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nte
rna
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l J
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l o
f
Appl
ied P
o
w
er
E
ng
ineering
(
I
J
AP
E
)
Vo
l.
8
,
No
.
3
,
Dec
em
b
er
201
9
,
p
p
.
2
2
1
~2
3
3
I
SS
N:
2252
-
879
2
DOI
:
1
0
.
1
1
5
9
1
/i
j
ap
e.
v
8
.
i3
.
p
p
2
2
1
-
233
221
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//
ia
e
s
co
r
e.
co
m/jo
u
r
n
a
ls
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d
ex
.
p
h
p
/
I
JA
P
E
A nov
el
m
a
tlab/s
i
m
ulin
k
m
o
del o
f
DFIG
drive usi
ng
NSMC
m
e
thod w
ith
NSV
M
strategy
H
a
bib
B
enbo
uh
en
ni
1
,
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i
nela
a
bid
ine B
o
ud
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e
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a
2
,
Abdel
k
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der
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ela
idi
3
1,
3
Na
ti
o
n
a
l
P
o
ly
tec
h
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c
h
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ria
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a
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ie E
lec
tri
q
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t
En
e
rg
ies
Re
n
o
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v
e
lab
les
(
L
G
EE
R
)
,
El
e
c
tri
c
a
l
En
g
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e
e
rin
g
De
p
a
rtm
e
n
t,
Ha
ss
ib
a
Be
n
b
o
u
a
li
Un
iv
e
rsity
,
A
lg
e
ria
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Dec
3
0
,
2
0
1
8
R
ev
i
s
ed
J
an
2
2
,
2
0
1
9
A
cc
ep
ted
Ma
r
6
,
2
0
1
9
In
t
h
is
a
rti
c
le,
w
e
p
re
se
n
t
a
c
o
m
p
a
ra
ti
v
e
stu
d
y
b
e
tw
e
e
n
p
u
lse
w
id
th
m
o
d
u
latio
n
(
P
W
M
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a
n
d
n
e
u
ra
l
sp
a
c
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e
c
to
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m
o
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latio
n
(NS
VM)
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teg
y
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ss
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iate
d
w
it
h
a
n
e
u
ro
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slid
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g
m
o
d
e
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o
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tro
l
(NSM
C)
o
f
sta
to
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re
a
c
ti
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e
a
n
d
sta
to
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a
c
ti
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p
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r
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o
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m
a
n
d
o
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a
d
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u
b
ly
fe
d
in
d
u
c
ti
o
n
g
e
n
e
ra
to
r
(DFIG
).
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h
e
o
b
tain
e
d
re
su
lt
s
sh
o
w
e
d
th
a
t,
th
e
p
ro
p
o
se
d
NSM
C
w
it
h
NSVM
stra
teg
y
h
a
v
e
ro
to
r
c
u
rre
n
t
w
it
h
lo
w
h
a
r
m
o
n
ic
d
ist
o
rti
o
n
a
n
d
lo
w
p
o
w
e
rs
ri
p
p
les
th
a
n
P
W
M
stra
teg
y
.
K
ey
w
o
r
d
s
:
DFI
G
NSM
C
NSVM
P
W
M
Co
p
y
rig
h
t
©
201
9
In
s
t
it
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Hab
ib
B
en
b
o
u
h
en
n
i,
Dep
ar
t
m
en
t o
f
E
lectr
ical
E
n
g
i
n
ee
r
in
g
,
Natio
n
al
P
o
l
y
tech
n
iq
u
e
Sch
o
o
l o
f
Or
an
Ma
u
r
ice
Au
d
i
n
,
Or
an
,
A
lg
er
ia.
E
m
ail:
h
ab
ib
0
2
6
4
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
I
n
r
ec
en
t
y
ea
r
s
,
s
lid
i
n
g
m
o
d
e
co
n
tr
o
l
(
SMC
)
h
as
d
r
a
w
n
m
u
ch
atte
n
tio
n
f
r
o
m
r
esear
c
h
g
r
o
u
p
s
an
d
in
d
u
s
tr
y
.
T
h
e
SM
C
th
eo
r
y
w
a
s
p
r
o
p
o
s
ed
b
y
Utk
i
n
i
n
1
9
7
7
[
1
]
.
T
h
e
p
r
in
cip
le
ad
v
an
ta
g
e
o
f
th
e
SM
C
is
t
h
at
t
h
e
r
o
b
u
s
tn
es
s
a
n
d
s
i
m
p
le
co
n
tr
o
l
.
On
t
h
e
o
th
er
h
an
d
,
t
h
e
S
M
C
h
as
a
m
aj
o
r
in
co
n
v
en
ie
n
ce
c
alled
th
e
c
h
atter
in
g
p
h
en
o
m
e
n
o
n
cr
ea
ted
b
y
th
e
d
is
co
n
ti
n
u
o
u
s
p
ar
t
o
f
co
m
m
a
n
d
[
2
]
.
I
n
o
r
d
er
to
m
in
i
m
ize
th
is
e
f
f
ec
t,
ar
ti
f
icia
l
in
telli
g
e
n
ce
s
tr
ate
g
ies
li
k
e
f
u
zz
y
lo
g
ic
(
F
L
)
an
d
ar
ti
f
icial
n
eu
r
al
n
et
w
o
r
k
s
(
ANN)
ar
e
u
s
ed
to
i
m
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
ce
o
f
SM
C
tec
h
n
iq
u
e.
I
n
[
3
]
,
f
u
zz
y
s
l
id
in
g
m
o
d
e
co
n
tr
o
ller
(
FS
MC)
w
as
d
esi
g
n
ed
f
o
r
t
h
e
d
o
u
b
l
y
f
ed
i
n
d
u
ctio
n
m
ac
h
in
e
s
co
n
tr
o
l.
Seco
n
d
o
r
d
er
s
lid
in
g
m
o
d
e
co
n
tr
o
ller
an
d
n
e
u
r
al
n
et
w
o
r
k
s
ar
e
co
m
b
in
ed
to
co
m
m
a
n
d
ac
tiv
e
a
n
d
r
ea
ctiv
e
p
o
w
er
o
f
DFI
G
b
ased
w
in
d
t
u
r
b
in
e
s
y
s
te
m
s
[
4
]
.
T
r
a
d
itio
n
all
y
,
t
h
e
s
p
ac
e
v
ec
t
o
r
m
o
d
u
latio
n
(
SVM)
tec
h
n
iq
u
e
is
w
id
el
y
u
s
ed
in
A
C
in
v
er
ter
s
.
Ho
w
e
v
er
,
t
h
is
s
tr
ate
g
y
r
ed
u
c
es
t
h
e
to
tal
h
ar
m
o
n
ic
d
is
to
r
ti
o
n
(
T
HD)
co
m
p
ar
ed
to
p
u
ls
e
w
id
th
m
o
d
u
latio
n
(
P
W
M)
tech
n
iq
u
e.
I
n
ad
d
itio
n
,
th
is
s
tr
ate
g
y
is
d
i
f
f
icu
lt
to
i
m
p
le
m
e
n
t.
Ho
w
e
v
er
,
t
h
is
tec
h
n
iq
u
e
h
as
ess
e
n
tia
l
d
r
a
w
b
ac
k
s
s
u
c
h
a
s
th
e
n
ee
d
o
f
s
ec
to
r
an
d
an
g
le
ca
lc
u
lata
t
io
n
[
5
]
an
d
i
m
p
o
r
tan
t
p
o
w
er
s
r
ip
p
les.
I
n
[
6
]
,
th
e
au
th
o
r
s
p
r
o
p
o
s
e
a
n
o
v
el
SV
M
tech
n
iq
u
e
b
ased
o
n
FL
c
o
n
tr
o
ller
(
FS
VM
)
to
co
n
tr
o
l
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
w
er
s
o
f
a
DFI
G.
I
n
t
h
is
p
ap
er
,
w
e
u
s
e
th
e
n
e
u
r
al
s
p
ac
e
v
e
cto
r
m
o
d
u
latio
n
(
NSVM)
to
c
o
n
tr
o
l th
e
p
o
w
er
s
o
f
a
DFI
G.
T
h
ese
p
r
o
p
o
s
ed
s
tr
ateg
ies r
ed
u
ce
t
h
e
T
HD
v
alu
e
o
f
r
o
to
r
cu
r
r
en
t a
n
d
p
o
w
er
s
r
ip
p
les.
2.
M
O
DE
L
O
F
T
H
E
W
I
ND
T
URB
I
N
E
I
n
T
h
e
w
i
n
d
tu
r
b
in
e
i
n
p
u
t p
o
wer
is
g
iv
e
n
b
y
[
7
,
8
]
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8792
I
n
t J
A
p
p
l P
o
w
er
E
n
g
,
Vo
l.
8
,
No
.
3
,
Dec
em
b
er
2019
:
2
2
1
–
233
222
V
R
P
ve
n
t
3
2
m
ax
5
.
0
(
1
)
T
h
e
m
ec
h
a
n
ical
p
o
w
er
ca
n
b
e
w
r
itte
n
as [
9
]
:
V
R
C
P
ve
n
t
p
m
3
2
).
(
.
5
.
0
(
2
)
V
R
1
1
.
(
3
)
.
)
e
x
p
(
).
.
.(
)
,
(
6
5
4
3
2
1
C
C
C
C
C
C
C
i
i
p
(
4
)
1
035
.
0
.
08
.
0
1
1
3
i
(
5
)
W
h
er
e,
C
1
=0
.
5
1
7
6
,
C
2
=1
1
6
,
C
3
=0
.
4
,
C
4
=5
,
C
5
=2
1
,
C
6
=0
.
0
0
6
8
.
ρ
: is air
d
en
s
it
y
.
V
v
ent
: W
in
d
s
p
ee
d
(
m
/s
)
.
P
m
ax
: M
ax
i
m
u
m
p
o
w
er
i
n
(
w
a
t
ts
)
.
R
: Rad
i
u
s
o
f
t
h
e
t
u
r
b
in
e
i
n
(
m
)
.
C
p
: T
h
e
ae
r
o
d
y
n
a
m
ic
co
e
f
f
icie
n
t o
f
p
o
w
er
.
λ
: T
h
e
tip
s
p
ee
d
r
atio
.
β:
T
h
e
b
la
d
e
p
itch
an
g
le
i
n
a
p
itch
-
co
n
tr
o
lled
w
in
d
t
u
r
b
in
e.
3.
M
O
DE
L
I
N
G
O
F
T
H
E
DF
I
G
T
h
e
m
at
h
e
m
atica
l
m
o
d
els o
f
t
h
r
ee
p
h
ases
D
FIG
in
t
h
e
P
ar
k
f
r
a
m
e
ar
e
w
r
itte
n
as [
1
0
-
12]
:
ψ
ω
ψ
dt
d
I
R
V
ψ
ω
ψ
dt
d
I
R
V
ψ
ω
ψ
dt
d
I
R
V
ψ
ω
ψ
dt
d
I
R
V
dr
r
qr
qr
r
qr
qr
r
dr
dr
r
dr
ds
s
qs
qs
s
qs
qs
s
ds
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s
ds
(
6
)
T
h
e
dq
s
y
n
c
h
r
o
n
o
u
s
r
ef
er
e
n
ce
f
r
a
m
e
eq
u
at
io
n
s
o
f
th
e
r
o
to
r
f
l
u
x
a
n
d
s
tato
r
m
a
y
b
e
w
r
it
ten
a
ls
o
as:
qs
qr
r
qr
ds
dr
r
dr
qr
qs
s
qs
dr
ds
s
ds
MI
I
L
MI
I
L
MI
I
L
MI
I
L
(
7
)
T
h
e
to
r
q
u
e
is
ex
p
r
ess
ed
as [
1
3
]
:
)
.
.
(
ds
qr
qs
dr
e
I
I
I
I
pM
T
(
8
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
A
P
E
I
SS
N:
2252
-
8792
A
n
o
ve
l m
a
tla
b
/s
imu
lin
k
mo
d
e
l o
f D
F
I
G
d
r
ive
u
s
in
g
N
S
MC
meth
o
d
w
ith
N
S
V
M
… (
Ha
b
ib
B
en
b
o
u
h
en
n
i
)
223
f
dt
d
J
T
T
r
e
(
9
)
T
h
e
s
tato
r
ac
tiv
e
an
d
s
tato
r
r
ea
ctiv
e
p
o
w
er
s
ca
n
b
e
ex
p
r
ess
e
d
as:
)
(
2
3
)
(
2
3
qs
ds
ds
qs
s
qs
qs
ds
ds
s
I
V
I
V
Q
I
V
I
V
P
(
1
0
)
4.
NE
URA
L
SPAC
E
V
E
CT
O
R
M
O
DULAT
I
O
N
T
h
e
d
is
ad
v
an
ta
g
e
o
f
t
h
e
co
n
v
en
t
io
n
al
S
VM
s
tr
ate
g
y
ar
e
ex
p
lain
ed
i
n
[
1
4
]
.
T
h
e
d
etails
ab
o
u
t
th
i
s
s
tr
ateg
y
ca
n
b
e
es
tab
lis
h
ed
in
[
1
5
-
1
7
]
.
T
h
e
p
r
o
p
o
s
ed
SVM
in
v
er
ter
as
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
T
h
is
m
o
d
u
latio
n
s
tr
ateg
y
is
s
i
m
p
le
s
ch
e
m
e
a
n
d
ea
s
y
i
m
p
le
m
en
tat
io
n
,
b
ased
o
n
f
o
llo
w
i
n
g
s
tep
s
[
1
8
]
:
a.
C
alcu
lates t
h
e
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ased
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ased
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ased
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2252
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8792
I
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t J
A
p
p
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Dec
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2019
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Fig
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ased
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ased
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g
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ased
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ased
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8792
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g
,
Vo
l.
8
,
No
.
3
,
Dec
em
b
er
2019
:
2
2
1
–
233
226
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I
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2019
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1
–
233
228
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