
s
y
s
te
m
eq
u
ip
p
ed
w
i
th
co
n
v
e
n
tio
n
al
P
SS
an
d
lo
ad
ca
p
ac
it
y
o
f
t
h
e
L
SP
S
is
v
ar
ied
.
Me
an
w
h
ile,
T
2
FLS
m
et
h
o
d
is
u
s
ed
in
th
i
s
r
esear
ch
b
ec
au
s
e
th
e
T
2
FL
S
is
ab
le
to
co
v
er
th
e
in
p
u
t,
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
,
an
d
o
u
tp
u
t
w
i
th
u
n
ce
r
tain
t
y
.
Als
o
,
T
2
FL
S
m
e
m
b
e
r
s
h
ip
f
u
n
ctio
n
an
d
r
u
le
s
ar
e
d
esi
g
n
ed
b
ased
o
n
th
e
k
n
o
w
led
g
e
o
f
t
h
e
d
esi
g
n
er
.
T
h
e
p
ap
er
is
o
r
g
an
ized
a
s
f
o
llo
w
s
:
Stab
ili
t
y
o
f
a
p
o
w
er
s
y
s
te
m
i
s
d
esc
r
ib
ed
in
Sect
io
n
2
.
Desig
n
o
f
A
N
FIS
an
d
T
2
FLS
-
b
ased
p
o
w
er
s
y
s
te
m
s
tab
il
i
ze
r
is
ex
p
lain
ed
in
Sec
tio
n
3
.
Nex
t,
r
esu
lt
a
n
d
d
is
cu
s
s
io
n
ar
e
p
r
esen
ted
i
n
Se
ctio
n
4
.
An
d
,
th
e
co
n
cl
u
s
io
n
is
p
r
o
v
id
ed
in
th
e
last
s
ec
t
io
n
.
2.
P
O
WE
R
SY
ST
E
M
ST
AB
I
L
I
T
Y
Stab
ilit
y
is
d
ef
in
ed
as
th
e
ab
il
it
y
o
f
p
o
w
er
s
y
s
te
m
to
co
v
er
th
e
d
is
t
u
r
b
an
ce
at
n
o
r
m
al
o
p
er
atio
n
th
e
ef
f
o
r
t
to
m
ai
n
tain
th
e
p
o
w
er
s
y
s
te
m
g
o
i
n
g
to
s
tead
y
s
tate
af
ter
th
e
d
i
s
t
u
r
b
an
ce
is
d
is
ap
p
ea
r
ed
.
Dy
n
a
m
ica
l
b
eh
av
io
r
o
f
a
s
i
n
g
le
m
ac
h
i
n
e
co
n
n
ec
ted
to
in
f
i
n
ite
b
u
s
is
d
ep
en
d
ed
o
n
in
ter
ac
tio
n
o
f
t
u
r
b
in
e,
g
e
n
er
ato
r
,
also
th
e
co
n
tr
o
ller
ch
ar
ac
ter
is
tic
s
u
c
h
as
g
o
v
er
n
o
r
an
d
ex
cita
tio
n
s
y
s
te
m
s
.
D
y
n
a
m
ica
l
o
f
a
s
in
g
le
m
ac
h
i
n
e
f
o
r
m
u
las i
n
li
n
ea
r
m
o
d
el
ar
e
as f
o
llo
w
s
[
2
5
]
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
76
–
86
78
̇
(
1
)
̇
(
2
)
A
lar
g
e
-
s
ca
le
p
o
w
er
s
y
s
te
m
(
L
SP
S)
co
n
s
i
s
ts
o
f
t
w
o
o
r
m
o
r
e
lo
ca
l
p
o
w
er
s
y
s
te
m
ar
ea
s
.
T
o
p
o
lo
g
y
o
f
ex
is
t
in
g
L
SP
S
u
s
u
al
l
y
f
o
llo
ws
th
e
g
eo
g
r
ap
h
ic
s
u
r
f
ac
es.
W
h
ich
i
s
t
h
e
lo
ca
l
ar
ea
is
co
n
n
e
cted
to
th
e
o
th
er
b
y
tr
an
s
m
is
s
io
n
li
n
e
to
f
o
r
m
t
h
e
L
SP
S.
T
h
e
L
SP
S
i
n
t
h
i
s
r
e
s
ea
r
ch
i
s
ta
k
e
n
f
r
o
m
P
ad
iy
ar
[
2
6
]
.
T
h
is
s
y
s
te
m
co
n
s
is
t
o
f
3
9
b
u
s
es
a
n
d
1
0
m
a
ch
in
e
s
.
F
u
r
th
er
m
o
r
e,
th
e
s
y
s
te
m
w
as
s
i
m
p
lifie
d
to
A
r
ea
I
,
Ar
ea
I
I
an
d
A
r
ea
I
I
I
,
an
d
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
T
h
e
Ma
ch
i
n
e
-
1
(
M
1
)
at
B
u
s
1
w
as
tr
ea
ted
as
a
r
ef
er
en
ce
b
u
s
.
Fu
r
th
er
m
o
r
e,
th
e
s
p
ee
d
an
d
an
g
le
r
o
to
r
d
ev
iatio
n
w
a
s
tak
en
a
s
ze
r
o
v
alu
e
s
,
r
esp
ec
tiv
el
y
.
Fig
u
r
e
1
.
A
lar
g
e
-
s
ca
le
p
o
w
er
s
y
s
te
m
P
o
w
er
s
y
s
te
m
s
tab
ilizer
(
P
SS
)
p
r
o
v
id
es
ad
d
itio
n
al
d
a
m
p
in
g
to
r
o
to
r
o
s
cillatio
n
o
f
m
ac
h
in
e
b
y
r
eg
u
lat
in
g
it
s
ex
ci
tatio
n
s
y
s
t
e
m
t
h
r
o
u
g
h
a
n
ad
d
itio
n
al
s
ta
b
ilizin
g
s
i
g
n
al.
T
h
e
P
SS
p
r
o
d
u
ce
s
an
elec
tr
ical
to
r
q
u
e
co
m
p
o
n
e
n
t
i
n
p
h
ase
w
i
th
th
e
r
o
to
r
s
p
ee
d
d
ev
iatio
n
to
p
r
o
v
id
e
th
e
ad
d
itio
n
al
d
am
p
i
n
g
to
r
q
u
e.
T
h
e
P
SS
is
v
er
y
i
m
p
o
r
tan
t
to
i
m
p
r
o
v
e
s
tab
ilit
y
o
f
o
v
er
all
p
o
w
er
s
y
s
te
m
s
.
Si
n
ce
th
e
P
SS
is
to
in
tr
o
d
u
ce
a
d
am
p
in
g
to
r
q
u
e
co
m
p
o
n
e
n
t,
a
lo
g
ical
s
i
g
n
al
to
u
s
e
f
o
r
r
eg
u
lat
in
g
ex
c
it
atio
n
s
y
s
te
m
o
f
m
ac
h
in
e
i
s
r
o
to
r
s
p
ee
d
d
ev
iatio
n
.
An
d
,
o
u
tp
u
t
o
f
th
e
P
SS
is
th
e
ad
d
itio
n
al
v
o
ltag
e
co
m
p
en
s
atio
n
th
at
f
ee
d
in
g
to
th
e
ex
citatio
n
s
y
s
te
m
.
C
o
n
v
en
t
io
n
al
P
SS
co
n
s
i
s
t
o
f
g
ain
,
w
as
h
o
u
t
a
n
d
p
h
a
s
e
co
m
p
en
s
atio
n
b
lo
ck
s
.
T
h
e
g
a
in
b
l
o
ck
d
eter
m
i
n
es
t
h
e
a
m
o
u
n
t
o
f
d
a
m
p
i
n
g
in
tr
o
d
u
ce
b
y
t
h
e
P
SS
.
T
h
e
s
ig
n
al
w
as
h
o
u
t
b
lo
ck
s
er
v
e
s
as
a
h
i
g
h
f
r
e
q
u
en
c
y
f
i
lter
,
w
i
th
th
e
ti
m
e
co
n
s
tan
t
T
.
T
h
e
p
h
ase
co
m
p
e
n
s
at
io
n
b
lo
ck
p
r
o
v
id
es
ap
p
r
o
p
r
iate
p
h
ase
lead
ch
ar
ac
ter
is
tic
to
co
m
p
e
n
s
ate
t
h
e
p
h
a
s
e
lag
b
et
w
ee
n
ex
ci
ter
in
p
u
t a
n
d
g
e
n
er
at
o
r
(
air
-
g
ap
)
elec
tr
ical
to
r
q
u
e.
3.
DE
SA
I
N
O
F
P
SS
B
ASE
D
O
N
ARTI
F
I
CI
AL
I
NT
E
L
L
I
G
E
NT
3
.
1
.
P
SS
ba
s
ed
o
n AN
F
I
S
C
o
ntr
o
ller
An
ad
ap
ti
v
e
n
eu
r
o
-
f
u
zz
y
i
n
f
er
en
ce
s
y
s
te
m
o
r
ad
ap
tiv
e
n
et
w
o
r
k
-
b
ased
f
u
zz
y
in
f
er
e
n
ce
s
y
s
te
m
(
A
NFI
S)
is
a
k
i
n
d
o
f
ar
ti
f
icial
n
eu
r
al
n
et
w
o
r
k
t
h
at
i
s
b
ased
o
n
T
ak
ag
i
–
Su
g
e
n
o
f
u
zz
y
i
n
f
er
en
ce
s
y
s
te
m
.
Si
n
ce
it
i
n
teg
r
ate
s
b
o
th
n
e
u
r
al
n
et
wo
r
k
s
a
n
d
f
u
zz
y
lo
g
ic
p
r
in
c
ip
les
,
it
h
as
p
o
ten
tia
l
to
ca
p
t
u
r
e
t
h
e
b
en
e
f
it
s
o
f
b
o
t
h
m
o
d
el
s
in
a
s
i
n
g
le
f
r
a
m
e
w
o
r
k
.
I
ts
in
f
er
e
n
ce
s
y
s
te
m
co
r
r
esp
o
n
d
s
to
a
s
et
o
f
f
u
zz
y
I
F
-
T
HE
N
r
u
les
th
a
t
h
a
v
e
lear
n
in
g
ca
p
ab
ilit
y
to
ap
p
r
o
x
i
m
ate
n
o
n
li
n
ea
r
f
u
n
c
tio
n
s
.
Hen
ce
,
A
N
FIS
is
co
n
s
id
er
ed
to
b
e
a
u
n
iv
er
s
al
e
s
ti
m
ato
r
.
T
h
e
A
NFI
S
co
n
s
is
t
s
o
f
p
r
em
is
e
an
d
co
n
s
eq
u
e
n
ce
p
ar
a
m
eter
s
.
T
h
e
A
N
FIS
f
u
n
ctio
n
i
s
s
a
m
e
as
th
e
f
u
zz
y
r
u
le
b
ased
o
n
Su
g
e
n
o
alg
o
r
ith
m
.
So
,
b
o
th
th
e
p
ar
a
m
eter
s
ar
e
o
b
tain
ed
b
y
o
f
f
-
li
n
e
lear
n
in
g
p
r
o
ce
s
s
e
s
w
ith
lea
s
t
s
q
u
ar
e
s
esti
m
ati
o
n
(
L
SE)
an
d
b
ac
k
-
p
r
o
p
ag
atio
n
alg
o
r
ith
m
s
.
At
f
o
r
w
ar
d
s
tep
,
th
e
p
ar
a
m
eter
s
ar
e
id
en
tifie
d
b
y
u
s
i
n
g
L
S
E
m
et
h
o
d
.
An
d
,
at
b
ac
k
w
ar
d
s
tep
t
h
e
p
ar
a
m
eter
s
ar
e
m
ai
n
tai
n
ed
b
y
u
s
i
n
g
g
r
ad
ien
t
d
esce
n
t
o
p
ti
m
izatio
n
.
Su
p
p
o
s
e
th
at
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
C
o
o
r
d
in
a
tio
n
o
f A
d
a
p
tive
N
eu
r
o
F
u
z
z
y
I
n
feren
ce
S
ystem
(
A
N
F
I
S
)
a
n
d
Typ
e
-
2
F
u
z
z
y
….
(
A
g
u
n
g
B
.
Mu
ljo
n
o
)
79
A
N
FIS
n
et
w
o
r
k
h
a
s
2
(
t
w
o
)
i
n
p
u
t
s
x,
y
a
n
d
a
n
o
u
tp
u
t
O
,
w
i
th
2
(
t
w
o
)
r
u
les
b
ased
o
n
f
ir
s
t
-
o
r
d
er
f
u
zz
y
m
o
d
el
Su
g
en
o
[
2
7
]
:
(
3
)
Ou
tp
u
t o
f
th
e
ANFI
S
-
n
e
t
w
o
r
k
is
f
o
r
m
u
lated
f
o
llo
w
:
∑
∑
∑
(
4
)
T
h
e
A
N
FIS
-
b
ased
P
SS
i
s
d
esi
g
n
ed
b
y
s
o
m
e
lear
n
i
n
g
p
r
o
ce
s
s
es
i
n
o
f
f
-
lin
e
m
o
d
e.
Data
tr
ai
n
in
g
th
a
t
u
s
ed
f
o
r
th
i
s
lear
n
in
g
p
r
o
ce
s
s
w
er
e
o
b
tain
ed
b
y
s
i
m
u
lati
n
g
t
h
e
co
n
v
en
tio
n
al
P
S
S.
T
o
o
b
tai
n
t
h
e
d
ata
tr
ai
n
in
g
,
th
e
L
SP
S
is
eq
u
ip
p
ed
b
y
co
n
v
en
t
io
n
al
P
SS
.
T
h
e
r
esu
lt
o
f
t
h
e
tr
ain
i
n
g
s
tag
e
w
a
s
th
e
A
N
FIS
-
b
ased
P
SS
an
d
th
is
P
SS
w
a
s
ap
p
lied
to
r
ep
lace
th
e
co
n
v
e
n
tio
n
al
P
SS
.
An
d
,
t
h
e
ANFI
S
-
b
ased
P
SS
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
f
o
r
r
o
to
r
s
p
ee
d
d
ev
iatio
n
is
ill
u
s
tr
ated
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
Me
m
b
er
s
h
ip
f
u
n
ctio
n
f
o
r
in
p
u
t
A
NFI
S o
f
3
3
.
2
.
P
SS
ba
s
ed
o
n
T
y
pe
-
2
F
uzzy
L
o
g
ic
Sy
s
t
e
m
(
T
2
F
L
S)
C
o
nt
ro
ller
T
y
p
e
-
2
f
u
zz
y
s
y
s
te
m
is
a
class
o
f
f
u
zz
y
lo
g
ic
s
y
s
te
m
w
h
ic
h
th
e
an
tece
d
e
n
t
o
r
c
o
n
s
eq
u
e
n
t
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
ar
e
t
y
p
e
-
2
f
u
zz
y
s
ets.
T
h
e
co
n
ce
p
t
o
f
a
t
y
p
e
-
2
f
u
zz
y
s
et
is
d
e
f
i
n
ed
as
an
ex
ten
s
io
n
o
f
th
e
co
n
ce
p
t
o
f
a
n
o
r
d
in
ar
y
s
et
(
h
en
ce
f
o
r
th
ca
ll
a
t
y
p
e
-
1
f
u
z
z
y
s
e
t)
.
T
h
e
s
tr
u
ct
u
r
e
o
f
a
T
2
FLS
is
q
u
ite
s
i
m
ilar
to
a
T
1
FL
S.
T
h
e
d
if
f
er
e
n
ce
i
s
th
at
o
n
t
h
e
a
n
tece
d
en
t
a
n
d
/o
r
co
n
s
eq
u
e
n
t
s
ets
o
f
th
e
T
2
FL
S a
r
e
t
y
p
e
-
2
a
n
d
ea
c
h
r
u
le
o
u
tp
u
t
s
et
is
a
t
y
p
e
-
2
al
s
o
.
T
h
e
T
2
FL
S
co
n
s
i
s
t
o
f
f
i
v
e
p
ar
ts
s
u
ch
a
s
[
2
8
]
,
[
2
9
]
:
Fu
zz
i
f
ier
,
r
u
le
b
ase,
in
f
er
en
ce
en
g
i
n
e,
t
y
p
e
r
ed
u
ce
r
an
d
d
ef
u
zz
if
ier
p
ar
ts
.
B
lo
ck
d
iag
r
a
m
o
f
t
y
p
e
-
2
FLS
an
d
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
o
f
in
p
u
t
3
ar
e
s
h
o
w
n
in
Fig
u
r
e
3
(
a)
an
d
Fig
u
r
e
3
(
b
)
,
r
esp
e
ctiv
el
y
.
Fig
u
r
e
3
.
P
SS
b
ased
o
n
th
e
t
y
p
e
-
2
lo
g
ic
f
u
zz
y
s
y
s
te
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
76
–
86
80
a
.
Fu
zz
i
f
ier
T
h
e
f
u
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f
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s
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f
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ase
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th
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s
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tten
a
s
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llo
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s
:
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is
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,
.
.
.
,
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f
is
T
h
en
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(
5
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en
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h
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Fo
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I
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r
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d
r
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h
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m
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p
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d
:
[
]
[
]
(
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h
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X
0
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th
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s
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n
in
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d
ar
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.
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R
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g
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h
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t
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in
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te
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to
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t
y
p
e
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1
s
e
t.
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h
er
e,
c
en
ter
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f
s
ets
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C
O
S)
t
y
p
e
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ed
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ctio
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m
e
th
o
d
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co
n
s
id
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is
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h
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h
is
m
eth
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d
is
m
o
r
e
ef
f
ec
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v
e
co
m
p
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tatio
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a
n
o
th
er
m
eth
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d
s
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d
r
eq
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ir
es less
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i
m
e
co
m
p
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tatio
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.
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h
e
C
OS
m
et
h
o
d
is
w
r
itte
n
as
f
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s
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h
er
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th
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al
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et
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eter
m
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y
t
w
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p
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d
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[
]
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p
e
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2
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e
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t set
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n
d
,
[
]
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ir
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g
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ter
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Def
u
zz
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f
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h
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i
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ts
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m
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p
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ig
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4.
RE
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I
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e
o
f
8
.
4
8
an
d
th
e
s
ettlin
g
ti
m
e
at
ti
m
e
o
f
5
.
4
6
s
.
Me
an
w
h
ile,
th
e
A
N
FIS
-
P
SS
an
d
C
ONV
-
P
SS
g
av
e
p
ea
k
o
v
er
s
h
o
o
t
at
t
h
e
v
alu
e
s
o
f
1
0
.
2
5
an
d
1
0
.
2
3
,
r
esp
ec
tiv
el
y
.
T
h
e
s
ettli
n
g
ti
m
e
o
f
t
h
e
A
N
FIS
-
P
S
S
an
d
C
ONV
-
P
SS
w
as
o
b
tai
n
ed
at
th
e
ti
m
es
o
f
6
.
0
9
an
d
6
.
6
5
s
.
Stead
y
s
tate
o
f
th
e
r
o
to
r
an
g
le
w
a
s
ac
h
ie
v
ed
at
t
h
e
v
a
lu
e
o
f
6
.
3
9
f
o
r
t
h
e
T
2
FL
S
-
P
SS
,
a
n
d
5
.
9
8
f
o
r
th
e
A
N
FIS
-
an
d
C
ONV
-
P
SS
.
Fro
m
F
ig
u
r
e
6
(
a)
an
d
T
ab
le
1
w
e
s
ee
t
h
at
p
ea
k
o
v
er
s
h
o
o
t
o
f
th
e
T
2
FL
S
-
P
SS
w
a
s
ac
h
i
ev
ed
at
th
e
v
alu
e
o
f
6
.
5
3
10
3
p
u
f
o
r
th
e
r
o
to
r
s
p
ee
d
d
ev
iatio
n
(
9
)
.
On
t
h
e
o
t
h
er
h
a
n
d
,
th
e
p
ea
k
o
v
er
s
h
o
o
t
o
f
th
e
A
N
FIS
-
P
SS
an
d
C
ONV
-
P
SS
w
er
e
ac
h
iev
ed
at
t
h
e
v
al
u
e
s
o
f
6
.
6
5
an
d
7
.
0
9
1
0
3
p
u
.
T
h
e
s
ettli
n
g
ti
m
e
w
a
s
o
b
tain
ed
at
th
e
ti
m
es
o
f
5
.
2
4
,
5
.
7
4
an
d
5
.
9
8
s
,
r
esp
ec
tiv
el
y
.
Fu
r
th
er
m
o
r
e,
th
e
r
esp
o
n
s
e
s
f
o
r
th
e
r
o
to
r
an
g
le
d
ev
iatio
n
(
9
)
is
s
h
o
w
n
in
F
i
g
u
r
e
6
(
b
)
an
d
lis
ted
in
T
ab
le
1
.
Si
m
u
latio
n
r
es
u
lt
o
n
t
h
e
First
Scen
ar
io
s
h
o
w
s
t
h
at
t
h
e
T
2
FL
S
-
P
SS
is
ab
le
to
i
m
p
r
o
v
e
s
t
ab
ilit
y
b
y
o
b
s
er
v
in
g
t
h
e
r
o
to
r
s
p
ee
d
an
d
an
g
le
d
ev
iatio
n
.
T
h
e
T
2
F
L
S
-
P
SS
g
i
v
es
p
ea
k
o
v
er
s
h
o
o
t
s
m
aller
t
h
an
th
e
A
N
FIS/
C
ONV
-
P
SS
f
o
r
b
o
th
r
o
to
r
s
p
ee
d
an
d
an
g
le.
A
l
s
o
,
th
e
s
ettli
n
g
ti
m
e
o
f
th
e
T
2
FL
S
-
P
SS
is
s
h
o
r
ter
t
h
an
th
e
o
th
er
P
SS
(
s
)
as
w
ell
a
s
f
o
r
th
e
lo
ca
l
m
o
d
e
o
s
cillatio
n
a
s
w
ell
a
s
in
ter
-
ar
ea
m
o
d
e
o
s
cilla
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
76
–
86
82
Fig
u
r
e
6
.
P
er
f
o
r
m
a
n
ce
o
f
r
esp
ec
tiv
e
P
SS
f
o
r
t
h
e
(
a)
9
an
d
(
b
)
9
r
esp
o
n
s
es
T
ab
le
1
.
P
er
f
o
r
m
a
n
ce
o
f
co
n
v
en
tio
n
al,
A
N
FIS
an
d
T
2
FL
S
-
P
SS
to
i
m
p
r
o
v
e
s
tab
ili
t
y
o
f
a
L
SP
S
PSS
-
t
y
p
e
3
3
P
e
a
k
o
v
e
r
sh
o
o
t
[
Po
]
S
e
t
t
l
i
n
g
t
i
me
Po
[
t
st
]
Ss
×
(
−
10
−
3
)
(
p
u
)
[
t
st
]
(
s)
(
)
(
s)
(
)
C
O
N
V
-
PSS
1
9
.
8
5
5
.
9
7
3
4
.
5
3
6
.
4
4
A
N
F
I
S
-
PSS
2
1
.
4
2
5
.
7
2
3
5
.
5
6
5
.
8
9
1
9
.
5
2
T
2
F
L
S
-
PSS
2
0
.
1
4
5
.
2
5
3
5
.
9
4
5
.
6
2
7
7
C
O
N
V
-
PSS
1
9
.
8
5
5
.
9
7
3
4
.
5
3
6
.
4
4
A
N
F
I
S
-
PSS
2
1
.
4
2
5
.
7
2
3
5
.
5
6
5
.
8
9
1
9
.
5
2
T
2
F
L
S
-
PSS
2
0
.
1
4
5
.
2
5
3
5
.
9
4
5
.
6
2
9
9
C
O
N
V
-
PSS
1
9
.
8
5
5
.
9
7
3
4
.
5
3
6
.
4
4
A
N
F
I
S
-
PSS
2
1
.
4
2
5
.
7
2
3
5
.
5
6
5
.
8
9
1
9
.
5
2
T
2
F
L
S
-
PSS
2
0
.
1
4
5
.
2
5
3
5
.
9
4
5
.
6
2
4
.
2
.
E
v
a
lua
t
io
n o
f
R
esp
ec
t
iv
e
P
SS
o
n
M
ultiple
D
is
t
urba
nces
Seco
n
d
Scen
ar
io
,
w
e
al
s
o
ev
al
u
ate
t
h
e
r
esp
ec
tiv
e
P
SS
(
s
)
b
y
f
o
r
cin
g
2
(
t
w
o
)
m
ec
h
a
n
ical
d
i
s
tu
r
b
an
ce
s
to
th
e
Ma
c
h
i
n
e
-
2
s
eq
u
e
n
tiall
y
o
n
0
.
0
8
an
d
0
.
0
9
p
u
at
0
.
0
an
d
8
.
0
s
.
Stab
ilit
y
i
m
p
r
o
v
e
m
e
n
t
o
f
M
3
,
M
7
a
n
d
M
9
is
o
b
s
er
v
ed
o
n
d
y
n
a
m
ic
r
esp
o
n
s
e
s
o
f
r
o
to
r
s
p
ee
d
an
d
an
g
le
d
ev
iatio
n
.
T
h
ese
d
y
n
a
m
i
c
r
es
p
o
n
s
es
ar
e
s
h
o
w
n
i
n
Fig
u
r
e
7
,
Fig
u
r
e
8
an
d
Fig
u
r
e
9
.
A
ls
o
,
th
e
r
esp
o
n
s
es o
f
r
esp
e
ctiv
e
P
SS
(
s
)
ar
e
lis
ted
in
T
ab
le
2
.
Fig
u
r
e
7
(
a)
an
d
T
a
b
le
2
s
h
o
w
t
h
e
d
y
n
a
m
ic
r
esp
o
n
s
e
o
f
t
h
e
C
ONV
-
P
SS
o
s
cillated
w
it
h
a
m
p
lit
u
d
e
b
et
w
ee
n
1
1
.
2
3
an
d
4
.
6
0
10
3
p
u
.
T
h
en
,
th
i
s
a
m
p
lit
u
d
e
was
d
ec
r
ea
s
ed
u
n
til
t
h
e
r
esp
o
n
s
e
ac
h
ie
v
ed
s
tead
y
s
tate
at
ti
m
e
m
o
r
e
t
h
a
n
1
5
.
0
s
.
D
y
n
a
m
ic
r
esp
o
n
s
e
o
f
t
h
e
A
N
FIS
-
P
SS
also
o
s
c
illated
with
a
m
p
lit
u
d
e
at
t
h
e
v
alu
e
s
b
et
w
ee
n
9
.
7
5
an
d
3
.
1
9
10
3
p
u
.
On
t
h
e
o
th
er
h
a
n
d
,
d
y
n
a
m
ic
r
esp
o
n
s
e
o
f
T
2
FL
S
-
P
SS
w
e
n
t
to
f
ir
s
t
s
w
in
g
a
n
d
th
i
s
r
esp
o
n
s
e
i
n
cr
ea
s
ed
r
ap
id
ly
to
ze
r
o
(
n
o
m
i
n
al
s
p
ee
d
)
.
T
h
is
r
esp
o
n
s
e
w
as
ab
le
to
ac
h
iev
ed
s
tead
y
s
tate
at
4
.
5
1
an
d
1
2
.
1
7
s
f
o
r
f
ir
s
t a
n
d
s
ec
o
n
d
d
is
tu
r
b
an
ce
s
,
r
esp
ec
tiv
el
y
.
P
ea
k
o
v
er
s
h
o
o
t o
f
th
e
T
2
FL
S
-
P
SS
f
o
r
th
e
s
ec
o
n
d
d
is
t
u
r
b
an
ce
w
a
s
ac
h
iev
ed
a
t
t
h
e
v
a
lu
e
o
f
6
.
5
2
10
3
p
u
.
T
h
e
r
o
to
r
an
g
le
(
3
)
r
esp
o
n
s
es
o
f
t
h
e
al
l
P
SS
(
s
)
ar
e
al
m
o
s
t
s
i
m
ilar
.
T
h
e
p
ea
k
o
v
er
s
h
o
o
t
(
P
o
1
)
o
f
t
h
e
T
2
FL
S
-
P
SS
,
ANFI
S
-
P
SS
a
n
d
C
ONV
-
P
SS
w
er
e
ac
h
iev
ed
at
t
h
e
v
al
u
es
o
f
2
1
.
3
4
,
2
1
.
3
2
an
d
2
1
.
8
3
,
r
esp
e
ctiv
el
y
.
Nex
t,
th
e
r
o
to
r
an
g
le
(
3
)
ac
h
iev
ed
P
o
2
at
th
e
v
a
lu
e
s
o
f
4
0
.
3
0
,
4
0
.
3
2
an
d
4
1
.
2
3
f
o
r
th
e
T
2
FL
S
-
P
SS
,
A
N
FIS
-
P
SS
a
n
d
C
O
NV
-
P
SS
.
An
d
,
th
es
e
r
esp
o
n
s
es
ac
h
ie
v
ed
s
tead
y
s
ta
te
a
t
ar
o
u
n
d
3
6
.
0
4
an
d
3
6
.
2
0
.
T
h
is
3
r
esp
o
n
s
e
is
s
h
o
w
n
i
n
Fi
g
u
r
e
7
(
b
)
.
T
h
e
p
er
f
o
r
m
an
ce
s
o
f
t
h
e
all
P
SS
(
s
)
ar
e
lis
ted
in
T
ab
le
2
.
Fig
u
r
e
8
(
a)
an
d
T
ab
le
2
s
h
o
w
th
e
d
y
n
a
m
ic
r
esp
o
n
s
e
o
f
r
o
to
r
s
p
ee
d
d
ev
iatio
n
(
7
)
w
h
e
n
eq
u
ip
p
ed
b
y
t
h
e
C
ONV
-
P
SS
.
T
h
is
r
es
p
o
n
s
e
o
s
cillated
w
it
h
a
m
p
li
tu
d
e
b
et
w
ee
n
1
.
8
1
an
d
0
.
6
5
10
3
p
u
.
T
h
en
,
th
i
s
a
m
p
lit
u
d
e
w
as
d
ec
r
ea
s
ed
u
n
ti
l
th
e
th
e
r
esp
o
n
s
e
ac
h
ie
v
ed
s
t
ea
d
y
s
tate
at
ti
m
e
o
f
m
o
r
e
th
an
1
5
.
0
s
.
D
y
n
a
m
ic
r
esp
o
n
s
e
o
f
t
h
e
A
N
FIS
-
P
SS
also
o
s
c
illated
w
it
h
a
m
p
lit
u
d
e
at
t
h
e
v
al
u
es
b
et
w
ee
n
2
.
3
3
an
d
0
.
6
4
10
3
p
u
.
(
b
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
C
o
o
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in
a
tio
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f A
d
a
p
tive
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r
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F
u
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y
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n
feren
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S
ystem
(
A
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S
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Typ
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2
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u
z
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y
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(
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g
u
n
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.
Mu
ljo
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83
Me
an
w
h
ile,
t
h
e
d
y
n
a
m
ic
r
esp
o
n
s
e
o
f
T
2
FL
S
-
P
SS
w
as
n
o
t
o
s
cillated
an
d
th
is
r
e
s
p
o
n
s
e
was
ab
le
to
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h
ie
v
ed
s
tead
y
s
tate
at
4
.
4
8
an
d
1
2
.
1
5
s
f
o
r
f
ir
s
t
a
n
d
s
ec
o
n
d
d
i
s
t
u
r
b
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ce
s
,
r
esp
ec
ti
v
el
y
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e
ak
o
v
er
s
h
o
o
t
o
f
t
h
e
T
2
FLS
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P
SS
f
o
r
th
e
s
ec
o
n
d
d
is
t
u
r
b
an
c
e
P
o
2
w
as a
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h
ie
v
ed
at
th
e
v
a
lu
e
o
f
1
.
1
2
10
3
p
u
.
T
h
e
r
o
to
r
an
g
le
(
7
)
f
o
r
th
e
T
2
FL
S
-
P
SS
,
A
NFI
S
-
P
SS
a
n
d
C
ONV
-
P
SS
ac
h
ie
v
ed
th
e
p
ea
k
o
v
er
s
h
o
o
t
P
o
1
at
t
h
e
v
alu
e
o
f
6
.
6
8
,
6
.
3
8
an
d
6
.
4
1
.
Fu
r
th
er
m
o
r
e,
t
h
e
r
o
to
r
an
g
le
P
o
2
w
a
s
ac
h
ie
v
ed
at
th
e
v
al
u
es
o
f
1
2
.
4
5
,
1
2
.
1
0
an
d
1
2
.
2
7
,
r
esp
ec
tiv
el
y
.
A
n
d
,
t
h
e
s
tead
y
s
tate
w
a
s
ac
h
iev
ed
at
ar
o
u
n
d
1
1
.
4
6
an
d
1
1
.
0
6
f
o
r
th
e
all
P
SS
.
T
h
e
7
r
esp
o
n
s
e
i
s
s
h
o
w
n
i
n
Fi
g
u
r
e
8
(
b
)
an
d
lis
ted
in
T
ab
le
2
.
Fig
u
r
e
7
.
R
esp
o
n
s
e
s
o
f
t
h
e
(
a)
3
a
n
d
(
b
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3
f
o
r
ce
d
b
y
m
u
ltip
le
d
is
t
u
r
b
an
ce
s
Fig
u
r
e
8
.
P
er
f
o
r
m
a
n
ce
o
f
th
e
r
esp
ec
tiv
e
P
SS
o
n
(
a)
7
a
n
d
(
b
)
7
D
y
n
a
m
ic
r
esp
o
n
s
e
s
o
f
(
9
)
f
o
r
th
e
r
esp
ec
ti
v
e
P
SS
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
9
(
a)
an
d
T
ab
le
2
.
Oscill
atio
n
o
cc
u
r
r
ed
o
n
t
h
e
C
ONV
-
P
SS
r
esp
o
n
s
e
w
it
h
a
m
p
lit
u
d
e
b
et
w
ee
n
4
.
7
4
an
d
3
.
4
2
10
3
p
u
.
T
h
is
r
esp
o
n
s
e
w
as
d
ec
r
ea
s
ed
an
d
ac
h
iev
ed
t
h
e
s
et
tli
n
g
ti
m
e
at
m
o
r
e
t
h
a
n
8
s
(
t
st
1
)
an
d
1
5
s
(
t
st
2
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,
f
o
r
f
ir
s
t
a
n
d
s
ec
o
n
d
d
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t
u
r
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ce
s
,
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y
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T
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e
o
s
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tio
n
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ls
o
ap
p
ea
r
ed
o
n
th
e
A
NFI
S
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P
SS
r
esp
o
n
s
e
w
it
h
a
m
p
lit
u
d
e
at
th
e
v
al
u
es
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et
w
ee
n
4
.
4
6
a
n
d
1
.
1
3
10
3
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u
.
B
u
t,
th
e
am
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litu
d
e
o
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th
e
A
N
FIS
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P
SS
r
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n
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e
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les
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th
a
n
th
e
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m
p
lit
u
d
e
o
f
th
e
C
ONV
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P
SS
r
esp
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n
s
e.
Mo
r
eo
v
er
,
t
h
e
r
o
to
r
s
p
ee
d
(
9
)
th
at
s
y
s
te
m
eq
u
ip
p
ed
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y
th
e
T
2
FL
S
-
P
SS
ac
h
ie
v
ed
p
ea
k
o
v
er
s
h
o
o
t
at
th
e
v
a
lu
e
s
o
f
2
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0
2
(
P
o
1
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an
d
2
.
3
0
10
3
(
P
o
2
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p
u
.
R
esp
o
n
s
e
o
f
T
2
FL
S
-
P
SS
is
b
etter
th
an
t
h
e
o
th
er
P
SS
(
s
)
.
Fig
u
r
e
9
.
E
n
h
a
n
ce
m
e
n
t
s
tab
ilit
y
o
f
t
h
e
(
a)
9
an
d
(
b
)
9
b
y
u
s
in
g
t
h
e
T
2
FL
S
-
P
SS
Evaluation Warning : The document was created with Spire.PDF for Python.
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,
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1
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Feb
r
u
ar
y
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76
–
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84
T
h
e
r
o
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g
le
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9
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f
o
r
th
e
T
2
FL
S
-
P
SS
,
A
NFI
S
-
P
SS
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n
d
C
ONV
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P
SS
ac
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ie
v
ed
th
e
p
ea
k
o
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er
s
h
o
o
t
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P
o
1
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th
e
v
alu
e
s
o
f
1
0
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0
4
,
9
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5
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d
9
.
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1
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g
ain
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h
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th
e
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ec
o
n
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d
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r
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ce
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s
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r
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ed
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k
o
v
er
s
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o
o
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o
f
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h
e
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d
1
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3
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e
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1
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o
r
th
e
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2
FL
S
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SS
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A
NFI
S
-
P
SS
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n
d
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ONV
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P
SS
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s
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ie
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es
o
f
4
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2
4
,
4
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3
2
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d
5
.
4
4
s
.
Nex
t,
th
e
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n
g
ti
m
e
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st
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s
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ie
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at
t
h
e
ti
m
es
o
f
1
2
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1
1
,
1
2
.
2
9
an
d
1
3
.
0
7
s
.
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tate
(
S
s
1
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w
a
s
ac
h
iev
ed
at
th
e
v
alu
e
o
f
5
.
7
8
f
o
r
th
e
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2
FL
S
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SS
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d
at
th
e
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al
u
e
o
f
5
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3
9
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o
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e
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n
d
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SS
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n
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t
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f
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6
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6
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5
1
an
d
1
6
.
5
0
f
o
r
th
e
T
2
FL
S
-
P
SS
,
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S
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P
SS
an
d
C
ONV
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SS
,
r
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ec
tiv
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h
e
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er
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o
r
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ce
o
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r
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e
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SS
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n
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esp
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s
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g
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b
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b
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2
.
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h
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im
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h
t
h
e
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i
m
u
lat
io
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s
u
l
t
s
i
n
[
1
6
]
th
at
th
e
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et
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n
g
ti
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e
o
f
t
h
e
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SS
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e
s
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h
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e
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te
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d
5
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s
.
T
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2
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d
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PSS
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e
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s
2
×
[
−
10
−
3
]
(
p
u
)
(
s)
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(
)
(
s)
−
(
)
C
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N
V
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PSS
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.
1
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1
1
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2
3
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2
3
7
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8
1
4
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9
1
7
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7
1
3
6
.
2
A
N
F
I
S
-
PSS
8
.
0
3
9
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7
5
4
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8
5
1
2
.
4
9
2
1
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2
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0
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3
2
4
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2
1
2
.
4
7
1
7
.
6
5
3
6
.
1
2
T
2
F
L
S
-
PSS
6
.
5
2
7
.
8
6
4
.
5
1
1
2
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1
7
2
1
.
3
4
4
0
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3
0
4
.
7
9
1
2
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3
6
1
7
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5
2
3
6
.
0
4
7
7
C
O
N
V
-
PSS
1
.
8
1
2
.
0
9
>
8
.
0
>
1
5
.
0
6
.
4
1
1
2
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2
7
5
.
4
3
1
3
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1
5
5
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3
9
1
1
.
0
6
A
N
F
I
S
-
PSS
2
.
3
3
2
.
4
3
4
.
8
1
1
2
.
4
7
6
.
3
8
1
2
.
1
0
4
.
5
1
1
2
.
3
8
5
.
3
9
1
1
.
0
6
T
2
F
L
S
-
PSS
1
.
0
1
1
.
1
2
4
.
4
8
1
2
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1
5
6
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6
8
1
2
.
4
5
4
.
3
2
1
2
.
1
4
5
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7
8
1
1
.
4
6
9
9
C
O
N
V
-
PSS
4
.
7
4
5
.
4
5
>
8
.
0
>
1
5
.
0
9
.
7
1
1
8
.
4
3
5
.
4
1
1
3
.
0
7
5
.
3
9
1
6
.
5
0
A
N
F
I
S
-
PSS
4
.
4
6
4
.
7
4
4
.
7
5
1
2
.
3
9
9
.
7
5
1
8
.
6
1
4
.
3
2
1
2
.
2
9
5
.
3
9
1
6
.
5
1
T
2
F
L
S
-
PSS
2
.
0
2
2
.
3
4
.
3
6
1
1
.
9
7
1
0
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0
4
1
8
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6
8
4
.
2
4
1
2
.
1
1
5
.
7
8
1
6
.
6
5
5.
CO
NCLU
SI
O
N
T
h
e
co
o
r
d
in
atio
n
o
f
ANFI
S
-
P
SS
an
d
T
2
FL
S
-
P
SS
ar
e
ev
alu
ated
o
n
a
lar
g
e
-
s
ca
le
p
o
wer
s
y
s
te
m
(
L
SP
S).
T
h
e
d
esig
n
o
f
P
SS
b
ased
o
n
ANFI
S
a
n
d
T
2
FL
S
ar
e
also
ex
p
lo
r
ed
in
-
d
ep
th
i
n
t
h
is
r
esear
c
h
.
On
d
esig
n
in
g
p
r
o
ce
s
s
,
t
h
e
A
N
FIS
-
P
SS
p
ar
a
m
e
ter
s
ar
e
o
b
tai
n
ed
au
to
m
at
icall
y
t
h
r
o
u
g
h
lear
n
i
n
g
s
tag
e.
A
ll
th
e
d
ata
th
at
u
s
ed
i
n
t
h
e
lear
n
i
n
g
s
ta
g
e
ar
e
o
b
tain
ed
b
y
s
i
m
u
lati
n
g
t
h
e
L
SP
S
eq
u
ip
p
ed
b
y
co
n
v
en
t
i
o
n
al
P
SS
(
C
ONV
-
P
SS
)
w
it
h
v
ar
ied
lo
ad
ca
p
ac
it
y
.
T
h
e
lear
n
in
g
p
r
o
ce
s
s
es
ar
e
co
n
d
u
cted
in
o
f
f
-
li
n
e
m
o
d
e.
Me
an
w
h
ile,
t
h
e
T
2
FL
S
p
ar
am
e
ter
s
ar
e
d
eter
m
in
ed
b
y
d
esi
g
n
b
ased
o
n
t
h
e
k
n
o
w
led
g
e
o
f
th
e
d
es
ig
n
er
.
T
h
e
p
r
o
p
o
s
ed
P
SS
(
s
)
ar
e
test
ed
b
y
u
s
in
g
t
w
o
s
ce
n
ar
io
s
in
o
r
d
er
to
v
alid
it
y
o
f
th
e
m
o
d
el.
I
n
First
Scen
ar
io
,
th
e
s
y
s
te
m
i
s
f
o
r
ci
n
g
b
y
a
s
in
g
le
m
ec
h
an
ica
l d
is
t
u
r
b
an
ce
o
n
th
e
Ma
c
h
i
n
e
-
2
.
T
h
e
s
i
m
u
latio
n
r
e
s
u
l
ts
ar
e
o
b
s
er
v
ed
o
n
th
e
r
o
to
r
s
p
ee
d
an
d
an
g
le
o
f
th
e
Ma
ch
i
n
e
-
3
(
M
3
)
f
o
r
m
o
d
e
lo
ca
l
o
s
cil
latio
n
,
a
n
d
Ma
ch
in
e
-
7
(
M
7
)
a
n
d
Ma
c
h
i
n
e
-
9
(
M
9
)
f
o
r
m
o
d
e
in
ter
-
ar
ea
o
s
cillat
io
n
.
T
h
e
s
i
m
u
latio
n
r
es
u
lts
ar
e
as
f
o
llo
ws:
T
h
e
s
ettli
n
g
ti
m
e
o
f
th
e
r
o
to
r
s
p
ee
d
(
Δ
3
)
a
n
d
an
g
le
Δ
3
ar
e
o
b
tain
ed
at
th
e
t
i
m
es
o
f
5
.
2
5
an
d
5
.
6
2
s
,
r
esp
e
ctiv
el
y
,
f
o
r
T
2
FL
S
-
P
SS
.
T
h
e
s
tead
y
s
tate
v
al
u
e
s
f
o
r
th
e
Δ
3
r
e
s
p
o
n
s
es
o
f
all
th
e
P
SS
(
s
)
ar
e
o
b
tain
ed
at
−
1
9
.
5
2
.
W
h
en
m
u
ltip
le
d
is
tu
r
b
an
c
es
ar
e
f
o
r
ce
d
to
th
e
s
y
s
te
m
,
r
esp
o
n
s
es
o
f
th
e
T
2
FL
S
-
P
SS
ar
e
as
f
o
ll
o
w
s
:
T
h
e
s
e
ttli
n
g
ti
m
e
ar
e
o
b
tain
ed
at
4
.
5
1
s
(
t
st1
)
an
d
1
2
.
1
7
s
(
t
st
2
)
f
o
r
th
e
Δ
3
.
T
h
e
s
ettli
n
g
ti
m
e
ar
e
o
b
tain
ed
at
4
.
7
9
s
(
t
st
1
)
an
d
1
2
.
3
6
s
(
t
st
2
)
f
o
r
th
e
Δ
3
.
T
h
e
s
tead
y
s
tat
e
v
alu
e
s
ar
e
o
b
tain
ed
at
−
1
7
.
5
2
(
S
s
1
)
an
d
−
3
6
.
0
4
(
S
s
2
)
.
T
h
e
ev
alu
a
tio
n
o
n
in
ter
-
ar
ea
m
o
d
e
o
s
cillatio
n
(
A
r
ea
I
I
an
d
I
I
I
)
is
o
b
tain
ed
as
f
o
llo
w
s
:
T
h
e
(
Δ
7
)
an
d
Δ
7
ar
e
o
b
tain
ed
at
th
e
ti
m
e
s
o
f
5
.
3
1
an
d
5
.
4
6
s
f
o
r
th
e
T
2
FL
S
-
P
SS
.
T
h
e
s
tead
y
s
tate
v
a
lu
e
s
f
o
r
th
e
Δ
7
r
e
s
p
o
n
s
es
o
f
t
h
e
T
2
FLS
-
P
SS
is
o
b
tain
ed
at
6
.
3
9
.
T
h
e
(
Δ
9
)
an
d
Δ
9
ar
e
o
b
tain
ed
at
t
h
e
ti
m
es
o
f
5
.
2
4
an
d
5
.
4
3
s
f
o
r
t
h
e
T
2
FL
S
-
P
SS
.
T
h
e
ev
al
u
atio
n
is
a
ls
o
co
n
d
u
cted
o
n
m
u
l
tip
le
d
is
tu
r
b
an
ce
s
.
I
n
th
is
s
ce
n
ar
io
,
r
esp
o
n
s
es
o
f
th
e
C
ONV
-
P
S
S
an
d
A
NFI
S
-
P
SS
o
s
cil
lated
in
a
f
e
w
s
ec
o
n
d
.
Me
an
w
h
ile,
th
e
T
2
FLS
r
esp
o
n
s
e
f
o
r
th
e
(
Δ
3
)
ac
h
ie
v
ed
th
e
s
ettli
n
g
t
i
m
e
at
ti
m
e
s
o
f
4
.
7
0
an
d
1
2
.
3
6
s
f
o
r
th
e
f
ir
s
t
an
d
s
ec
o
n
d
d
i
s
tu
r
b
a
n
ce
s
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r
esp
ec
tiv
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T
h
e
p
ea
k
o
v
er
s
h
o
o
t
(
P
o
1
a
n
d
P
o
2
)
ar
e
ac
h
ie
v
ed
at
t
h
e
v
alu
e
s
o
f
−
6
.
5
2
an
d
−
7
.
8
6
×
10
−
3
p
u
.
I
t
is
co
n
clu
d
ed
th
at
th
e
T
2
FL
S
-
P
SS
g
i
v
es
b
etter
r
esp
o
n
s
es
t
h
an
t
h
e
o
th
er
P
SS
.
W
h
er
e,
p
ea
k
o
v
er
s
h
o
o
t
o
f
t
h
e
T
2
FL
S
-
P
SS
le
s
s
t
h
a
n
t
h
e
o
th
er
P
SS
an
d
th
e
s
ettli
n
g
ti
m
e
a
ls
o
s
h
o
r
ter
t
h
an
th
e
o
th
er
P
SS
.
ACK
NO
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D
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au
th
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Dir
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Ge
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H
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d
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th
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T
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Min
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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e.
RE
F
E
R
E
NC
E
S
[1
]
Am
iru
ll
a
h
a
n
d
A
.
Kis
wa
n
to
n
o
,
“
P
o
w
e
r
Qu
a
li
t
y
En
h
a
n
c
e
m
e
n
t
o
f
I
n
teg
ra
ti
o
n
P
h
o
to
v
o
lt
a
ic
Ge
n
e
ra
to
r
to
G
rid
u
n
d
e
r
V
a
riab
le
S
o
lar
Irra
d
ian
c
e
L
e
v
e
l
M
P
P
T
-
F
u
z
z
y
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
i
n
E
lec
trica
l
a
n
d
Co
mp
u
ter
En
g
i
n
e
e
rin
g
(
IJ
ECE
),
V
o
l.
6
.
No
.
6
.
p
p
.
2
6
2
9
-
2
6
4
2
,
2
0
1
6
.
[2
]
A
.
S
.
T
o
m
e
r
a
n
d
S
.
P
.
D
u
b
e
y
,
“
Re
sp
o
n
se
Ba
se
d
Co
m
p
a
ra
ti
v
e
A
n
a
l
y
sis
o
f
Tw
o
In
v
e
rter
F
e
d
S
ix
P
h
a
s
e
P
M
S
M
Driv
e
b
y
u
sin
g
P
I
a
n
d
F
u
z
z
y
L
o
g
ic
Co
n
tro
ll
e
r”
,
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
i
n
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
),
V
o
l
.
6
.
No
.
6
.
p
p
.
2
6
4
3
-
2
6
5
7
,
2
0
1
6
.
[3
]
L
.
S
.
M
o
u
li
n
,
e
t
a
l.
,
“
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
in
e
s
f
o
r
T
r
a
n
sie
n
t
S
tab
il
it
y
A
n
a
l
y
sis
o
f
L
a
r
g
e
-
s
c
a
le
P
o
w
e
r
S
y
ste
m
s,”
IEE
E
T
ra
n
s.
o
n
Po
we
r S
y
st.
,
v
o
l.
1
9
,
n
o
.
2
,
2
0
0
4
.
[4
]
A
.
B.
M
u
lj
o
n
o
,
e
t
a
l.
,
“
Dy
n
a
m
ic
S
ta
b
il
it
y
Im
p
ro
v
e
me
n
t
u
si
n
g
ANF
IS
-
b
a
se
d
Po
we
r
S
y
ste
m
S
ta
b
il
ize
r
in
a
M
u
lt
ima
c
h
i
n
e
Po
we
r
S
y
ste
m
(
in
Ba
h
a
sa
In
d
o
n
e
sia
),
”
P
ro
c
.
o
f
S
E
NT
I
A
,
P
o
li
n
e
m
a
M
a
lan
g
,
v
o
l.
7
,
p
p
.
B1
6
-
B2
1
,
2
0
1
5
.
[5
]
A
.
B.
M
u
lj
o
n
o
,
e
t
a
l.
,
“
Dy
n
a
m
ic
S
tab
il
it
y
I
m
p
ro
v
e
m
e
n
t
o
f
M
u
lt
ima
c
h
in
e
P
o
w
e
r
S
y
ste
m
u
sin
g
A
NF
IS
-
b
a
se
d
P
o
w
e
r
S
y
st
e
m
S
tab
il
ize
r,
”
T
EL
KOM
NI
KA
(
T
e
lec
o
mm
u
n
ica
ti
o
n
C
o
mp
u
t
in
g
El
e
c
tro
n
ics
a
n
d
C
o
n
tr
o
l
)
,
v
o
l.
7
,
p
p
.
1
1
7
0
-
1
1
7
8
,
2
0
1
5
.
[6
]
M
.
H.
Al
-
Qa
ta
m
in
,
“
A
n
Op
ti
m
a
l
S
tate
F
e
e
d
b
a
c
k
Co
n
tro
ll
e
r
Ba
se
d
Ne
u
ra
l
Ne
t
w
o
rk
s
f
o
r
S
y
n
c
h
ro
n
o
u
s
Ge
n
e
ra
to
rs,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
i
n
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
),
V
o
l.
3
N
o
.
4
,
p
p
.
5
6
1
-
5
6
7
,
2
0
1
3
.
[7
]
T
.
Hu
ss
e
in
a
n
d
A
.
S
h
a
m
e
k
h
,
“
Ad
a
p
ti
v
e
Ru
le
-
b
a
se
Fu
zz
y
Po
we
r
S
y
ste
m
S
ta
b
il
ize
r
fo
r
a
M
u
lt
i
-
ma
c
h
in
e
S
y
ste
m,”
P
r
o
c
.
o
f
th
e
M
ED
Co
n
f
.
,
p
p
.
1
4
1
5
-
1
4
1
9
,
2
0
1
3
.
[8
]
M
.
Ku
s
h
w
a
h
a
a
n
d
R.
Kh
a
re
,
“
Dy
n
a
mic
S
ta
b
il
it
y
En
h
a
n
c
e
me
n
t
o
f
Po
we
r
S
y
ste
m
u
sin
g
F
u
zz
y
L
o
g
i
c
Ba
se
d
Po
we
r
S
y
ste
m S
ta
b
il
ize
r,”
P
ro
c
.
o
f
In
t.
C
o
n
f
.
o
n
IC
P
EC,
p
p
.
2
1
3
-
2
1
9
,
2
0
1
3
.
[9
]
B.
S
h
a
h
,
“
C
o
mp
a
r
a
ti
v
e
S
t
u
d
y
o
f
Co
n
v
e
n
ti
o
n
a
l
a
n
d
Fu
zz
y
B
a
se
d
P
o
we
r
S
y
ste
m
S
ta
b
il
ize
r,”
P
ro
c
.
o
f
In
t.
Co
n
f
.
o
n
CS
NT
,
IEE
E,
p
p
.
5
4
7
-
5
5
1
,
2
0
1
3
.
[1
0
]
F
.
M
.
A
d
jero
u
d
,
e
t
a
l
.
,
“
A
Co
o
r
d
in
a
ted
G
e
n
e
ti
c
Ba
se
d
Ty
p
e
-
2
F
u
z
z
y
S
tab
il
ize
r
f
o
r
Co
n
v
e
n
ti
o
n
a
l
a
n
d
S
u
p
e
rc
o
n
d
u
c
ti
n
g
G
e
n
e
ra
to
rs,”
El
e
c
tric P
o
we
r S
y
ste
ms
Res
e
a
rc
h
,
v
o
l.
1
2
9
,
p
p
.
5
1
-
6
1
,
2
0
1
5
.
[1
1
]
Z.
S
u
n
,
e
t
a
l.
,
“
Op
ti
m
a
l
T
u
n
in
g
o
f
Ty
p
e
-
2
F
u
z
z
y
L
o
g
ic
P
o
w
e
r
S
y
ste
m
S
tab
il
ize
r
Ba
se
d
o
n
Dif
f
e
r
e
n
ti
a
l
Ev
o
lu
ti
o
n
A
l
g
o
rit
h
m
,
”
IJ
EP
ES
,
v
o
l.
6
2
,
p
p
.
1
9
-
2
8
,
2
0
1
4
.
[1
2
]
S
.
Ka
m
e
l,
e
t
a
l
.,
“
An
I
n
d
ire
c
t
Ad
a
p
ti
v
e
T
y
p
e
-
2
Fu
zz
y
S
li
d
i
n
g
M
o
d
e
P
S
S
De
si
g
n
t
o
Da
m
p
Po
we
r
S
y
ste
m
Os
c
il
la
ti
o
n
s,”
P
ro
c
.
o
f
In
t.
C
o
n
f
.
o
n
M
o
d
e
li
n
g
,
I
d
e
n
ti
f
ica
ti
o
n
a
n
d
C
o
n
tr
o
l,
T
u
n
isia,
2
0
1
5
.
[1
3
]
K.F
.
Zh
a
n
g
a
n
d
X.Z
.
Da
i,
“
S
t
ru
c
tu
ra
l
A
n
a
l
y
sis
o
f
L
a
r
g
e
-
s
c
a
l
e
P
o
w
e
r
S
y
ste
m
s”
,
M
a
th
e
ma
ti
c
a
Pro
b
lem
in
En
g
i
n
e
e
rin
g
,
Hi
n
d
a
wi
Pu
b
.
,
2
0
1
2
.
[1
4
]
Y.Y.
Hs
u
a
n
d
C.
C.
S
u
,
“
A
p
p
li
c
a
ti
o
n
o
f
P
o
w
e
r
S
y
ste
m
S
tab
il
ize
r
o
n
a
S
y
ste
m
w
it
h
P
u
m
p
e
d
S
to
ra
g
e
p
lan
t,
”
IEE
E
T
ra
n
s.
o
n
Po
we
r
S
y
st.,
v
o
l.
3
,
n
o
.
1
,
1
9
8
8
.
[1
5
]
M
.
S
a
id
y
a
n
d
F
.
M
.
Hu
g
h
e
s,
“
P
e
rf
o
rm
a
n
c
e
I
m
p
ro
v
e
m
e
n
t
o
f
a
Co
n
v
e
n
ti
o
n
a
l
P
o
w
e
r
S
y
st
e
m
S
tab
il
ize
r,
”
El
e
c
t.
Po
we
r
a
n
d
En
e
rg
y
S
y
st.,
v
o
l
.
1
7
n
o
.
5
,
1
9
9
5
.
[1
6
]
J.
T
a
laq
,
“
Op
ti
m
a
l
P
o
w
e
r
S
y
ste
m
S
tab
il
ize
rs f
o
r
M
u
l
ti
M
a
c
h
i
n
e
S
y
s
tem
s,”
IJ
EP
ES
,
v
o
l.
4
3
,
p
p
.
7
9
3
-
8
0
3
,
2
0
1
2
.
[1
7
]
I.
M
.
G
in
a
rsa
,
e
t
a
l.
,
“
Co
n
tro
ll
in
g
Ch
a
o
s an
d
V
o
lt
a
g
e
Co
ll
a
p
se
u
sin
g
La
y
e
r
e
d
Re
c
u
rre
n
t
Ne
tw
o
rk
-
b
a
s
e
d
P
ID
-
S
V
C
in
P
o
w
e
r
S
y
ste
m
s,”
T
EL
KOM
NIKA
(
T
e
lec
o
mm
u
n
ica
ti
o
n
Co
m
p
u
t
in
g
El
e
c
tro
n
ics
a
n
d
Co
n
tro
l
),
v
o
l.
1
1
,
p
p
.
4
5
1
-
4
6
2
,
n
o
.
3
,
2
0
1
3
.
[1
8
]
S
.
M
o
h
a
g
h
e
g
h
i,
e
t
a
l
.
,
“
A
d
a
p
ti
v
e
Crit
ic
D
e
sig
n
Ba
se
d
o
n
Ne
u
ro
-
f
u
z
z
y
Co
n
tro
ll
e
r
f
o
r
a
S
tatic
Co
m
p
e
n
sa
to
r
in
a
M
u
lt
im
a
c
h
in
e
P
o
w
e
r
S
y
ste
m
”
,
IEE
E
T
ra
n
s.
o
n
P
o
we
r S
y
st.,
v
o
l.
2
1
,
n
o
.
4
,
2
0
0
6
.
[1
9
]
S
.
M
o
h
a
g
h
e
g
h
i,
e
t
a
l.
,
“
Ha
rd
w
a
re
I
m
p
le
m
e
n
tatio
n
o
f
a
M
a
m
d
a
n
i
F
u
z
z
y
L
o
g
ic
Co
n
tro
ll
e
r
f
o
r
a
S
tatic
Co
m
p
e
n
sa
to
r
in
a
M
u
lt
im
a
c
h
in
e
P
o
w
e
r
S
y
st
e
m
,
”
IEE
E
T
ra
n
s.
o
n
I
n
d
u
stry
Ap
p
.
,
v
o
l.
4
5
,
n
o
.
4
,
2
0
0
9
.
[2
0
]
I.
M
.
G
in
a
rsa
,
e
t
a
l.
,
“
Co
n
tro
ll
i
n
g
Ch
a
o
s
a
n
d
Vo
lt
a
g
e
Co
ll
a
p
se
u
sin
g
a
n
A
NFIS
-
b
a
se
d
Co
m
p
o
site
Co
n
tr
o
ll
e
r
-
sta
ti
c
V
a
r
Co
m
p
e
n
sa
to
r
i
n
P
o
w
e
r
S
y
ste
m
s,”
IJ
EP
ES
,
v
o
l.
4
6
,
p
p
.
7
9
-
8
8
,
2
0
1
3
.
[2
1
]
I.
M
.
G
in
a
rsa
,
e
t
a
l.
,
“
I
m
p
ro
v
e
m
e
n
t
o
f
T
r
a
n
sie
n
t
V
o
lt
a
g
e
Re
sp
o
n
se
s
u
sin
g
a
n
A
d
d
it
io
n
a
l
P
ID
-
lo
o
p
o
n
a
n
A
NFIS
b
a
se
d
Co
m
p
o
site
Co
n
tro
l
l
e
r
-
S
V
C
(CC
-
S
V
C)
to
Co
n
tr
o
l
C
h
a
o
s
a
n
d
v
o
lt
a
g
e
Co
ll
a
p
se
in
P
o
w
e
r
S
y
ste
m
s,”
IEE
J
T
ra
n
s.
o
n
P
o
we
r a
n
d
E
n
e
rg
y
(
S
e
c
ti
o
n
B),
v
o
l
.
1
3
1
,
n
o
.
1
0
,
p
p
.
8
3
6
-
8
4
8
,
2
0
1
1
.
[2
2
]
I.
M
.
G
in
a
rsa
,
e
t
a
l.
,
“
Reg
u
la
ti
o
n
o
f
1
2
-
p
u
lse
Rec
ti
fi
e
r
Co
n
v
e
rte
r
u
sin
g
ANF
IS
-
b
a
se
d
Co
n
tro
ll
e
r
in
a
HVD
C
T
ra
n
sm
issio
n
S
y
ste
m”,
in
In
teg
r
a
ted
S
c
i
-
T
e
c
h
:
T
h
e
In
ter
d
isc
ip
l
i
n
a
ry
Res
e
a
rc
h
Ap
p
ro
a
c
h
,
v
o
l
.
1
,
c
h
a
p
t.
6
,
U
P
T
P
e
r
p
u
sta
k
a
a
n
UN
ILA
L
a
m
p
u
n
g
,
p
p
.
4
4
-
5
3
,
2
0
1
5
.
[2
3
]
N.
Ba
wa
n
e
,
e
t
a
l.
,
“
A
NFIS
Ba
se
d
Co
n
tr
o
l
a
n
d
F
a
u
lt
De
tec
ti
o
n
o
f
HV
DC
Co
n
v
e
rter,”
HAIT
J
o
u
r
n
a
l
o
f
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
B,
v
o
l
.
2
,
n
o
.
5
-
6
,
p
p
.
6
7
3
-
6
8
9
,
2
0
1
1
.
[2
4
]
I.
M
.
G
in
a
rsa
a
n
d
O.
Zeb
u
a
,
“
S
tab
il
it
y
Im
p
ro
v
e
m
e
n
t
o
f
S
in
g
le
M
a
c
h
in
e
u
si
n
g
A
NFIS
-
P
S
S
Ba
se
d
o
n
F
e
e
d
b
a
c
k
li
n
e
a
riza
ti
o
n
,
”
T
EL
KO
M
NIKA
(
T
e
lec
o
mm
u
n
ica
ti
o
n
Co
mp
u
ti
n
g
El
e
c
tro
n
ics
a
n
d
Co
n
tro
l
)
,
v
o
l.
1
2
,
n
o
.
2
,
2
0
1
4
.
[2
5
]
P
.
Ku
n
d
u
r,
P
o
we
r S
y
ste
m S
t
a
b
il
it
y
a
n
d
Co
n
tro
l
,
E
P
RI
M
c
G
ra
w
-
Hil
l.
Ne
w
Yo
rk
,
1
9
9
4
.
[2
6
]
K.R.
P
a
d
iy
a
r,
Po
we
r S
y
ste
m Dy
n
a
mic
S
t
a
b
il
it
y
a
n
d
C
o
n
tro
l,
Jo
h
n
W
il
e
y
a
n
d
S
o
n
s (A
sia
)
P
te L
td
,
S
i
n
g
a
p
o
re
,
1
9
9
4
.
[2
7
]
J.
-
S
.
R.
Ja
n
g
,
e
t
a
l
.,
Ne
u
ro
-
f
u
zz
y
a
n
d
S
o
ft
C
o
mp
u
ti
n
g
:
A
Co
m
p
u
ta
ti
o
n
a
l
Ap
p
ro
a
c
h
t
o
L
e
a
rn
i
n
g
a
n
d
M
a
c
h
in
e
In
telli
g
e
n
c
e
,
P
re
n
ti
c
e
-
Ha
ll
In
tern
a
ti
o
n
a
l,
In
c
.
,
USA
,
1
9
9
7
.
[2
8
]
N.N.
Ka
rn
ik
,
e
t
a
l
., “
T
y
p
e
-
2
F
u
z
z
y
L
o
g
ic S
y
ste
m
s,”
IEE
E
T
ra
n
s.
o
n
Fu
zz
y
S
y
st.,
v
o
l.
7
,
n
o
.
6
,
1
9
9
9
.
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