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I
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Facu
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o
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s
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ch
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lo
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Hass
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First Un
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Set
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7
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R
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1.
I
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D
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R
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k
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lo
b
al
tr
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s
itio
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to
s
u
s
tain
ab
le
en
er
g
y
[
1
]
.
W
in
d
f
ar
m
s
g
en
er
ate
elec
tr
icity
b
y
h
ar
n
ess
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licates
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ax
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p
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in
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tr
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MPPT)
p
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s
.
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s
p
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if
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win
d
s
p
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th
er
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is
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tim
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m
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p
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.
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ly
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tu
r
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p
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f
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ac
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iev
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m
ax
im
u
m
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g
y
p
r
o
d
u
ctio
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[
2
]
,
[
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
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I
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N:
2088
-
8
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C
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(
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4455
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cr
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wh
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as
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ten
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esear
ch
i
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to
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p
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im
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s
tr
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[
4
]
,
[
5
]
.
Nu
m
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s
MPT
T
m
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th
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s
an
d
co
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v
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f
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.
T
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m
eth
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d
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ter
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s
t,
co
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v
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p
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c
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lex
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s
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ir
em
en
ts
an
d
s
im
p
licity
o
f
im
p
lem
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tatio
n
[
6
]
.
MPPT
tech
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iq
u
es
ar
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g
e
n
er
ally
g
r
o
u
p
e
d
in
to
class
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an
d
ar
tific
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in
tellig
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m
eth
o
d
s
[
7
]
–
[
9
]
.
T
r
a
d
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I
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HC
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an
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P&
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e
n
t sy
s
tem
v
ar
i
ab
les in
a
co
m
p
lex
way
,
an
d
t
o
ad
ap
t th
e
co
n
tr
o
l stra
teg
y
in
r
ea
l tim
e.
Mo
s
t
ex
is
tin
g
wo
r
k
in
th
e
liter
atu
r
e
o
f
win
d
tu
r
b
in
e
m
ax
i
m
u
m
p
o
wer
p
o
in
t
tr
ac
k
in
g
f
o
cu
s
es
o
n
im
p
r
o
v
in
g
tr
ac
k
in
g
s
p
ee
d
,
r
e
d
u
cin
g
o
s
cillatio
n
s
,
an
d
m
a
x
im
izin
g
ex
t
r
ac
ted
p
o
wer
.
Ou
r
wo
r
k
co
n
tin
u
es
t
o
p
u
r
s
u
e
th
ese
o
b
jectiv
es
wh
ile
p
r
o
p
o
s
in
g
an
in
n
o
v
ativ
e
ap
p
r
o
ac
h
:
an
o
p
tim
ized
MPPT
m
o
d
el,
d
esig
n
ed
to
m
in
im
ize
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
wh
ile
g
u
ar
a
n
teein
g
h
ig
h
-
p
er
f
o
r
m
a
n
ce
tr
ac
k
in
g
.
T
h
is
ar
ticle
p
r
esen
ts
th
e
ap
p
licatio
n
o
f
th
r
ee
m
ax
im
u
m
p
o
wer
p
o
in
t
tr
ac
k
in
g
tech
n
iq
u
es:
o
n
e
class
ical
tech
n
iq
u
e,
t
h
at
is
,
p
e
r
tu
r
b
atio
n
an
d
o
b
s
er
v
atio
n
,
an
d
two
in
tel
lig
en
t
tech
n
iq
u
es,
wh
ich
ar
e
a
r
tific
ial
n
eu
r
al
n
etwo
r
k
ar
c
h
itectu
r
es,
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
an
d
d
e
ep
n
e
u
r
al
n
etwo
r
k
s
(
DNN)
.
I
n
p
ar
ticu
lar
,
we
f
o
c
u
s
o
n
o
p
tim
izin
g
th
e
s
tr
u
ctu
r
e
o
f
n
eu
r
al
n
etwo
r
k
s
to
r
ed
u
ce
t
h
eir
co
m
p
le
x
ity
an
d
f
ac
ilit
ate
th
eir
im
p
lem
en
tatio
n
in
em
b
ed
d
ed
s
y
s
tem
s
.
T
h
is
d
o
cu
m
e
n
t
is
d
iv
id
ed
in
t
o
f
o
u
r
m
ain
s
ec
tio
n
s
.
First,
th
e
win
d
tu
r
b
in
e
a
n
d
PMSG
g
en
er
ato
r
m
o
d
els
ar
e
d
escr
ib
ed
in
d
etail.
Nex
t,
t
h
e
d
ev
elo
p
m
en
t
a
n
d
v
alid
atio
n
o
f
t
h
e
co
n
tr
o
l
s
tr
ateg
ies
ar
e
p
r
esen
ted
.
T
h
e
th
ir
d
p
ar
t
d
is
cu
s
s
es
th
e
s
im
u
latio
n
r
esu
lts
.
Fin
ally
,
th
e
co
n
clu
s
io
n
s
u
m
m
ar
izes
th
e
s
tu
d
y
an
d
p
r
esen
ts
p
r
o
s
p
ec
ts
f
o
r
f
u
tu
r
e
im
p
r
o
v
em
en
ts
.
2.
T
H
E
S
T
UD
I
E
D
WI
ND
T
UR
B
I
N
E
SY
ST
E
M
S
As
s
h
o
wn
in
Fig
u
r
e
1
,
th
e
s
y
s
tem
u
n
d
er
s
tu
d
y
co
m
p
r
is
es
a
win
d
tu
r
b
in
e
e
q
u
ip
p
ed
with
a
p
er
m
an
en
t
m
ag
n
et
s
y
n
ch
r
o
n
o
u
s
g
en
er
ato
r
.
T
h
is
d
ev
ice
tr
an
s
f
o
r
m
s
th
e
win
d
'
s
m
ec
h
an
ical
en
er
g
y
in
to
elec
tr
ical
en
er
g
y
in
th
e
f
o
r
m
o
f
t
h
r
ee
-
p
h
ase
alter
n
atin
g
c
u
r
r
en
t.
T
h
e
elec
tr
ical
en
er
g
y
g
en
e
r
ated
b
y
th
e
PM
SG
is
th
en
r
ec
tifie
d
in
to
d
ir
ec
t
c
u
r
r
en
t
b
y
a
d
io
d
e
b
r
id
g
e
.
T
h
is
d
ir
ec
t
cu
r
r
e
n
t
is
th
en
b
o
o
s
ted
in
v
o
ltag
e
b
y
a
b
o
o
s
t
co
n
v
e
r
ter
,
en
ab
lin
g
th
e
m
ax
im
u
m
p
o
wer
p
o
in
t
to
b
e
tr
ac
k
ed
an
d
th
e
v
o
ltag
e
lev
el
to
b
e
ad
a
p
ted
to
t
h
e
r
eq
u
i
r
em
en
ts
o
f
th
e
elec
tr
ical
lo
ad
.
T
h
e
k
ey
elem
en
t
o
f
o
u
r
s
y
s
tem
is
th
e
m
ax
im
u
m
p
o
we
r
p
o
in
t
tr
ac
k
in
g
c
o
n
tr
o
l
tech
n
iq
u
es.
T
h
ese
tech
n
iq
u
es,
s
u
ch
as
p
er
tu
r
b
an
d
o
b
s
er
v
e
,
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
n
etwo
r
k
s
an
d
d
ee
p
n
e
u
r
al
n
etwo
r
k
s
,
aim
t
o
co
n
tin
u
o
u
s
ly
o
p
tim
ize
th
e
o
p
e
r
atio
n
o
f
th
e
win
d
tu
r
b
in
e
in
o
r
d
er
t
o
g
et
th
e
m
ax
im
u
m
av
ailab
le
en
er
g
y
f
r
o
m
th
e
win
d
.
B
y
ad
ju
s
tin
g
th
e
g
e
n
er
ato
r
'
s
o
p
er
atin
g
p
o
in
t,
th
e
s
y
s
tem
's en
er
g
y
ef
f
icien
c
y
is
m
a
x
im
ized
.
Fig
u
r
e
1
.
B
lo
ck
d
iag
r
am
o
f
th
e
s
tu
d
ied
s
y
s
tem
2
.
1
.
M
o
del o
f
wind
t
urbin
e
T
h
e
win
d
t
u
r
b
in
e
is
a
d
ev
ic
e
u
s
ed
f
o
r
co
n
v
er
tin
g
th
e
w
in
d
'
s
en
er
g
y
f
r
o
m
k
in
etic
en
er
g
y
in
t
o
m
ec
h
an
ical
en
e
r
g
y
.
T
h
is
co
n
v
er
s
io
n
p
r
o
ce
s
s
ca
n
b
e
d
escr
ib
ed
b
y
an
eq
u
atio
n
th
at
r
ela
tes
th
e
m
ec
h
an
ical
p
o
wer
p
r
o
d
u
ce
d
(
Pm
)
t
o
th
e
w
in
d
s
p
ee
d
[
1
0
]
–
[
1
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
4
5
4
-
4
4
6
4
4456
=
1
2
2
2
(
1
)
I
n
p
e
r
m
an
en
t
o
p
e
r
atio
n
,
t
h
is
p
o
wer
is
p
r
o
p
o
r
tio
n
al
to
th
e
air
d
e
n
s
ity
(
)
,
th
e
ar
ea
s
wep
t
b
y
th
e
b
lad
es
(
d
eter
m
in
ed
b
y
th
e
tu
r
b
in
e
r
ad
iu
s
R
t
)
,
an
d
th
e
c
u
b
e
o
f
t
h
e
wi
n
d
s
p
ee
d
(
V
w
)
.
A
co
ef
f
icien
t
o
f
p
er
f
o
r
m
a
n
ce
(
Cp
)
is
also
in
clu
d
ed
in
th
is
e
q
u
at
io
n
,
r
ep
r
esen
tin
g
th
e
tu
r
b
in
e'
s
en
er
g
y
ef
f
icien
cy
.
T
h
is
c
o
e
f
f
icien
t
is
s
tr
o
n
g
ly
in
f
lu
en
ce
d
b
y
g
eo
m
etr
ic
p
ar
am
eter
s
s
u
ch
as
th
e
n
u
m
b
e
r
o
f
b
lad
es,
th
eir
p
itch
,
an
d
t
h
eir
p
r
o
f
ile,
an
d
is
th
eo
r
etica
lly
lim
ited
b
y
B
etz
'
s
law.
T
h
e
tip
s
p
ee
d
r
atio
(
λ
)
is
d
ef
in
ed
as
th
e
r
elatio
n
s
h
ip
b
etwe
en
th
e
s
p
ee
d
o
f
th
e
b
lad
e
tip
s
an
d
th
e
win
d
s
p
ee
d
:
=
(
2
)
T
h
e
to
r
q
u
e
p
r
o
d
u
ce
d
b
y
th
e
tu
r
b
in
e
(
)
is
th
en
ex
p
r
ess
ed
b
y
(
3
)
:
=
=
1
2
(
,
)
2
2
(
3
)
W
h
en
th
e
s
p
ee
d
r
atio
is
k
ep
t
a
t
its
o
p
tim
al
v
alu
e
_
,
th
e
p
o
wer
co
ef
f
icien
t
r
ea
ch
es
its
m
ax
im
u
m
_
.
I
n
th
is
co
n
d
itio
n
,
th
e
m
ax
im
u
m
ex
tr
ac
tab
le
p
o
wer
f
r
o
m
th
e
win
d
tu
r
b
in
e
is
:
=
1
2
2
2
(
4
)
Fig
u
r
e
2
illu
s
tr
ates
th
at
f
o
r
ea
ch
win
d
s
p
ee
d
.
T
h
er
e
is
an
o
p
tim
al
r
o
to
r
s
p
ee
d
th
at
allo
ws
t
h
e
tu
r
b
in
e
to
ca
p
tu
r
e
m
a
x
im
u
m
p
o
wer
.
T
h
is
f
lu
ctu
atio
n
in
th
e
p
o
wer
p
o
in
t
h
ig
h
lig
h
ts
th
e
im
p
o
r
tan
c
e
o
f
d
ev
el
o
p
in
g
a
n
d
in
teg
r
atin
g
ef
f
ec
tiv
e
m
o
n
ito
r
i
n
g
o
r
tr
ac
k
in
g
m
et
h
o
d
s
t
o
e
n
s
u
r
e
win
d
tu
r
b
in
es
c
o
n
s
is
ten
tly
p
r
o
d
u
ce
m
ax
im
u
m
p
o
wer
.
Fig
u
r
e
2
.
Po
wer
c
u
r
v
es f
o
r
a
win
d
tu
r
b
in
e
at
v
ar
io
u
s
win
d
s
p
ee
d
s
2
.
2
.
P
er
m
a
nent
m
a
g
net
s
y
nc
hro
no
us
g
ener
a
t
o
r
m
o
del
PS
MG
s
ar
e
co
m
m
o
n
ly
em
p
lo
y
ed
in
win
d
en
er
g
y
s
y
s
tem
s
an
d
o
p
e
r
ate
o
n
t
h
r
ee
p
h
ases
p
r
o
d
u
ce
d
b
y
s
tato
r
f
ield
win
d
in
g
s
.
B
y
n
eg
lectin
g
th
e
h
o
m
o
p
o
lar
co
m
p
o
n
en
ts
o
f
th
e
f
lu
x
,
th
e
m
o
d
el
ca
n
b
e
s
im
p
lifie
d
u
s
in
g
Par
k
tr
an
s
f
o
r
m
atio
n
s
[
1
4
]
–
[
1
7
]
.
T
h
e
s
im
p
lifie
d
m
ath
em
atica
l m
o
d
el
is
d
escr
ib
ed
b
y
th
e
s
y
s
tem
o
f
(
5
)
an
d
(
6
)
:
{
=
−
−
+
=
−
−
+
+
∅
=
−
+
−
1
=
−
−
(
+
1
∅
)
+
1
(
5
)
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:
2088
-
8
7
0
8
C
o
mp
a
r
is
o
n
o
f lo
n
g
s
h
o
r
t
-
term me
mo
r
y
a
n
d
d
ee
p
n
eu
r
a
l
n
e
tw
o
r
k
o
p
timiz
ed
…
(
E
z
z
ito
u
n
i J
a
r
mo
u
n
i
)
4457
T
h
e
elec
tr
o
m
ag
n
etic
to
r
q
u
e
ca
n
th
en
b
e
d
ef
in
e
d
b
y
(
6
)
:
=
3
2
(
2
)
[
(
−
)
+
∅
]
(
6
)
wh
er
e
d
en
o
tes
th
e
n
u
m
b
er
o
f
p
o
le
p
air
s
an
d
ω
is
th
e
g
en
er
ato
r
’
s
a
n
g
u
la
r
s
p
ee
d
.
T
h
e
∅
r
ef
er
s
to
th
e
p
er
m
an
en
t
m
ag
n
et
f
lu
x
.
an
d
ar
e
th
e
d
ir
ec
t
a
n
d
q
u
ad
r
atu
r
e
c
o
m
p
o
n
en
ts
o
f
th
e
s
tato
r
cu
r
r
en
t,
r
esp
ec
tiv
ely
.
an
d
in
d
icate
s
th
e
d
ir
ec
t
an
d
q
u
ad
r
atu
r
e
v
o
ltag
es
o
f
th
e
s
tato
r
.
an
d
s
tan
d
s
f
o
r
th
e
s
tato
r
’
s
d
ir
ec
t
an
d
q
u
ad
r
atu
r
e
in
d
u
ctan
ce
s
,
a
n
d
r
ep
r
esen
ts
th
e
s
tato
r
r
esis
tan
ce
.
3.
WI
ND
E
NE
RG
Y
CO
NVE
R
SI
O
N
SY
ST
E
M
(
W
E
CS)
C
O
NT
RO
L
S
T
RA
T
E
G
Y
R
en
ewa
b
le
en
er
g
y
s
o
u
r
ce
s
,
s
u
ch
as
win
d
tu
r
b
in
es,
a
r
e
c
h
ar
a
cter
ized
b
y
in
ter
m
itten
t
an
d
f
lu
ctu
atin
g
en
er
g
y
p
r
o
d
u
ctio
n
.
T
o
m
ax
im
ize
en
er
g
y
ca
p
t
u
r
e
f
r
o
m
th
ese
s
o
u
r
ce
s
,
MPPT
alg
o
r
ith
m
s
ar
e
u
s
ed
.
B
y
tr
ac
k
in
g
th
e
p
o
wer
o
f
th
e
DC
lin
k
an
d
d
y
n
am
ically
a
d
ju
s
tin
g
th
e
s
y
s
tem
's
o
p
er
atin
g
p
ar
am
ete
r
s
,
MPPT
tech
n
iq
u
e
s
en
ab
le
th
e
s
y
s
tem
to
tr
ac
k
th
e
p
o
in
t
o
f
m
a
x
im
u
m
p
o
we
r
a
n
d
ex
tr
ac
t
th
e
m
ax
im
u
m
a
v
ailab
le
p
o
we
r
f
r
o
m
th
e
win
d
r
eso
u
r
ce
[
1
8
]
–
[
2
0
]
.
T
o
o
v
e
r
co
m
e
th
e
lim
itatio
n
s
o
f
co
n
v
en
tio
n
al
MPPT
co
n
tr
o
ller
s
(
f
ix
ed
p
itch
,
o
s
cillatio
n
s
ar
o
u
n
d
th
e
MPP,
lo
w
tr
ac
k
in
g
ef
f
icien
c
y
,
c
o
m
p
lex
ity
d
u
e
to
a
lar
g
e
n
u
m
b
er
o
f
h
i
d
d
en
n
eu
r
o
n
s
)
,
we
p
r
o
p
o
s
e
in
tellig
en
t M
PP
T
co
n
tr
o
ller
s
b
ased
o
n
an
o
p
tim
i
ze
d
DNN
an
d
L
STM
n
e
u
r
al
n
etwo
r
k
.
Fo
r
MPPT
co
n
tr
o
l,
n
eu
r
al
n
et
wo
r
k
tr
ain
i
n
g
in
v
o
lv
es
p
r
e
-
p
r
o
ce
s
s
in
g
n
o
is
y
d
ata,
th
en
d
iv
i
d
in
g
it
in
t
o
tr
ain
in
g
,
v
alid
atio
n
an
d
test
s
e
ts
.
I
n
b
o
th
DNN
an
d
L
STM
ca
s
es,
th
e
co
n
tr
o
ller
u
s
es
v
o
lta
g
e
(
)
an
d
cu
r
r
en
t
(
)
as
in
p
u
ts
an
d
aim
s
to
o
p
tim
ally
ad
ju
s
t
th
e
d
u
ty
c
y
cle
o
f
th
e
DC
/DC
co
n
v
er
ter
.
T
h
is
s
ec
tio
n
will
b
e
d
ed
icate
d
to
th
e
p
r
esen
tatio
n
o
f
th
e
co
n
t
r
o
ller
s
(
L
STM
an
d
DNN)
u
s
ed
an
d
th
ei
r
d
ev
el
o
p
m
e
n
t stag
es.
3
.
1
.
L
o
ng
s
ho
rt
-
t
er
m
m
emo
ry
(
L
ST
M
)
L
STM
s
ar
e
a
ty
p
e
o
f
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
(
R
NN)
id
ea
l
f
o
r
tim
e
s
er
ies
d
ata.
T
h
ey
ar
e
u
s
ef
u
l
f
o
r
o
p
tim
izin
g
en
e
r
g
y
ex
tr
ac
tio
n
in
win
d
tu
r
b
in
es
b
y
im
p
lem
en
t
in
g
MPPT
alg
o
r
ith
m
s
.
I
n
th
is
s
tu
d
y
,
we
p
r
o
p
o
s
e
to
u
s
e
an
L
STM
n
etwo
r
k
to
p
r
ed
ict
th
e
MPP
o
f
a
win
d
tu
r
b
in
e
in
r
ea
l
tim
e.
I
n
th
e
ca
s
e
o
f
MPPT
c
o
n
tr
o
l,
L
STM
s
ar
e
tr
ain
ed
to
m
o
d
el
th
e
n
o
n
-
lin
ea
r
r
elatio
n
s
h
ip
b
e
twee
n
in
p
u
t
v
ar
ia
b
les
(
v
o
ltag
e,
cu
r
r
en
t)
a
n
d
th
e
o
u
tp
u
t
v
a
r
iab
le
(
d
u
ty
cy
cle)
u
s
ed
to
co
n
tr
o
l
th
e
DC
/DC
in
v
er
ter
.
T
ab
le
1
illu
s
tr
ates
th
e
co
m
p
o
n
en
ts
o
f
th
e
L
STM
co
n
tr
o
ller
a
r
ch
itectu
r
e
u
s
ed
in
th
is
s
tu
d
y
.
I
t
s
h
o
ws
a
two
-
d
im
en
s
io
n
al
in
p
u
t
s
eq
u
e
n
ce
p
r
o
ce
s
s
ed
b
y
a
10
-
u
n
it L
STM
lay
er
,
p
l
u
s
a
f
u
l
ly
co
n
n
ec
te
d
lay
er
a
n
d
a
r
eg
r
e
s
s
io
n
o
u
tp
u
t la
y
er
with
a
s
in
g
l
e
o
u
tp
u
t.
Fig
u
r
e
3
s
h
o
ws
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
L
STM
co
n
t
r
o
ller
.
T
h
e
f
ir
s
t
cu
r
v
e
illu
s
tr
ates
th
e
ev
o
lu
tio
n
o
f
r
o
o
t
m
ea
n
s
q
u
ar
e
e
r
r
o
r
(
R
MSE
)
.
T
h
is
cu
r
v
e
s
h
o
ws
a
r
a
p
i
d
d
ec
r
ea
s
e
in
e
r
r
o
r
at
th
e
s
tar
t
o
f
tr
ain
in
g
,
th
e
n
s
tab
ilizes
at
a
lo
w
v
alu
e.
T
h
is
in
d
icate
s
th
at
th
e
m
o
d
el
is
l
ea
r
n
in
g
ef
f
icien
tly
f
r
o
m
th
e
t
r
ain
in
g
d
ata
an
d
is
ac
h
iev
in
g
s
atis
f
ac
to
r
y
p
er
f
o
r
m
an
ce
.
T
h
e
s
ec
o
n
d
cu
r
v
e
r
ep
r
es
en
ts
th
e
lo
s
s
f
u
n
ctio
n
,
wh
o
s
e
ev
o
lu
tio
n
is
alm
o
s
t
s
im
ilar
to
th
at
o
f
th
e
R
MSE
.
T
h
e
d
ec
r
ea
s
e
in
th
is
lo
s
s
m
ea
n
s
th
at
th
e
m
o
d
el
is
s
u
cc
ess
f
u
lly
m
in
im
izin
g
th
e
d
if
f
er
en
ce
b
etwe
en
its
p
r
ed
icti
o
n
s
an
d
ac
tu
al
v
alu
es.
Fig
u
r
e
3
.
L
STM
co
n
tr
o
ller
tr
ai
n
in
g
p
e
r
f
o
r
m
an
ce
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
4
5
4
-
4
4
6
4
4458
T
ab
le
1
.
T
h
e
u
s
ed
L
STM
co
n
t
r
o
ller
ar
ch
itectu
r
e
N
a
me
Ty
p
e
A
c
t
i
v
a
t
i
o
n
s
1
S
e
q
u
e
n
c
e
i
n
p
u
t
w
i
t
h
2
d
i
m
e
n
s
i
o
n
s
S
e
q
u
e
n
c
e
i
n
p
u
t
2
2
LSTM
w
i
t
h
1
0
h
i
d
d
e
n
u
n
i
t
s
LSTM
10
3
F
u
l
l
y
C
o
n
n
e
c
t
e
d
f
u
l
l
y
c
o
n
n
e
c
t
e
d
l
a
y
e
r
1
4
R
-
mea
n
-
sq
u
a
r
e
d
-
e
r
r
o
r
R
e
g
r
e
ssi
o
n
o
u
t
p
u
t
1
3.
2
.
O
pti
m
ized
deep
neura
l net
wo
rk
s
ANNs
ar
e
in
f
o
r
m
atio
n
p
r
o
c
ess
in
g
s
y
s
tem
s
in
s
p
ir
ed
b
y
th
e
h
u
m
an
b
r
ai
n
.
T
h
ey
h
av
e
b
ee
n
th
e
s
u
b
ject
o
f
in
te
n
s
e
r
esear
ch
an
d
th
e
ex
tr
ac
tio
n
o
f
p
er
tin
en
t
in
f
o
r
m
ati
o
n
f
r
o
m
co
m
p
lex
d
ata.
T
h
eir
f
lex
ib
le
ar
ch
itectu
r
e
en
ab
les
ANNs
to
lear
n
p
atter
n
s
,
m
ak
e
p
r
ed
ictio
n
s
an
d
aid
d
e
cisi
o
n
-
m
ak
in
g
in
m
an
y
ar
ea
s
,
s
u
ch
as
MPPT
f
o
r
win
d
tu
r
b
i
n
es,
wh
ich
is
th
eir
ap
p
licatio
n
o
b
jectiv
e
in
o
u
r
c
ase.
Fig
u
r
e
4
s
h
o
ws
th
e
ar
c
h
i
tectu
r
e
o
f
an
ANN
co
n
ce
iv
ed
f
o
r
tr
ac
k
in
g
th
e
p
o
i
n
t
o
f
m
ax
im
u
m
p
o
wer
.
T
h
e
n
eu
r
al
n
etwo
r
k
u
s
es
v
o
ltag
e
an
d
cu
r
r
en
t
as
in
p
u
ts
.
I
t
is
tr
ain
ed
to
p
r
e
d
ict
th
e
o
p
t
im
al
d
u
ty
c
y
cle
to
ap
p
ly
to
t
h
e
DC
/DC
co
n
v
er
ter
,
with
t
h
e
aim
o
f
ex
tr
ac
tin
g
m
ax
im
u
m
p
o
wer
f
r
o
m
th
e
win
d
tu
r
b
in
e
.
T
h
e
d
esig
n
o
f
n
e
u
r
al
n
etwo
r
k
s
p
o
s
es
ch
allen
g
es
o
f
co
m
p
u
tatio
n
al
co
m
p
lex
ity
,
as
th
e
n
u
m
b
er
o
f
lay
er
s
in
t
h
e
n
etwo
r
k
ca
n
lead
to
e
x
ce
s
s
iv
e
co
m
p
u
tatio
n
tim
es.
I
t
is
cr
u
cial
to
f
in
d
a
b
alan
ce
b
etwe
en
m
o
d
el
co
m
p
lex
ity
a
n
d
ca
p
ac
ity
f
o
r
g
en
er
aliza
tio
n
.
T
h
is
s
tu
d
y
aim
s
to
o
p
tim
ize
t
h
e
ar
c
h
itectu
r
e
o
f
a
d
ee
p
n
e
u
r
a
l
n
etwo
r
k
to
r
ed
u
ce
co
m
p
u
tati
o
n
al
co
s
ts
wh
ile
m
ax
im
izin
g
p
er
f
o
r
m
a
n
ce
.
T
o
ac
h
iev
e
th
is
,
we
ca
r
r
ied
o
u
t
s
ev
er
al
s
er
ies
o
f
e
x
p
er
im
e
n
ts
.
W
e
v
ar
ied
p
a
r
am
eter
s
s
u
ch
as
th
e
n
u
m
b
e
r
o
f
h
i
d
d
en
an
d
o
u
tp
u
t
lay
er
s
(
HL
an
d
HO)
,
ac
tiv
atio
n
f
u
n
ctio
n
s
,
n
u
m
b
er
o
f
n
eu
r
o
n
s
p
er
lay
er
,
n
u
m
b
e
r
o
f
lear
n
i
n
g
iter
atio
n
s
an
d
o
p
tim
izatio
n
alg
o
r
ith
m
s
.
T
h
e
d
etailed
r
esu
lt
s
o
f
th
ese
ex
p
er
im
e
n
ts
ar
e
p
r
e
s
en
ted
in
T
ab
le
2
.
Fig
u
r
e
4
.
MPPT
ar
tific
ial
n
eu
r
al
n
etwo
r
k
ar
c
h
itectu
r
e
T
ab
le
2
.
Su
m
m
a
r
y
o
f
b
est tr
ai
n
in
g
r
esu
lts
A
l
g
o
r
i
t
h
m
M
LP m
o
d
e
l
st
r
u
c
t
u
r
e
H
L
a
n
d
O
L
R
s
q
u
a
r
e
M
S
E
(
x
1
0
-
4)
N
°
o
f
Ep
o
c
h
V
a
r
i
a
b
l
e
l
e
a
r
n
i
n
g
r
a
t
e
g
r
a
d
i
e
n
t
d
e
s
c
e
n
t
[2
-
10
-
1]
Lo
g
si
g
-
P
u
r
e
l
i
n
0
.
9
3
7
3
.
3
4
0
9
3
2
1
[2
-
4
-
1]
Ta
n
s
i
g
-
P
u
r
e
l
i
n
0
.
9
2
6
1
.
0
2
5
9
8
4
2
8
[2
-
6
-
1]
P
u
r
e
l
i
n
-
P
u
r
e
l
i
n
0
.
9
4
2
4
.
3
2
8
6
3
1
9
7
G
r
a
d
i
e
n
t
d
e
s
c
e
n
t
w
i
t
h
m
o
m
e
n
t
u
m
[2
-
3
-
1]
Lo
g
si
g
-
P
u
r
e
l
i
n
0
.
9
4
7
1
.
3
2
9
8
5
3
8
6
[2
-
4
-
1]
Ta
n
s
i
g
-
P
u
r
e
l
i
n
0
.
9
2
6
2
.
2
9
0
4
4
4
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Ma
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ith
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izatio
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ar
c
h
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d
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s
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h
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ig
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lin
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s
atis
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ac
to
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y
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esu
lts
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ter
m
s
o
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MSE
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co
r
r
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co
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icien
t
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o
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h
7
4
.
T
h
ese
im
p
r
o
v
ed
r
esu
lts
ar
e
s
h
o
wn
in
Fig
u
r
e
s
5
(
a)
an
d
5
(
b
)
.
An
aly
s
is
o
f
th
e
p
er
f
o
r
m
an
ce
cu
r
v
e
r
ev
ea
ls
two
d
is
tin
ct
p
h
ases
in
m
o
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el
lear
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g
.
Fro
m
t
h
e
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ir
s
t
f
ew
ep
o
ch
s
we
o
b
s
er
v
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id
d
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r
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in
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m
ea
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u
ar
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r
o
r
,
r
e
f
lectin
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t
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m
ap
p
in
g
o
f
r
elatio
n
s
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ip
s
in
th
e
d
ata
an
d
a
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:
2088
-
8
7
0
8
C
o
mp
a
r
is
o
n
o
f lo
n
g
s
h
o
r
t
-
term me
mo
r
y
a
n
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d
ee
p
n
eu
r
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l
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k
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p
timiz
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…
(
E
z
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a
r
mo
u
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i
)
4459
co
r
r
ec
tly
a
d
ju
s
ted
lear
n
i
n
g
r
a
te.
T
h
is
in
itial
p
h
ase
s
h
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p
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iv
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4
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4
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9
×
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-
5
r
ea
ch
ed
at
7
4
ep
o
ch
s
.
T
h
e
f
i
n
al
s
tab
ilizatio
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o
f
th
e
e
r
r
o
r
at
th
is
ex
tr
em
ely
lo
w
lev
el
(
o
f
th
e
o
r
d
er
o
f
1
0
-
5
)
,
wi
th
n
o
n
o
ticea
b
le
f
lu
ctu
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n
,
c
o
n
f
ir
m
s
n
o
t
o
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ly
t
h
e
m
o
d
el'
s
ab
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to
p
er
f
ec
tly
m
in
im
ize
th
e
g
ap
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etwe
en
p
r
e
d
ictio
n
s
an
d
tar
g
et
v
alu
es,
b
u
t
also
th
e
ab
s
en
ce
o
f
o
v
er
lear
n
in
g
.
Mo
r
eo
v
er
,
th
e
co
r
r
elatio
n
co
e
f
f
icien
t
s
h
o
wn
in
Fig
u
r
e
5
(
b
)
is
v
er
y
clo
s
e
to
1
,
s
h
o
ws
th
e
s
tr
o
n
g
co
r
r
e
latio
n
b
etwe
en
th
e
o
p
tim
ized
DNN
m
o
d
el
o
u
tp
u
ts
an
d
d
esire
d
o
u
tp
u
ts
.
(
a)
(
b
)
Fig
u
r
e
5
.
DNN
m
o
d
el
p
er
f
o
r
m
an
ce
: tr
ain
in
g
,
v
alid
atio
n
a
n
d
t
esti
n
g
(
a)
DNN
tr
ain
in
g
p
er
f
o
r
m
an
ce
an
d
(
b
)
r
e
g
r
ess
io
n
an
aly
s
is
o
f
th
e
DNN
m
o
d
el
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
o
b
jectiv
e
o
f
th
is
s
ec
tio
n
is
to
p
r
esen
t,
a
n
aly
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d
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o
m
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ar
e
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e
r
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m
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o
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t
tr
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k
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n
g
p
r
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d
ed
b
y
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e
L
STM
an
d
DNN
m
o
d
els,
as
well
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b
y
th
e
class
ic
P&
O
m
eth
o
d
.
T
o
v
alid
ate
th
e
r
o
b
u
s
tn
ess
o
f
th
e
m
o
d
els
we
h
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e
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ed
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we
will
test
th
em
u
n
d
er
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d
is
tin
ct
o
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e
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atio
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al
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n
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itio
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s
.
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e
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ir
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in
v
o
lv
e
tr
ac
k
in
g
th
e
m
ax
im
u
m
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p
o
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t
in
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en
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ir
o
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m
en
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f
co
n
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tan
t
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d
s
p
ee
d
,
wh
ile
th
e
s
ec
o
n
d
will a
s
s
ess
th
eir
ab
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to
ad
ap
t to
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ar
iatio
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s
in
w
in
d
s
p
ee
d
.
S
ce
n
a
r
io
1
:
C
o
n
s
ta
n
t wi
n
d
s
p
e
ed
I
n
th
is
ca
s
e,
t
h
e
win
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tu
r
b
in
e
was
g
iv
en
a
co
n
s
tan
t
win
d
s
p
ee
d
(
1
2
m
/s
)
.
Fig
u
r
es
6
(
a
)
,
6
(
b
)
,
a
n
d
6
(
c
)
illu
s
tr
ate
th
e
m
ax
im
u
m
p
o
w
er
p
r
o
d
u
ce
d
u
s
in
g
d
if
f
e
r
en
t
m
eth
o
d
s
:
r
esp
ec
tiv
ely
,
th
e
m
eth
o
d
b
ased
o
n
th
e
p
er
tu
r
b
atio
n
-
o
b
s
er
v
atio
n
tech
n
iq
u
e
(
P&
O_
P),
th
e
p
r
o
p
o
s
ed
m
eth
o
d
b
ased
o
n
th
e
L
STM
m
o
d
el
(
L
STM
_
P)
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
4
5
4
-
4
4
6
4
4460
an
d
th
e
m
eth
o
d
b
ased
o
n
th
e
p
r
o
p
o
s
ed
o
p
tim
ized
DNN
m
o
d
el
(
DNN_
P).
Fig
u
r
e
6
(
d
)
s
h
o
ws
a
co
m
p
ar
is
o
n
b
etwe
en
th
e
p
o
wer
ex
t
r
ac
ted
b
y
th
e
t
h
r
ee
tech
n
iq
u
es
an
d
th
e
th
eo
r
etica
l
m
ax
im
u
m
p
o
wer
(
T
h
eo
r
etica
l_
P).
I
t
is
clea
r
th
at
th
e
th
r
ee
m
eth
o
d
s
ar
e
v
er
y
clo
s
e
in
ter
m
s
o
f
m
ax
im
u
m
p
o
wer
,
b
u
t
a
s
ig
n
i
f
ican
t
d
if
f
er
e
n
ce
is
o
b
s
er
v
ed
w
h
en
u
s
in
g
th
e
p
r
o
p
o
s
ed
DNN
m
o
d
el.
I
n
f
ac
t
,
th
e
u
s
e
o
f
th
e
o
p
tim
ize
d
DNN
m
o
d
el
o
f
f
er
s
h
ig
h
er
p
er
f
o
r
m
an
ce
,
with
an
ef
f
icie
n
cy
o
f
9
8
.
7
%
an
d
a
r
em
ar
k
a
b
le
tr
ac
k
in
g
s
p
ee
d
.
T
h
e
o
p
tim
ized
DNN
m
o
d
el
s
u
cc
ee
d
s
in
lo
ca
tin
g
th
e
p
o
in
t
o
f
m
ax
im
u
m
p
o
wer
in
0
.
0
3
2
s
ec
o
n
d
s
.
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
6
.
T
h
e
m
ax
im
u
m
p
o
we
r
ex
tr
ac
ted
(
c
o
n
s
tan
t w
in
d
s
p
e
ed
)
u
s
in
g
(
a)
P&
O_
P,
(
b
)
L
ST
M_
P,
(
c)
DNN_
P,
an
d
(
d
)
T
h
e
o
r
etica
l_
P
3
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:
2088
-
8
7
0
8
C
o
mp
a
r
is
o
n
o
f lo
n
g
s
h
o
r
t
-
term me
mo
r
y
a
n
d
d
ee
p
n
eu
r
a
l
n
e
tw
o
r
k
o
p
timiz
ed
…
(
E
z
z
ito
u
n
i J
a
r
mo
u
n
i
)
4461
S
ce
n
a
r
io
2
:
va
r
ia
b
le
w
in
d
s
p
e
ed
T
o
v
alid
ate
th
e
r
o
b
u
s
tn
ess
o
f
th
e
tr
ac
k
in
g
s
y
s
tem
in
a
d
y
n
am
ic
en
v
ir
o
n
m
e
n
t,
we
s
im
u
lated
r
ap
id
v
ar
iatio
n
s
in
win
d
s
p
ee
d
(
5
m
/s
,
7
m
/s
,
1
0
m
/s
an
d
1
2
m
/s
)
.
T
h
e
r
esu
lts
p
r
esen
ted
in
Fig
u
r
e
7
s
h
o
w
s
th
at
th
e
o
p
tim
ized
DNN
m
o
d
el
s
tan
d
s
o
u
t
f
o
r
its
ab
ilit
y
to
ef
f
icien
tl
y
tr
ac
k
th
e
p
o
in
t
o
f
m
a
x
im
u
m
p
o
wer
,
e
v
en
in
t
h
e
p
r
esen
ce
o
f
s
u
d
d
en
ly
v
a
r
y
in
g
win
d
s
p
ee
d
s
.
I
n
f
ac
t,
it
is
ch
ar
ac
ter
ized
b
y
its
r
ed
u
ce
d
o
s
cillatio
n
s
,
h
ig
h
ef
f
icien
cy
,
an
d
v
e
r
y
s
h
o
r
t
r
esp
o
n
s
e
tim
e
c
o
m
p
ar
e
d
t
o
ex
is
tin
g
wo
r
k
in
th
e
f
ield
o
f
MPPT
tr
ac
k
in
g
im
p
r
o
v
em
e
n
t f
o
r
win
d
p
o
wer
s
y
s
tem
s
[
2
1
]
–
[
2
5
]
.
Fig
u
r
e
7
.
T
h
e
m
ax
im
u
m
p
o
we
r
ex
tr
ac
ted
(
v
ar
iab
le
win
d
s
p
e
ed
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
4
5
4
-
4
4
6
4
4462
5.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
p
r
esen
ts
an
ap
p
r
o
a
ch
to
MPT
T
in
win
d
p
o
wer
s
y
s
tem
s
,
a
cr
u
cial
ch
allen
g
e
f
o
r
o
p
tim
izin
g
en
er
g
y
p
r
o
d
u
ctio
n
an
d
m
ak
in
g
win
d
p
o
wer
in
s
tallatio
n
s
ef
f
icien
t.
Ou
r
c
o
m
p
ar
ativ
e
s
tu
d
y
s
h
o
ws
th
at
th
is
s
o
lu
tio
n
o
u
tp
e
r
f
o
r
m
s
b
o
th
th
e
co
n
v
en
tio
n
al
(
P&
O)
m
eth
o
d
an
d
th
e
L
STM
n
etwo
r
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ased
ap
p
r
o
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m
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o
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s
ev
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s
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ch
as
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o
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r
ate
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m
ax
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m
p
o
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lo
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lizatio
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s
p
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.
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h
e
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ain
o
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o
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h
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was
to
d
esig
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co
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ller
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o
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l
y
o
f
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d
ap
tin
g
in
r
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l
tim
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to
r
ap
id
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d
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p
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ed
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f
lu
ctu
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s
in
win
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s
p
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b
u
t
also
o
f
m
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y
e
x
tr
ac
tio
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wh
ile
m
ain
tain
in
g
o
p
tim
u
m
o
p
er
atio
n
al
s
tab
ilit
y
an
d
with
o
p
tim
ized
n
eu
r
al
ar
ch
itectu
r
e
.
Ou
r
o
p
tim
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DNN
m
o
d
el
h
as
s
o
m
e
r
em
ar
k
ab
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f
ea
tu
r
es
th
at
m
ak
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it
p
ar
ticu
lar
ly
s
u
itab
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f
o
r
r
ea
l
-
tim
e
ap
p
l
icatio
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s
:
a
r
ed
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ce
d
o
s
cillatio
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r
ate
ar
o
u
n
d
th
e
M
PP
,
a
v
er
y
f
ast
co
n
v
er
g
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ce
s
p
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d
,
an
d
a
v
er
y
co
m
p
ac
t
n
eu
r
al
ar
ch
itectu
r
e
.
T
h
ese
ex
ce
p
tio
n
al
p
er
f
o
r
m
an
ce
s
in
ter
m
s
o
f
s
tab
ilit
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,
s
p
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d
an
d
ef
f
icien
c
y
m
ak
e
o
u
r
ap
p
r
o
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h
p
a
r
ticu
lar
ly
in
ter
esti
n
g
f
o
r
r
ea
l
-
tim
e
im
p
le
m
en
tatio
n
.
RE
F
E
R
E
NC
E
S
[
1
]
C
.
M
.
H
o
n
g
,
C
.
H
.
C
h
e
n
,
a
n
d
C
.
S
.
Tu
,
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a
x
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m
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so
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s,”
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e
r
g
y
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v
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2
.
[
2
]
E.
Jarm
o
u
n
i
,
A
.
M
o
u
h
s
e
n
,
M
.
La
m
h
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md
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,
E
.
En
n
a
j
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h
,
I
.
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n
a
o
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i
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a
n
d
A
.
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r
i
,
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h
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v
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5
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2
3
.
[
3
]
Y
.
Er
r
a
m
i
,
M
.
O
u
a
ssa
i
d
,
a
n
d
M
.
M
a
a
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o
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f
i
,
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o
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f
a
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y
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t
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m
f
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p
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w
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ma
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mi
z
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t
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o
n
a
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d
g
r
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f
a
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t
c
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d
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t
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o
n
s
,
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En
e
r
g
y
Pr
o
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,
v
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p
p
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.
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p
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2
0
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3
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1
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.
0
2
2
.
[
4
]
Y
.
Er
r
a
m
i
,
M
.
O
u
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ssai
d
,
a
n
d
M
.
M
a
a
r
o
u
f
i
,
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p
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n
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o
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r
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d
f
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m,”
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e
rg
y
Pr
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d
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,
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p
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2
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5
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0
7
.
7
3
2
.
[
5
]
B
.
M
e
g
h
n
i
,
N
.
K
.
M
’
S
i
r
d
i
,
a
n
d
A
.
S
a
a
d
o
u
n
,
“
A
n
o
v
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m
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x
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mu
m
p
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t
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c
k
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g
b
y
V
S
A
S
a
p
p
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c
h
f
o
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ma
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t
ma
g
n
e
t
d
i
r
e
c
t
d
r
i
v
e
W
EC
S
,
”
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n
e
rg
y
Pr
o
c
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d
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a
,
v
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l
.
8
3
,
p
p
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9
–
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5
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1
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.
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9
8
.
[
6
]
E.
Jarm
o
u
n
i
,
A
.
M
o
u
h
s
e
n
,
M
.
La
m
h
a
mm
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d
i
,
a
n
d
H
.
O
u
l
d
z
i
r
a
,
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n
t
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r
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o
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o
f
a
r
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f
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l
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l
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so
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man
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me
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t
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smar
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r
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d
,
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t
e
rn
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t
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a
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9
-
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9
2
7
.
[
7
]
Z.
M
.
D
a
l
a
l
a
,
Z
.
U
.
Za
h
i
d
,
W
.
Y
u
,
Y
.
C
h
o
,
a
n
d
J.
S
.
L
a
i
,
“
D
e
s
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n
a
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d
a
n
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l
y
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s o
f
a
n
M
P
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l
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y
c
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v
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sy
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t
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ms,”
I
EEE
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r
a
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s
a
c
t
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s
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En
e
rg
y
C
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,
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o
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3
,
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5
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6
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.
[
8
]
E.
Jarm
o
u
n
i
,
A
.
M
o
u
h
se
n
,
M
.
L
a
m
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m
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d
i
,
H
.
O
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d
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r
a
,
a
n
d
I
.
En
-
N
a
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,
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.
[
9
]
R
.
T
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w
a
r
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a
n
d
N
.
R
.
B
a
b
u
,
“
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b
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se
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v
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m,
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[
1
0
]
A
.
B
.
C
u
l
t
u
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a
a
n
d
Z
.
M
.
S
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l
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me
h
,
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o
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e
rg
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[
1
1
]
M
.
S
t
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b
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,
W
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n
d
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e
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y
s
y
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m
s f
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n
.
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,
2
0
0
8
.
[
1
2
]
R
.
T
i
w
a
r
i
a
n
d
R
.
N
.
B
a
b
u
,
“
C
o
mp
a
r
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t
i
v
e
a
n
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l
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z
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t
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in
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ti
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c
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ti
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ro
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re
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telli
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o
ra
to
r
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f
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e
r
g
y
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a
teria
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stru
m
e
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tatio
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n
d
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lec
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m
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ro
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c
o
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h
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c
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l
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g
y
,
Ha
ss
a
n
1
st
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iv
e
rsity
,
M
o
ro
c
c
o
.
BP
:
5
7
7
,
r
o
u
te
d
e
Ca
sa
b
lan
c
a
.
S
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tt
a
t,
M
o
r
o
c
c
o
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
e
z
z
it
o
u
n
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jarm
o
u
n
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m
a
il
.
c
o
m
.
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e
d
M
o
u
h
se
n
re
c
e
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e
d
h
is
P
h
.
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d
e
g
re
e
in
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lec
tro
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ics
fro
m
th
e
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i
v
e
rsity
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f
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rd
e
a
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x
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ra
n
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n
1
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h
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rre
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tl
y
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r
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t
th
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tri
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n
g
in
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rtme
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a
c
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lt
y
o
f
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s
a
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d
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,
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ss
a
n
I
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v
e
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,
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e
tt
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t,
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r
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c
c
o
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se
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rc
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strie
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ss
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BP
:
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te
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sa
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a
.
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t,
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r
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c
o
.
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c
a
n
b
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c
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n
tac
ted
a
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m
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:
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d
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se
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c
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m
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m
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D.
(2
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)
in
m
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teria
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d
tec
h
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tro
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c
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p
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ts
fro
m
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l
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a
b
a
ti
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r
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rsit
y
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lo
u
se
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ra
n
c
e
.
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r
fo
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r
y
e
a
rs’
re
se
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rc
h
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n
g
in
e
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r
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ra
n
d
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ti
fier
p
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jec
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t
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icro
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lec
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c
s
&
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f
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r
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t
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ro
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m
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th
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tec
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m
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h
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c
tro
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ig
n
a
ls
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n
d
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m
s
(ES
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)
g
r
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p
.
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n
u
a
r
y
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0
1
8
,
h
e
j
o
in
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d
th
e
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a
c
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lt
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o
f
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c
e
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n
d
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h
n
o
l
o
g
y
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n
S
e
tt
a
t,
M
o
r
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c
c
o
,
wh
e
re
h
e
b
e
c
a
m
e
a
m
e
m
b
e
r
o
f
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n
e
rg
y
,
M
a
ter
ials
,
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stru
m
e
n
tati
o
n
&
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c
o
m
La
b
o
ra
to
ry
.
C
u
rre
n
t
re
se
a
rc
h
to
p
ics
in
c
lu
d
e
M
E
M
S
se
n
so
rs
f
o
r
RF
a
p
p
l
ica
ti
o
n
s,
m
a
teria
ls
sc
ien
c
e
s,
in
telli
g
e
n
t
sy
ste
m
s
a
n
d
e
n
e
r
g
y
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
o
h
a
m
e
d
.
lam
h
a
m
d
i@g
m
a
il
.
c
o
m
.
En
n
a
ji
h
Elm
e
h
d
i
,
P
h
.
D.
st
u
d
e
n
t
,
re
c
e
iv
e
d
h
is
m
a
ste
r’s
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fro
m
F
a
c
u
lt
y
o
f
S
c
i
e
n
c
e
a
n
d
Tec
h
n
o
lo
g
y
S
e
tt
a
t,
in
2
0
1
9
,
a
n
d
h
e
is
c
u
r
re
n
tl
y
a
P
ro
fe
ss
o
r
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
ri
n
g
in
BTS
“
Bre
v
e
t
d
e
tec
h
n
icie
n
s
u
p
é
rieu
r”
,
a
t
th
e
M
in
istr
y
o
f
Na
ti
o
n
a
l
Ed
u
c
a
ti
o
n
,
M
o
r
o
c
c
o
.
Hi
s
re
se
a
r
c
h
a
re
a
s
in
c
lu
d
e
e
n
e
rg
y
c
o
n
v
e
rsi
o
n
sy
ste
m
s
,
sy
ste
m
c
o
n
tro
l
a
n
d
a
rti
ficia
l
in
telli
g
e
n
c
e
.
Watc
h
Lab
o
ra
to
r
y
o
f
Eme
rg
in
g
Tec
h
n
o
lo
g
ies
(
LAVETE
),
Th
e
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s
a
n
d
Te
c
h
n
o
l
o
g
y
,
Ha
ss
a
n
F
irst
Un
i
v
e
rsity
o
f
S
e
tt
a
t
,
M
o
r
o
c
c
o
.
BP
:
5
7
7
,
r
o
u
te
d
e
Ca
sa
b
lan
c
a
.
S
e
tt
a
t,
M
o
r
o
c
c
o
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
e
.
e
n
n
a
ji
h
@
u
h
p
.
a
c
.
m
a
.
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