Internati
o
nal
Journal of P
o
wer Elect
roni
cs an
d
Drive
S
y
ste
m
(I
JPE
D
S)
Vol
.
4
,
No
. 2,
J
une
2
0
1
4
,
pp
. 23
3~
24
0
I
S
SN
: 208
8-8
6
9
4
2
33
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJPEDS
Adaptive Fuzzy Logi
c Contr
o
l of
W
i
nd
T
u
rbine E
m
ulator
Bouz
id Moh
a
med Amine*,
Z
i
ne Souhil
a**
, A
llao
u
i
T
a
yeb**
*
, M
a
ssoum
Ahmed*
* Department of
Electrical Eng
i
n
eering
,
Dj
ill
ali
L
I
ABES Univer
sity
,
Si
di
Bel
Abbes-ALGERIA
** Departmen
t
o
f
Electr
i
cal
Engineering
,
Univ
ersi
ty
of
Scien
ces
and Technolog
y
,
Oran-ALGERIA
*** Dep
a
rtment of Electrical En
gineer
i
ng, IBN
Khaldoun University
,
Tiaret-ALGERIA
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Dec 19, 2013
Rev
i
sed
Feb
17
, 20
14
Accepted
Mar 10, 2014
In this paper,
a Wind Turbine Emulator
(WTE) based on a separ
a
tely
excited
direc
t
curr
ent (
D
C) m
o
tor is
studied
. Th
e win
d
turbine w
a
s
e
m
ulated
b
y
controlling th
e torque of th
e DC
moto
r. Th
e W
T
E is
us
ed
as
a pr
im
e m
over
for Permanent Magnet S
y
nchro
nous Mach
ine (PMSM). In ord
e
r to extract
m
a
xim
u
m
power from
the wind, P
I
and F
u
zz
y con
t
roll
ers
were t
e
s
t
ed.
Simulation results are given to show
performance of proposed fuzzy
contro
l
s
y
stem in maximum po
wer points tr
ack
ing in
a wind energ
y
conversion
s
y
stem under various wind conditions. Th
e strateg
y
con
t
rol was implemented
in simulation using MATLAB/Simulink.
Keyword:
Wi
n
d
T
u
r
b
i
n
e
W
i
nd T
u
rbine
Em
ulator
DC m
o
tor
FLC
Copyright ©
201
4 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
BOUZ
ID
M
o
h
a
m
e
d Am
ine,
Depa
rtem
ent of Elect
ri
cal
E
n
gi
nee
r
i
n
g,
Djilla
li
LIABES
Un
iv
ersity,
Facul
t
é
de
Tec
h
n
o
l
o
gi
e, B
P
8
9
,
ci
t
é
B
e
n M
’
Hi
di
,
2
2
0
0
0
Si
di
B
e
l
A
b
bes,
ALG
E
R
I
E
.
Em
ail: bouzid_m
oham
e
dam
i
ne@yahoo.com
1.
INTRODUCTION
W
i
n
d
ene
r
gy
i
s
one
of t
h
e fa
st
est
gro
w
i
n
g
m
a
jor s
o
urces
of
new el
ect
ri
c
i
t
y
arou
nd t
h
e
wo
rl
d
.
W
i
n
d
turbine de
velopm
ent is currently a very dynam
i
c i
ndust
r
y [1]. Howe
ver, access, tes
ting and m
onitoring
in
stalled
tu
rb
in
es is d
i
fficu
lt. Si
m
u
latio
n
is an
ap
pro
p
riate to
o
l
to
ev
aluate th
e effect o
f
m
o
d
i
fication
s
and
o
f
fers a so
lu
tion
to th
is
p
r
ob
lem
.
In
resea
r
ch a
p
pl
i
cat
i
ons, t
h
e
W
i
n
d
T
u
r
b
i
n
e
Em
ul
at
or i
s
an
im
port
a
nt
de
v
i
ce fo
r de
vel
o
pi
n
g
W
i
n
d
Energy C
o
nversion System
s. The
WTE
can
be
use
d
to drive an electrical
gene
rato
r i
n
si
m
ilar way
as a
W
i
n
d
Turbine
.
T
h
e
m
o
tivation for
this study is to create an em
ulation system
that as clos
ely
as possible re
plicates
t
h
e be
ha
vi
o
r
of
a wi
nd
t
u
rbi
n
e
.
In th
e
wind
t
u
rb
in
e em
u
l
ato
r
,
th
e wi
n
d
turb
i
n
e
was sub
s
titu
ted
b
y
t
h
e
o
u
t
p
u
t
t
o
rqu
e
calcu
lated
fro
m
th
e wi
n
d
turb
i
n
e to
rqu
e
m
o
d
e
l.
The m
a
i
n
o
b
je
ct
i
v
e o
f
t
h
e
WTE i
s
re
pr
o
d
u
c
i
ng t
h
e
wi
n
d
t
u
rbi
n
e
out
put
t
o
rq
ue c
o
r
r
es
po
n
d
i
n
g t
o
any
wind s
p
ee
d input. T
h
e
refere
nce curre
nt is ca
lculated as
f
u
n
c
t
i
on
of
t
h
e
wi
nd
t
u
rbi
n
e s
p
ee
d a
n
d
wi
n
d
s
p
e
e
d t
o
pr
o
duce
t
h
e ae
ro
dy
nam
i
c t
o
rq
ue
of
t
h
e
wi
n
d
t
u
r
b
i
n
e
[2]
.
The wi
nd t
u
r
b
i
n
e em
ul
at
or gi
ves t
h
e
op
p
o
rt
uni
t
y
t
h
at
any
desi
re
d wi
nd s
p
eed
pr
o
f
i
l
e
can be t
e
st
e
d
an
d used to
st
ud
y th
e
b
e
h
a
v
i
or
o
f
t
h
e system.
The Pa
per i
s
o
r
ga
ni
zed as f
o
l
l
ows:
Sect
i
o
n 2 di
sc
usses o
n
t
h
e sy
st
em
t
o
p
o
l
o
gy
and m
o
d
e
l
i
ng o
f
t
h
e
Perm
anent
M
a
gnet
Sy
nc
h
r
o
n
ous Gen
e
rat
o
r and wi
n
d
t
u
r
b
i
n
e. Sect
i
o
n 3 d
e
scri
bes t
h
e C
ont
rol
st
rat
e
gy
of t
h
e
Em
ul
at
or an
d
t
h
e F
u
zzy
Lo
gi
c C
o
nt
r
o
l
l
e
r. Si
m
u
l
a
t
i
ons
ru
n
wi
t
h
M
A
TLAB
/
S
i
m
uli
nk s
h
owi
n
g
t
h
e
per
f
o
r
m
a
nce o
f
p
r
o
p
o
se
d em
ul
at
or a
r
e
pres
ent
e
d i
n
Sect
i
o
n 4
.
Sect
i
o
n
5
concl
ude
s t
h
e
pape
r
wi
t
h
ana
l
y
s
i
s
of
th
e resu
lts an
d
d
i
scu
s
ses th
e valid
ity o
f
th
e propo
sed m
o
d
e
l.
Evaluation Warning : The document was created with Spire.PDF for Python.
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4
2.
SYSTE
M
CO
NFIG
U
RATI
O
N A
N
D
M
O
DELING
The p
o
w
er c
o
nve
rsi
o
n sy
st
e
m
consi
s
t
s
of
a Perm
anent
M
a
gnet
Sy
nc
h
r
o
n
ous
Ge
nera
t
o
r (
P
M
S
G
)
, a
rectifier an
d an in
v
e
rter con
n
ected
to
t
h
e l
o
ad or to th
e
g
r
i
d
.
Th
e system
to
po
log
y
used in
t
h
is work is sh
own in
Figu
re1
.
Fi
gu
re
1.
C
o
nt
r
o
l
sy
st
em
2.
1.
Perma
n
en
t
Ma
gne
t
S
y
n
c
hron
ous
Ge
n
erat
o
r m
o
del
Th
e ro
tor ex
citatio
n
of th
e Perm
an
en
t Mag
n
e
t Syn
c
h
r
on
ou
s Gen
e
rator (PMSG) is assu
m
e
d
to
be
constant, s
o
its
electrical
m
o
del in the
sync
hro
n
o
u
s
refe
renc
e fram
e i
s
gi
ve
n
by
[
3
]
,
[4]
:
d
sd
d
q
q
q
sq
q
d
d
f
di
Ri
V
i
L
dt
di
Ri
V
i
L
dt
(
1
)
Wh
ere su
b
s
cri
p
ts d
and
q
refer to
th
e ph
ysi
cal q
u
a
n
tities t
h
at h
a
v
e
b
e
en
tran
sform
e
d
in
to
th
e (d
, q)
sy
nch
r
o
n
o
u
s
r
o
tating re
fere
nc
e fra
m
e
, the el
ectrical rotating s
p
ee
d
ω
e
i
s
g
i
ven
by
:
ep
T
n
(
2
)
The p
o
we
r
e
q
u
a
t
i
ons
a
r
e gi
ve
n by
:
3
(.
.
)
2
3
(.
.
)
2
dd
q
q
qd
d
q
Pv
i
v
i
Qv
i
v
i
(
3
)
The el
ect
r
o
m
a
gnet
i
c
t
o
r
que
T
e
can
be
de
ri
ve
d
fr
om
:
sq
f
p
e
i
n
T
2
3
(
4
)
2.
2.
Wind Tu
r
b
ine Modelin
g
Th
e m
a
th
e
m
at
i
cal relatio
n
fo
r th
e
m
echanic
al power e
x
tra
c
tion from
the wind can
be expresse
d as
f
o
llow
s
[5
]:
2
.
.
).
,
(
).
,
(
3
w
p
w
P
m
V
A
C
P
C
P
(
5
)
Th
e tip sp
eed ratio
,
λ
,
i
s
gi
ve
n
by
[6]
,
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6
9
4
Ad
apt
i
ve F
u
zzy
Lo
gi
c C
ont
r
o
l
of
Wi
n
d
Tur
b
i
n
e Em
ul
at
or (
B
OU
ZI
D M
o
ha
med
A
m
i
n
e)
23
5
m
w
R
V
(
6
)
The
powe
r c
o
e
fficient C
p
ca
n
b
e
exp
r
essed as [7
],
[8
],
12
3
4
5
6
11
(
,
)
(
)
e
xp(
)
P
ii
CC
C
C
C
C
C
(7)
Whe
r
e
)
1
03
5
.
0
08
.
0
1
1
3
i
C
1
=0
.517
6, C
2
=116, C
3
=0
.4
, C
4
=5, C
5
=21 a
n
d C
6
=0.
0
06
8.
The t
o
r
que
o
f
t
h
e
wi
n
d
t
u
r
b
i
n
e w
oul
d
be e
x
p
r
esse
d as:
.
2
.
.
).
,
(
3
w
p
V
A
C
T
(
8
)
3.
CO
NTR
O
L S
T
RATEG
Y
O
F
THE WI
N
D
TURBI
N
E E
M
UL
ATO
R
In
t
h
i
s
sect
i
o
n,
t
h
e f
u
zzy
c
ont
r
o
l
m
e
t
hod a
p
pl
i
e
d t
o
t
h
e
wi
n
d
t
u
r
b
i
n
e
em
ul
ator
i
s
p
r
ese
n
t
e
d.
3.
1.
T
h
e
E
m
u
l
ati
o
n of Wi
n
d
T
u
rbi
n
e
A
ccord
ing
to [9
]-
[1
2
]
, th
e ch
ar
acteristics of
Wind
t
u
rb
in
e h
a
v
e
a great sim
i
l
a
rity to
the
ch
aracteristics o
f
DC m
o
to
r, so
it can b
e
simu
lated
b
y
a
DC
m
o
to
r.
Fi
gu
re
2
pre
s
e
n
t
t
h
e c
o
nt
r
o
l
b
l
ock
di
ag
ram
of t
h
e
wi
nd
t
u
r
b
i
n
e Em
ul
at
or s
y
st
em
.
Fi
gu
re
2.
C
o
nt
r
o
l
bl
ock
di
a
g
ra
m
of t
h
e
wi
n
d
t
u
r
b
i
n
e
Em
ul
ator
sy
st
em
In this
diagra
m
,
the wind
rotor s
p
eed is express
e
d as t
h
e
m
easured
DC
m
o
t
o
r speed
di
vi
de
d by
t
h
e
ratio
of th
e
g
e
arbox
. Th
e referen
ce torqu
e
o
f
th
e DC
m
o
to
r
wh
ich
is th
e
Win
d
Tu
rb
in
e
Aerod
y
n
a
m
i
c to
rqu
e
is
calculated
by the
W
i
nd T
u
rbi
n
e m
odel acc
ordi
ng to
t
h
e
dy
nam
i
c wi
nd s
p
eed a
n
d
t
h
e
bl
ade
pi
t
c
h a
n
gl
e an
d
the wind
rot
o
r spee
d. T
h
e re
fere
nce
c
u
r
r
ent
of t
h
e DC
m
o
t
o
r i
s
obt
ai
ne
d
by the re
fe
rence torque
of the DC
mo
t
o
r
.
In th
is
work
,
PI an
d FLC con
t
ro
llers are
u
s
ed in
sp
eed
regu
latio
n
.
3.
2.
F
u
z
z
y
Logi
c
C
o
n
t
rol
Fu
zzy log
i
c is ab
le to
u
s
e
human
reason
s
no
t in
term
s o
f
d
i
screte sym
b
o
l
s an
d
n
u
m
b
e
rs, bu
t in
term
s
o
f
fu
zzy sets.
Th
ese term
s are q
u
ite flex
ib
le with
resp
ect
t
o
t
h
e defi
ni
t
i
on
an
d val
u
es
. The bi
g
a
dva
nt
ages of
fuzzy logic control when a
pplied to
a wi
nd
turbine are that
th
e tu
rb
in
e
syste
m
n
e
ith
er
needs to
be acc
urately
descri
bed
n
o
r
doe
s i
t
nee
d
t
o
be l
i
n
ea
r [
1
3]
.
R
u
l
e
based f
u
z
z
y
l
ogi
c cont
r
o
l
l
e
rs are usef
ul
whe
n
t
h
e sy
st
em
dy
nam
i
cs are not
wel
l
k
n
o
w
n
or w
h
e
n
th
ey con
t
ain
si
g
n
i
fican
t
n
o
n
lin
earities, su
ch
as th
e
un
-station
a
ry
wind
con
t
ain
s
larg
e turbu
l
en
ce.
I
n
Figur
e 3
,
str
u
ctur
e of
f
u
zzy co
n
t
ro
l is s
h
own
.
A
fu
zzy co
n
t
ro
ller
usu
a
lly co
n
t
ains f
o
u
r
m
a
in
com
pone
nt
s:
F
u
zzi
fi
er,
f
u
zzy
rul
e
base, i
n
f
e
rence en
gi
ne and
Def
u
zzi
fi
e
r
. The F
u
zzi
fi
er chan
ges t
h
e
i
nput
(crisp
sig
n
a
ls) in
to
fu
zzy v
a
lues. Th
e fu
zzy ru
le b
a
se con
s
ists o
f
b
a
sic d
a
ta an
d
lin
gu
istic ru
les. Th
e eng
i
n
e
is
Evaluation Warning : The document was created with Spire.PDF for Python.
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6
th
e br
ain of
a
f
u
zzy con
t
r
o
ll
er
wh
ich
ab
ilit
y to
sim
u
late t
h
e
h
u
m
an
d
eci
sio
n
b
a
sed
on
f
i
n
a
lly, th
e second
t
r
ans
f
o
r
m
a
ti
on con
v
e
r
t
s
val
u
e
s
in
to
th
e real v
a
lu
es
[1
4
]
.
Figu
re
3.
F
u
zz
y
infere
nce
sy
stem
3.
3. Desi
gn of
the
F
u
z
z
y
L
o
g
i
c
Co
ntr
o
l
l
er
The
pl
ant
co
nt
rol
u i
s
i
n
fer
r
e
d
fr
om
t
h
e t
w
o st
at
e
vari
a
b
l
e
s, er
r
o
r
(e
) a
n
d c
h
a
nge
i
n
er
ro
r
Δ
e. T
h
e
cont
rol rules are designed t
o
a
ssign a
fuzzy s
e
t of the c
ont
r
o
l
i
n
p
u
t
u
fo
r e
ach com
b
i
n
at
i
on
of
fuzzy
set
s
of e
and
Δ
e
.
Ta
ble 1 s
h
ows t
h
e
rules
base.
Ea
ch pai
r
(e
,
Δ
e
)
det
e
rm
i
n
es the o
u
t
put
l
e
ve
l
corr
esp
o
ndi
n
g
t
o
u.
Fi
gu
re
4 s
h
ows
t
h
e
fuzzy
l
ogi
c co
nt
r
o
l
l
e
r.
Fi
gu
re
4.
F
u
zz
y
l
ogi
c c
ont
rol
l
er
Tabl
e 1. rul
e
b
a
se
Th
e ab
brev
iati
o
n
s
u
s
ed
in
Tab
l
e 1
are d
e
fin
e
d
as
fo
llows: NB is Negativ
e Big
,
NM
is Neg
a
tiv
e
Med
i
u
m
, NS is Neg
a
tiv
e Sm
all, ZR is Zer
o
, PS is Positiv
e Sm
al
l, PM
is Po
sitiv
e Med
i
u
m
, PB is P
o
sitiv
e
B
i
g, B
i
s
B
i
g and S i
s
sm
al
l
.
Fi
gu
res (
5
–
7
)
rep
r
ese
n
t
,
res
p
ectively, the
me
m
b
ershi
p
fu
nctio
n
s
of
th
e i
n
pu
t e,
th
e m
e
m
b
ersh
ip
fun
c
tion
s
o
f
th
e inpu
t
Δ
e and
th
e m
e
m
b
ersh
ip
fu
n
c
tion
s
of th
e ou
tpu
t
u
.
In
th
is
p
a
p
e
r, t
h
e triang
u
l
ar me
m
b
ersh
ip
fun
c
tio
n, the m
a
x–m
in reasoni
ng m
e
thod, and the cente
r of
gra
v
ity defuzzification m
e
thod are used
, as those m
e
thods
are m
o
st frequen
tly used in
m
a
ny literatures [15-
16]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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S
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8-8
6
9
4
Ad
apt
i
ve F
u
zzy
Lo
gi
c C
ont
r
o
l
of
Wi
n
d
Tur
b
i
n
e Em
ul
at
or (
B
OU
ZI
D M
o
ha
med
A
m
i
n
e)
23
7
Fi
gu
re
5.
M
e
m
b
ers
h
i
p
f
u
nct
i
o
n
fo
r i
n
p
u
t
e
Fi
gu
re
6.
M
e
m
b
ers
h
i
p
f
u
nct
i
o
ns
fo
r i
n
p
u
t
Δ
e
Fi
gu
re
7.
M
e
m
b
ers
h
i
p
f
u
nct
i
o
n
of
o
u
t
p
ut
4.
SIM
U
LATI
O
N
RESULTS
AN
D DIS
C
US
SION
Sim
u
l
a
t
i
ons w
e
re carri
e
d
o
u
t
wi
t
h
a 3k
W PM
SG-
b
ase
d
WEC
S
w
h
i
c
h has t
h
e
opt
im
al powe
r
coefficient Cpmax=0.48 and
th
e op
tim
al t
i
p
-
sp
eed
ratio
λ
=8
.1
.
C
ont
r
o
l
per
f
o
r
m
ances o
f
b
o
t
h
P
I
a
n
d F
U
Z
Z
Y C
ont
ro
llers are co
m
p
ared
in
p
a
rallel.
Th
e st
o
c
h
a
stic
wi
n
d
pr
ofi
l
e
i
s
sho
w
n i
n
Fi
gu
r
e
8.
Fi
gu
re 8.
W
i
nd
vel
o
ci
t
y
Fi
gu
re
9 s
h
ows
t
h
e
out
put
t
r
ac
ki
n
g
per
f
o
r
m
a
nces.
0
10
20
30
40
50
60
70
80
90
10
0
3
4
5
6
7
8
9
10
11
T
i
m
e
[s
]
W
i
nd speed
[
m
/
s
]
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
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:
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94
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J
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S
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l. 4
,
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. 2
,
Jun
e
2
014
:
23
3
–
24
0
23
8
Figu
re
9.
(a
) P
o
we
r c
o
ef
ficient (
b
)
Tip
-
s
p
ee
d
ratio (c
)
Spee
d
I
n
Fi
gu
re
10
, i
t
’s i
n
di
cated t
h
e tracki
n
g errors.
B
o
t
h
o
f
t
h
e t
w
o m
e
t
hods t
r
ac
k t
h
e o
u
t
p
ut
refere
nce ade
qua
t
e
l
y
. The FLC
pro
v
i
d
e
s
bet
t
e
r t
r
acki
n
g
th
an
t
h
e PI contro
ller.
Tw
o im
port
a
n
t
fact
ors s
h
o
w
t
h
e
efficiency of t
h
e powe
r conv
ersi
on: the
powe
r coe
fficient
main
ten
a
n
ce an
d th
e tip-sp
e
ed
ratio
m
a
in
t
e
n
a
n
c
e und
er
wind
sp
eed
fl
u
c
tu
ation
s
. The FLC sho
w
s b
e
tter
p
e
rform
a
n
ces b
e
tter th
an
PI co
n
t
ro
ller i
n
o
p
t
i
m
iz
in
g
th
e power con
v
ersi
on
. Th
e
PI con
t
ro
ller stays oscillatin
g
aroun
d op
tim
a
l
v
a
lu
es.
Th
e FLC k
e
ep
s t
h
e
op
ti
m
a
l p
o
wer co
efficien
t an
d tip
-sp
eed ratio
v
a
lu
es con
s
tant after
tran
sien
t tim
e.
It is clear that
the m
a
xim
u
m powe
r e
x
tract
ion
co
nt
r
o
l
wo
rks
ve
ry
wel
l
whe
r
e
t
h
e val
u
e
o
f
po
we
r
coefficient
was
ke
pt at optim
um
value of power coe
fficient
Cp
opt
w
h
i
c
h
eq
ual
s
0.
48
wi
t
h
vary
i
n
g
wi
n
d
s
p
eed
.
0
1
2
3
4
5
6
7
8
9
10
0
0.
1
0.
2
0.
3
0.
4
0.
5
Ti
m
e
[
s
]
P
o
w
e
r
C
oef
f
i
c
i
ent
(
C
p
)
Cp
m
a
x
FL
C
PI
0
1
2
3
4
5
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
Ti
m
e
[
s
]
T
i
p s
p
eed
r
a
t
i
o
o
p
ti
m
a
l
T
i
p s
p
ee
d
ra
ti
o
FL
C
PI
0
1
2
3
4
5
6
7
8
9
10
0
50
100
150
200
250
300
350
T
i
m
e
[
s ]
D
C
m
o
to
r
S
p
e
e
d
[
r
/ m
i
n
]
S
pee
d r
e
f
e
ren
c
e
PI
FL
C
(a)
(b)
(c)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PED
S
I
S
SN
:
208
8-8
6
9
4
Ad
apt
i
ve F
u
zzy
Lo
gi
c C
ont
r
o
l
of
Wi
n
d
Tur
b
i
n
e Em
ul
at
or (
B
OU
ZI
D M
o
ha
med
A
m
i
n
e)
23
9
Fig
u
r
e
10
.
(
a
) Pow
e
r
co
ef
f
i
cien
t
tr
ack
i
ng
er
ro
r
(b
)
Ti
p-spee
d
ratio trac
king error
(c
)
Spee
d trac
kin
g
e
r
r
o
r
5.
CO
NCL
USI
O
N
In
th
is
work, th
e m
o
d
e
l o
f
the DC
m
o
to
r was in
corpo
r
ated
with
i
n
a larger sim
u
lat
i
o
n
o
f
a PM
SG
syste
m
with
the DC m
o
to
r act
in
g
as th
e prime m
o
v
e
r.
One
o
f
t
h
e a
d
vant
a
g
es
of
t
h
e
W
T
E
i
s
t
h
at
vari
ous
wi
nd
p
r
o
f
i
l
e
s can
be
t
e
st
ed t
o
veri
f
y
t
h
e co
nt
r
o
l
alg
o
rith
m
s
.
Th
e
FLC m
e
t
h
od
can
qu
ickly an
d
accur
a
tely tr
ack
th
e
max
i
m
u
m
p
o
w
e
r ou
tpu
t
for w
i
nd
po
w
e
r
syste
m
.
Sim
u
l
a
t
i
on re
s
u
l
t
s
p
r
ese
n
t
e
d
i
n
t
h
i
s
pa
per
p
r
o
v
e t
h
at
a
go
od
M
PPT
st
rat
e
gy
can
be
i
m
pl
em
ent
e
d
with a
fuzzy logic controller.
Fu
rt
h
e
r work
will b
e
fo
cu
sed on
indu
ctio
n
mach
in
e to emu
l
ate th
e
wind
tu
rb
in
e.
REFERE
NC
ES
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u
r. Productivity
and dev
e
lopm
en
t issues of global wind turbin
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Renewab
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Ener
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: 10
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W
e
ihao, Hu
, Yue W
,
Xianwen
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aoan W
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De
velopm
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i
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u
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o
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ffic
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a
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rro
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ren
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i
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a
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ac
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p
eed
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r
a
cki
ng
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r
r
o
r
[
r
/
m
i
n
]
e
r
ro
r r
e
f
e
re
n
c
e
PI
FL
C
(a)
(b)
(c)
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S
SN
:
2
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PED
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l. 4
,
No
. 2
,
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e
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–
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