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14
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307
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DC
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8
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I
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Vo
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14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
307
-
3
1
6
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it
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[
3
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lo
w,
s
tar
tin
g
f
r
o
m
g
en
er
atio
n
b
y
r
e
n
ewa
b
le
r
eso
u
r
ce
s
,
th
r
o
u
g
h
th
e
co
n
v
er
s
io
n
p
r
o
ce
s
s
th
at
ad
ap
ts
th
e
p
o
wer
to
u
s
ab
le
f
o
r
m
s
,
an
d
f
in
ally
to
e
n
d
u
s
ag
e.
T
h
is
s
y
s
tem
is
in
d
icativ
e
o
f
a
clo
s
ed
-
lo
o
p
en
er
g
y
s
o
lu
t
io
n
,
r
ed
u
cin
g
r
elian
ce
o
n
n
o
n
-
r
en
ewa
b
le
en
er
g
y
s
o
u
r
ce
s
an
d
co
n
tr
ib
u
tin
g
to
a
r
ed
u
ctio
n
in
ca
r
b
o
n
f
o
o
tp
r
in
t.
I
t
u
n
d
e
r
s
co
r
es
th
e
p
o
ten
tial
o
f
in
teg
r
ated
r
en
ewa
b
le
e
n
er
g
y
s
y
s
tem
s
in
d
r
iv
in
g
f
o
r
war
d
a
clea
n
en
e
r
g
y
tr
a
n
s
itio
n
f
o
r
a
r
a
n
g
e
o
f
ev
er
y
d
ay
ap
p
licatio
n
s
[
4
]
,
[
5
]
.
R
ec
en
t tr
en
d
s
in
th
e
f
ield
h
a
v
e
s
ee
n
a
g
r
o
win
g
i
n
ter
est in
th
e
ap
p
licatio
n
o
f
a
d
v
an
ce
d
tech
n
o
lo
g
ies to
o
v
er
co
m
e
th
ese
lim
itatio
n
s
.
Ma
ch
in
e
lear
n
in
g
(
ML
)
,
with
its
ca
p
ab
ilit
y
to
an
aly
ze
lar
g
e
d
atasets
an
d
p
r
ed
ict
co
m
p
lex
p
atter
n
s
,
h
as
em
er
g
e
d
as
a
p
r
o
m
is
in
g
s
o
lu
tio
n
.
T
h
e
ap
p
licatio
n
o
f
ML
in
r
en
ew
ab
le
en
er
g
y
s
y
s
tem
s
h
as
s
h
o
wn
p
o
ten
tial
in
v
ar
io
u
s
d
o
m
ain
s
,
f
r
o
m
p
r
ed
ictin
g
e
n
er
g
y
g
en
e
r
atio
n
b
ased
o
n
w
ea
th
er
co
n
d
itio
n
s
to
o
p
tim
izin
g
th
e
o
p
er
atio
n
an
d
m
ain
ten
an
ce
o
f
r
en
ewa
b
le
e
n
er
g
y
f
ac
ilit
ies.
T
h
is
ad
o
p
tio
n
o
f
ML
r
ef
lects
a
b
r
o
ad
e
r
s
h
if
t
to
war
d
s
m
o
r
e
i
n
tellig
en
t,
ad
ap
tiv
e
,
an
d
ef
f
i
cien
t
en
er
g
y
m
an
a
g
em
en
t
s
tr
ateg
ies,
ca
p
ab
le
o
f
h
an
d
lin
g
th
e
d
y
n
a
m
ic
n
atu
r
e
o
f
r
en
ewa
b
le
en
e
r
g
y
s
o
u
r
ce
s
.
Fig
u
r
e
2
s
h
o
ws
th
e
ML
b
ased
DC
-
DC
co
n
v
er
ter
ap
p
licatio
n
s
[
6
]
.
Fig
u
r
e
2
.
ML
b
ased
DC
-
DC
co
n
v
er
ter
ap
p
licatio
n
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
n
efficien
t lo
a
d
-
b
a
la
n
cin
g
i
n
ma
ch
in
e
lea
r
n
in
g
-
b
a
s
ed
DC
-
DC
…
(
Ma
llika
r
ju
n
a
s
w
a
my
S
r
i
ka
n
ta
s
w
a
my)
309
Desp
ite
th
e
p
r
o
m
is
in
g
a
d
v
an
c
em
en
ts
,
th
er
e
r
e
m
ain
s
a
s
ig
n
if
ican
t
g
ap
in
r
esear
ch
,
p
ar
ticu
l
ar
ly
in
th
e
ap
p
licatio
n
o
f
ML
f
o
r
lo
a
d
-
b
a
lan
cin
g
in
DC
-
DC
co
n
v
er
s
io
n
s
y
s
tem
s
.
Mo
s
t
ex
is
t
in
g
s
tu
d
ies
h
av
e
f
o
cu
s
ed
o
n
im
p
r
o
v
in
g
in
d
iv
id
u
al
co
m
p
o
n
en
ts
o
f
r
en
ewa
b
le
en
er
g
y
s
y
s
tem
s
,
s
u
ch
as
en
h
an
cin
g
th
e
a
cc
u
r
ac
y
o
f
e
n
er
g
y
g
en
er
atio
n
f
o
r
ec
asts
o
r
o
p
tim
izin
g
th
e
ef
f
icien
cy
o
f
s
in
g
le
c
o
n
v
er
ter
s
.
Ho
wev
e
r
,
t
h
e
h
o
lis
tic
ap
p
licatio
n
o
f
ML
to
d
y
n
am
ically
m
a
n
ag
e
th
e
e
n
tire
co
n
v
er
s
io
n
p
r
o
ce
s
s
,
esp
ec
ially
in
ad
ap
tin
g
to
en
e
r
g
y
s
u
p
p
ly
v
ar
ia
b
ilit
y
an
d
ch
an
g
in
g
lo
ad
s
,
h
as
n
o
t
b
ee
n
t
h
o
r
o
u
g
h
ly
ex
p
lo
r
ed
.
T
h
is
g
ap
h
ig
h
lig
h
ts
th
e
n
ee
d
f
o
r
in
n
o
v
at
iv
e
ap
p
r
o
ac
h
es
th
at
ca
n
lev
er
ag
e
ML
to
en
h
an
ce
n
o
t
o
n
ly
th
e
ef
f
icien
c
y
an
d
r
eli
ab
ilit
y
o
f
th
ese
s
y
s
tem
s
b
u
t
al
s
o
th
eir
ad
ap
tab
ilit
y
an
d
r
esp
o
n
s
iv
en
ess
to
f
lu
ctu
at
in
g
en
er
g
y
in
p
u
ts
[
7
]
.
T
h
is
p
ap
er
in
tr
o
d
u
ce
s
a
n
o
v
el
ML
ap
p
r
o
ac
h
aim
ed
at
b
r
id
g
i
n
g
th
is
r
esear
ch
g
ap
b
y
en
h
a
n
cin
g
lo
a
d
-
b
alan
cin
g
in
DC
-
DC
co
n
v
er
s
io
n
s
y
s
tem
s
u
tili
zin
g
r
en
ewa
b
le
en
er
g
y
.
B
y
m
o
v
in
g
b
e
y
o
n
d
tr
ad
itio
n
al
co
n
tr
o
l
s
tr
ateg
ies,
o
u
r
ML
s
o
lu
tio
n
o
p
tim
izes
k
ey
p
er
f
o
r
m
an
ce
in
d
icato
r
s
s
u
ch
as
c
o
n
v
e
r
s
io
n
e
f
f
icien
cy
,
r
eliab
ilit
y
,
ad
ap
tab
ilit
y
to
en
er
g
y
s
u
p
p
ly
v
ar
iab
ilit
y
,
an
d
r
esp
o
n
s
e
tim
e
to
ch
an
g
in
g
lo
ad
s
.
Har
n
ess
in
g
p
r
ed
ictiv
e
an
aly
tics
an
d
ad
a
p
tiv
e
alg
o
r
ith
m
s
,
o
u
r
ap
p
r
o
ac
h
d
y
n
am
ically
m
a
n
a
g
es
th
e
co
n
v
er
s
io
n
p
r
o
ce
s
s
,
ac
h
iev
in
g
s
u
p
e
r
io
r
p
er
f
o
r
m
an
ce
co
m
p
ar
ed
to
tr
ad
itio
n
al
lo
ad
-
b
alan
cin
g
tech
n
iq
u
es.
I
n
itial
r
esu
lts
u
n
d
er
s
co
r
e
th
e
tr
an
s
f
o
r
m
ativ
e
p
o
ten
tial
o
f
in
teg
r
atin
g
ML
i
n
to
r
en
ewa
b
le
en
e
r
g
y
c
o
n
v
e
r
s
io
n
s
y
s
tem
s
,
p
r
o
m
is
in
g
s
ig
n
if
ican
tly
en
h
an
ce
d
ef
f
icien
cy
an
d
s
y
s
tem
r
esil
ien
ce
.
A
s
r
en
ewa
b
le
en
er
g
y
s
o
u
r
ce
s
b
ec
o
m
e
in
cr
ea
s
in
g
l
y
in
te
g
r
al
to
o
u
r
en
er
g
y
in
f
r
astru
ctu
r
e,
th
e
d
ev
elo
p
m
e
n
t
o
f
s
u
ch
a
d
v
an
ce
d
ML
-
b
ased
s
o
lu
tio
n
s
r
e
p
r
esen
ts
a
cr
iti
ca
l
s
tep
f
o
r
war
d
in
en
s
u
r
in
g
th
eir
ef
f
ec
tiv
e
a
n
d
s
u
s
tain
ab
le
in
teg
r
atio
n
[
8
]
,
[
9
]
.
2.
RE
L
AT
E
D
WO
RK
I
n
r
ec
en
t
y
ea
r
s
,
s
ev
er
al
s
tu
d
ie
s
h
av
e
m
ad
e
s
ig
n
i
f
ican
t
co
n
t
r
ib
u
tio
n
s
to
th
e
f
ield
s
o
f
r
en
ewa
b
le
en
er
g
y
co
n
v
er
s
io
n
.
T
h
e
a
p
p
licatio
n
o
f
ML
in
en
h
an
cin
g
th
e
ef
f
icien
cy
an
d
r
eliab
ilit
y
o
f
th
ese
s
y
s
tem
s
.
A
r
ev
iew
o
f
r
elate
d
wo
r
k
r
e
v
ea
ls
a
s
p
ec
tr
u
m
o
f
in
n
o
v
ativ
e
a
p
p
r
o
ac
h
es,
a
p
p
licatio
n
s
,
an
d
s
o
m
e
lim
itatio
n
s
th
at
h
ig
h
lig
h
t
th
e
n
ee
d
f
o
r
f
u
r
th
er
r
esear
ch
.
E
lm
o
r
s
h
ed
y
et
a
l.
[
1
0
]
in
tr
o
d
u
ce
d
a
d
ee
p
lear
n
i
n
g
m
o
d
el
d
esig
n
ed
to
o
p
tim
ize
th
e
MPPT
p
r
o
ce
s
s
in
s
o
lar
co
n
v
e
r
ter
s
.
T
h
e
n
o
v
elty
o
f
th
eir
ap
p
r
o
ac
h
lies
in
its
u
s
e
o
f
h
is
to
r
ical
a
n
d
r
ea
l
-
tim
e
d
ata
to
p
r
ed
ict
th
e
m
ax
im
u
m
p
o
wer
p
o
in
t
m
o
r
e
ac
cu
r
ately
th
an
tr
ad
itio
n
al
alg
o
r
ith
m
s
.
T
h
is
m
eth
o
d
s
h
o
wed
a
m
ar
k
ed
im
p
r
o
v
em
e
n
t
in
c
o
n
v
er
s
io
n
e
f
f
icien
cy
,
p
ar
ticu
lar
ly
i
n
r
ap
i
d
ly
ch
an
g
in
g
wea
th
e
r
co
n
d
itio
n
s
.
Ho
wev
er
,
th
e
ap
p
licatio
n
was
p
r
im
ar
ily
lim
ited
to
s
o
lar
en
er
g
y
s
y
s
tem
s
,
an
d
th
e
m
o
d
el
r
e
q
u
ir
es
ex
ten
s
iv
e
d
ata
f
o
r
tr
ain
in
g
,
p
o
s
in
g
a
ch
allen
g
e
in
less
in
s
tr
u
m
en
ted
e
n
v
ir
o
n
m
en
ts
.
Den
n
is
to
n
et
a
l.
[
1
1
]
p
r
esen
te
d
a
n
o
v
el
ap
p
licatio
n
o
f
r
ein
f
o
r
ce
m
en
t le
ar
n
in
g
alg
o
r
ith
m
s
t
o
ad
ju
s
t th
e
co
n
tr
o
l
p
ar
a
m
eter
s
o
f
win
d
en
er
g
y
co
n
v
er
ter
s
d
y
n
am
ically
.
T
h
eir
wo
r
k
s
tan
d
s
o
u
t
f
o
r
its
ad
ap
tab
ilit
y
to
v
ar
io
u
s
win
d
co
n
d
itio
n
s
,
s
ig
n
if
ica
n
tly
im
p
r
o
v
in
g
e
n
er
g
y
ca
p
t
u
r
e
e
f
f
i
ci
en
cy
.
W
h
ile
th
e
s
tu
d
y
d
em
o
n
s
tr
ated
p
o
te
n
tial in
win
d
en
e
r
g
y
ap
p
licatio
n
s
,
a
d
r
awb
ac
k
n
o
ted
was th
e
i
n
cr
ea
s
ed
co
m
p
u
tatio
n
al
co
m
p
lex
ity
,
wh
ich
co
u
ld
im
p
ac
t
th
e
r
ea
l
-
tim
e
im
p
lem
en
tatio
n
o
f
th
e
alg
o
r
ith
m
.
Sh
ee
la
et
a
l.
[
1
2
]
f
o
cu
s
ed
o
n
t
h
e
ap
p
licatio
n
o
f
s
u
p
er
v
is
ed
lear
n
in
g
tech
n
iq
u
es to
p
r
ed
ict
a
n
d
b
alan
ce
lo
ad
s
in
d
ata
ce
n
ter
s
p
o
wer
ed
b
y
r
en
ewa
b
le
en
er
g
y
.
T
h
e
n
o
v
elty
o
f
th
eir
r
esear
ch
lies
in
th
e
co
m
p
r
eh
e
n
s
iv
e
in
teg
r
atio
n
o
f
e
n
er
g
y
co
n
s
u
m
p
tio
n
p
atter
n
s
with
r
en
ewa
b
le
e
n
er
g
y
av
ailab
ilit
y
,
th
e
r
eb
y
o
p
ti
m
izin
g
b
o
th
en
e
r
g
y
u
s
e
an
d
o
p
e
r
atio
n
al
co
s
ts
.
Ho
wev
er
,
th
e
m
o
d
el'
s
ef
f
ec
tiv
en
ess
is
h
ig
h
ly
d
ep
en
d
e
n
t
o
n
t
h
e
q
u
ality
an
d
q
u
an
tity
o
f
av
ailab
le
d
ata
,
lim
itin
g
its
ap
p
licab
ilit
y
in
s
ce
n
ar
io
s
with
i
n
ad
eq
u
ate
d
ata
tr
ac
k
in
g
s
y
s
tem
s
.
Ab
b
asi
et
a
l.
[
1
3
]
ex
p
lo
r
ed
t
h
e
u
s
e
o
f
ML
f
o
r
m
a
n
ag
in
g
h
y
b
r
id
s
y
s
tem
s
co
m
b
in
in
g
s
o
lar
an
d
win
d
en
er
g
y
.
T
h
eir
in
n
o
v
ativ
e
a
p
p
r
o
ac
h
u
s
es
a
co
m
b
i
n
atio
n
o
f
p
r
ed
ictiv
e
an
aly
tics
an
d
r
ea
l
-
tim
e
d
ata
to
m
ak
e
im
m
ed
iate
ad
ju
s
tm
en
ts
to
th
e
s
y
s
tem
'
s
o
p
er
atio
n
,
en
h
a
n
cin
g
o
v
er
all
ef
f
icien
c
y
an
d
r
eliab
ilit
y
.
On
e
lim
itatio
n
,
h
o
wev
er
,
is
th
e
m
o
d
el'
s
d
ep
e
n
d
en
cy
o
n
co
n
tin
u
o
u
s
an
d
r
el
iab
le
d
ata
s
tr
ea
m
s
,
wh
ic
h
ca
n
b
e
c
h
allen
g
in
g
in
r
em
o
te
o
r
u
n
s
tab
le
en
v
ir
o
n
m
e
n
ts
.
E
ac
h
o
f
th
ese
s
tu
d
ies
co
n
tr
i
b
u
t
es
to
th
e
ev
o
lv
in
g
lan
d
s
ca
p
e
o
f
r
en
ewa
b
le
e
n
er
g
y
m
an
a
g
em
e
n
t
th
r
o
u
g
h
th
e
ap
p
licatio
n
o
f
ML
tech
n
i
q
u
es.
W
h
ile
th
ey
d
em
o
n
s
tr
ate
th
e
p
o
ten
tial
f
o
r
s
ig
n
if
ican
t
im
p
r
o
v
em
e
n
ts
in
ef
f
icien
cy
,
ad
a
p
tab
ilit
y
,
an
d
r
e
liab
ilit
y
,
co
m
m
o
n
d
r
aw
b
ac
k
s
i
n
clu
d
e
th
e
n
ee
d
f
o
r
ex
ten
s
iv
e
d
ata,
co
m
p
u
tatio
n
al
co
m
p
lex
ity
,
a
n
d
th
e
ch
allen
g
es
o
f
r
ea
l
-
tim
e
im
p
lem
en
tatio
n
.
T
h
ese
in
s
ig
h
ts
u
n
d
er
s
co
r
e
th
e
im
p
o
r
tan
ce
o
f
o
n
g
o
in
g
r
esear
ch
to
r
ef
in
e
ML
alg
o
r
ith
m
s
,
r
ed
u
ce
co
m
p
u
tatio
n
al
d
em
an
d
s
,
an
d
im
p
r
o
v
e
th
e
r
o
b
u
s
tn
ess
o
f
th
ese
s
y
s
tem
s
in
d
iv
er
s
e
o
p
er
atin
g
c
o
n
d
itio
n
s
.
3.
M
E
T
H
O
DO
L
O
G
Y
Me
th
o
d
o
lo
g
y
is
s
tr
u
ctu
r
ed
to
s
y
s
tem
atica
lly
ad
d
r
ess
th
e
ch
allen
g
es
o
f
lo
a
d
b
alan
ci
n
g
i
n
DC
-
DC
co
n
v
er
s
io
n
s
y
s
tem
s
f
o
r
r
en
ew
ab
le
en
er
g
y
ap
p
licatio
n
s
as
s
h
o
wn
in
th
e
Fig
u
r
e
3
.
I
t
in
v
o
lv
es
th
e
co
llectio
n
o
f
r
elev
an
t
d
ata,
d
ev
el
o
p
m
en
t
a
n
d
tr
ain
in
g
o
f
th
e
ML
m
o
d
el,
an
d
r
ig
o
r
o
u
s
test
in
g
an
d
v
ali
d
atio
n
p
h
ases
.
T
h
e
ap
p
r
o
ac
h
is
d
esig
n
ed
to
o
p
ti
m
ize
k
ey
p
e
r
f
o
r
m
an
ce
in
d
ica
to
r
s
,
in
clu
d
i
n
g
co
n
v
er
s
io
n
ef
f
icien
cy
,
r
eliab
ilit
y
,
ad
ap
tab
ilit
y
to
en
e
r
g
y
s
u
p
p
ly
v
ar
iab
ilit
y
,
an
d
r
esp
o
n
s
e
tim
e
to
ch
an
g
i
n
g
lo
a
d
s
[
1
4
]
,
[
1
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
307
-
3
1
6
310
Fig
u
r
e
3
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
o
f
ML
-
b
ased
DC
-
DC
co
n
v
er
s
io
n
m
o
d
el
4.
P
RO
P
O
SE
D
M
A
T
H
E
M
A
T
I
CAL E
Q
U
AT
I
O
NS A
ND
A
L
G
O
RIT
H
M
S
T
h
e
p
r
o
p
o
s
ed
a
ML
-
b
ased
s
o
lu
tio
n
f
o
r
en
h
a
n
cin
g
lo
ad
-
b
alan
cin
g
in
DC
-
DC
co
n
v
er
s
i
o
n
s
y
s
tem
s
u
tili
zin
g
r
en
ewa
b
le
en
er
g
y
is
cr
u
cial.
I
t
r
eq
u
ir
es
th
e
to
d
ef
in
e
a
s
et
o
f
m
ath
em
atica
l
eq
u
ati
o
n
s
th
at
ca
p
tu
r
e
th
e
ess
en
ce
o
f
th
e
p
r
o
b
lem
a
n
d
th
e
m
eth
o
d
o
lo
g
y
o
f
th
e
s
o
lu
tio
n
.
T
h
ese
eq
u
atio
n
s
will
f
o
r
m
th
e
f
o
u
n
d
atio
n
o
f
t
h
e
p
r
ed
ictiv
e
an
d
ad
ap
ti
v
e
alg
o
r
it
h
m
s
th
at
ar
e
ce
n
tr
al
t
o
th
e
p
r
o
p
o
s
ed
ML
m
o
d
el
[
1
6
]
,
[
1
7
]
.
4
.
1
.
E
nerg
y
co
nv
er
s
io
n e
f
f
iciency
I
n
th
e
p
r
o
p
o
s
ed
wo
r
k
,
e
n
er
g
y
co
n
v
er
s
io
n
e
f
f
icien
cy
is
a
cr
iti
ca
l p
er
f
o
r
m
a
n
ce
in
d
icato
r
th
at
q
u
an
tifie
s
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
DC
-
D
C
co
n
v
er
s
io
n
s
y
s
tem
in
tr
an
s
f
o
r
m
in
g
in
p
u
t
p
o
wer
f
r
o
m
r
en
ew
ab
le
en
er
g
y
s
o
u
r
ce
s
in
to
u
s
ab
le
o
u
tp
u
t
p
o
wer
[
1
5
]
.
T
h
e
ef
f
icien
cy
o
f
th
e
DC
-
DC
co
n
v
er
ter
is
m
ath
em
atica
lly
r
ep
r
esen
ted
b
y
(
1
)
.
W
h
er
e
ƞ
is
r
ep
r
esen
ted
as
e
f
f
ici
en
cy
o
f
th
e
DC
-
DC
co
n
v
er
ter
,
is
d
escr
ib
ed
as
th
e
o
u
tp
u
t
p
o
w
er
,
an
d
is
id
en
tifie
d
as th
e
in
p
u
t p
o
wer
f
r
o
m
r
e
n
ewa
b
le
s
o
u
r
ce
s
.
ƞ
=
∗
100%
(
1
)
4
.
2
.
P
o
wer
ba
la
nce
equa
t
io
n
I
n
o
u
r
p
r
o
p
o
s
ed
wo
r
k
f
o
cu
s
in
g
o
n
en
h
a
n
cin
g
lo
ad
-
b
alan
cin
g
in
DC
-
DC
co
n
v
er
s
io
n
s
y
s
te
m
s
u
s
in
g
r
en
ewa
b
le
en
e
r
g
y
s
o
u
r
ce
s
,
t
h
e
Po
wer
B
alan
ce
E
q
u
atio
n
p
la
y
s
a
f
o
u
n
d
atio
n
al
r
o
le
[
1
]
.
T
h
is
e
q
u
atio
n
is
ess
en
tial
f
o
r
en
s
u
r
in
g
th
at
t
h
e
en
er
g
y
s
y
s
tem
's in
p
u
t,
o
u
tp
u
t,
an
d
s
to
r
a
g
e
elem
en
ts
ar
e
in
h
ar
m
o
n
y
,
m
ain
tain
in
g
s
tab
ilit
y
an
d
ef
f
icien
c
y
,
it is
ex
p
r
ess
ed
as
(
2
)
:
=
+
+
∆
(
2
)
W
h
er
e
r
e
p
r
esen
ts
th
e
p
o
wer
i
n
p
u
t
f
r
o
m
r
e
n
ewa
b
le
s
o
u
r
ce
s
,
d
en
o
tes
th
e
p
o
wer
co
n
s
u
m
ed
b
y
th
e
lo
ad
,
ac
co
u
n
ts
f
o
r
th
e
lo
s
s
es
with
in
th
e
s
y
s
tem
,
an
d
∆
s
ig
n
if
ies
th
e
ch
an
g
e
in
s
to
r
ed
en
er
g
y
,
wh
ic
h
ca
n
eith
er
b
e
p
o
s
itiv
e
(
en
er
g
y
b
ein
g
s
to
r
ed
)
o
r
n
e
g
ativ
e
(
en
e
r
g
y
b
ein
g
r
elea
s
ed
f
r
o
m
s
to
r
a
g
e)
.
4
.
3
.
P
re
dict
iv
e
m
o
del f
o
r
ener
g
y
s
up
p
ly
a
nd
dem
a
nd
I
n
o
u
r
p
r
o
p
o
s
ed
ML
-
b
ased
ap
p
r
o
ac
h
to
en
h
a
n
ce
lo
ad
-
b
a
lan
cin
g
in
DC
-
DC
co
n
v
er
s
io
n
s
y
s
tem
s
u
tili
zin
g
r
en
ewa
b
le
en
e
r
g
y
,
a
p
r
ed
ictiv
e
m
o
d
el
f
o
r
en
er
g
y
s
u
p
p
ly
an
d
d
em
a
n
d
o
cc
u
p
ies a
c
en
tr
al
r
o
le
[
2
]
.
T
h
is
m
o
d
el
is
d
esig
n
ed
to
f
o
r
ec
ast
f
u
tu
r
e
en
e
r
g
y
a
v
ailab
ilit
y
f
r
o
m
r
en
ewa
b
le
s
o
u
r
ce
s
an
d
an
ticip
ated
en
er
g
y
d
em
a
n
d
,
en
ab
lin
g
p
r
o
ac
tiv
e
ad
ju
s
tm
en
t
s
to
th
e
s
y
s
tem
'
s
lo
ad
-
b
alan
ci
n
g
s
tr
ateg
ies.
T
h
e
co
r
e
o
f
th
is
p
r
ed
ictiv
e
m
o
d
el
is
en
ca
p
s
u
lated
b
y
(
3
).
̂
.
+
1
=
(
,
−
1
,
…
…
…
.
−
;
)
(
3
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
n
efficien
t lo
a
d
-
b
a
la
n
cin
g
i
n
ma
ch
in
e
lea
r
n
in
g
-
b
a
s
ed
DC
-
DC
…
(
Ma
llika
r
ju
n
a
s
w
a
my
S
r
i
ka
n
ta
s
w
a
my)
311
W
h
er
e
̂
+
1
r
ep
r
esen
ts
th
e
p
r
ed
icte
d
v
alu
e
f
o
r
th
e
n
ex
t
tim
e
p
er
io
d
,
,
−
1
,
…
…
…
.
−
;
ar
e
th
e
o
b
s
er
v
ed
v
alu
es
at
cu
r
r
en
t
an
d
p
r
e
v
io
u
s
tim
e
p
er
io
d
s
,
d
en
o
tes
th
e
p
ar
am
et
er
s
o
f
th
e
ML
m
o
d
el,
an
d
(
.
)
s
y
m
b
o
lizes
th
e
f
o
r
ec
asti
n
g
f
u
n
ctio
n
.
T
h
is
f
u
n
ctio
n
co
u
l
d
b
e
in
s
tan
tiated
th
r
o
u
g
h
v
a
r
io
u
s
ML
tec
h
n
iq
u
es
,
in
clu
d
in
g
b
u
t
n
o
t
lim
ited
to
,
a
u
to
r
eg
r
ess
iv
e
in
teg
r
ated
m
o
v
in
g
av
e
r
ag
e
(
AR
I
MA
)
,
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
STM
)
n
etwo
r
k
s
,
o
r
co
n
v
o
lu
ti
o
n
al
n
e
u
r
al
n
etwo
r
k
s
(
C
NNs),
d
ep
en
d
in
g
o
n
th
e
n
atu
r
e
o
f
th
e
d
ata
an
d
th
e
s
p
ec
if
ic
r
eq
u
ir
e
m
en
ts
o
f
th
e
s
y
s
tem
[
1
8
]
.
4
.
4
.
Ada
ptiv
e
co
ntr
o
l a
lg
o
ri
t
hm
f
o
r
lo
a
d ba
la
ncing
Pro
p
o
s
ed
wo
r
k
o
n
en
h
a
n
cin
g
lo
ad
-
b
alan
cin
g
in
DC
-
DC
co
n
v
er
s
io
n
s
y
s
tem
s
u
tili
zin
g
r
en
ewa
b
le
en
er
g
y
th
r
o
u
g
h
ML
,
th
e
d
ev
elo
p
m
en
t
o
f
a
n
a
d
ap
tiv
e
c
o
n
tr
o
l
alg
o
r
ith
m
is
p
i
v
o
tal
[
1
9
]
.
T
h
is
alg
o
r
ith
m
is
d
esig
n
ed
to
d
y
n
am
ically
ad
ju
s
t
th
e
s
y
s
tem
's
p
ar
am
eter
s
in
r
e
al
-
tim
e
to
m
ain
tain
o
p
tim
al
p
e
r
f
o
r
m
a
n
ce
d
esp
ite
th
e
in
h
er
en
t
v
a
r
iab
ilit
y
in
en
e
r
g
y
s
u
p
p
l
y
f
r
o
m
r
en
ewa
b
le
s
o
u
r
ce
s
an
d
f
lu
ctu
atin
g
en
er
g
y
d
em
an
d
s
.
T
h
e
c
o
r
e
o
b
jectiv
e
o
f
t
h
is
ad
ap
tiv
e
co
n
t
r
o
l a
lg
o
r
ith
m
is
en
ca
p
s
u
lated
i
n
th
e
o
p
tim
izatio
n
(
4
)
min
(
,
̂
;
)
+
(
)
(
4
)
W
h
e
r
e
(
,
̂
;
)
r
e
p
r
e
s
e
n
ts
t
h
e
l
o
s
s
f
u
n
c
t
i
o
n
,
m
e
a
s
u
r
i
n
g
t
h
e
d
i
s
c
r
e
p
a
n
cy
b
e
t
w
e
e
n
t
h
e
p
r
e
d
i
ct
e
d
v
a
l
u
es
̂
a
n
d
t
h
e
a
c
t
u
a
l
o
b
s
e
r
v
e
d
v
a
l
u
e
s
,
d
e
n
o
t
e
s
t
h
e
c
o
n
t
r
o
l
p
a
r
a
m
e
t
e
r
s
o
f
t
h
e
s
y
s
t
e
m
,
(
)
i
s
a
r
e
g
u
l
a
r
i
z
at
i
o
n
t
e
r
m
t
o
p
r
e
v
e
n
t
o
v
e
r
f
i
t
t
i
n
g
,
a
n
d
i
s
t
h
e
r
e
g
u
l
a
r
i
za
t
i
o
n
p
a
r
a
m
e
t
e
r
t
h
at
b
a
l
a
n
c
es
th
e
l
o
s
s
f
u
n
c
ti
o
n
a
n
d
t
h
e
r
e
g
u
l
a
r
i
z
a
t
i
o
n
t
e
r
m
[
2
0
]
.
5.
P
RO
P
O
SE
D
L
O
AD
-
B
A
L
A
NCING
I
N
M
ACH
I
NE
L
E
A
RNING
-
B
A
SE
D
D
C
-
DC
CO
NVERS
I
O
N
USI
NG
R
E
NE
WAB
L
E
E
N
E
RG
Y
RE
SO
UR
CE
S
T
h
e
p
r
o
p
o
s
ed
b
lo
ck
d
iag
r
am
s
h
o
wn
in
Fig
u
r
e
4
v
is
u
ally
en
ca
p
s
u
lates
th
e
in
teg
r
atio
n
o
f
a
ML
ap
p
r
o
ac
h
in
to
DC
-
DC
co
n
v
er
s
io
n
s
y
s
te
m
s
p
o
wer
ed
b
y
r
en
ewa
b
le
en
er
g
y
s
o
u
r
ce
s
,
aim
in
g
t
o
en
h
a
n
ce
lo
ad
-
b
alan
cin
g
ef
f
icien
cy
.
At
th
e
f
o
u
n
d
atio
n
o
f
th
is
s
y
s
tem
ar
e
th
e
r
en
ewa
b
le
en
er
g
y
s
o
u
r
ce
s
,
in
clu
d
in
g
s
o
lar
p
an
e
ls
an
d
win
d
tu
r
b
in
es,
wh
ich
s
er
v
e
as
th
e
p
r
im
ar
y
in
p
u
ts
.
T
h
ese
s
o
u
r
ce
s
f
e
ed
in
to
a
d
ata
co
llectio
n
m
o
d
u
le
th
at
g
ath
er
s
b
o
th
r
ea
l
-
tim
e
an
d
h
is
to
r
ical
d
ata,
s
u
ch
as
s
o
lar
ir
r
ad
ia
n
ce
,
win
d
s
p
ee
d
,
an
d
en
e
r
g
y
o
u
tp
u
t,
ess
en
tial
f
o
r
i
n
f
o
r
m
e
d
d
ec
is
io
n
-
m
ak
in
g
.
C
en
t
r
al
to
th
e
d
iag
r
am
is
th
e
ML
Mo
d
el,
a
co
m
p
lex
u
n
it
co
m
p
r
is
in
g
two
m
ain
s
u
b
-
co
m
p
o
n
en
ts
:
Pre
d
ictiv
e
An
al
y
tics
an
d
Ad
a
p
tiv
e
Alg
o
r
it
h
m
s
.
T
h
e
Pre
d
ictiv
e
An
aly
tic
s
s
u
b
-
co
m
p
o
n
en
t
is
r
esp
o
n
s
ib
le
f
o
r
f
o
r
ec
asti
n
g
f
u
tu
r
e
co
n
d
itio
n
s
o
f
en
er
g
y
s
u
p
p
ly
an
d
d
em
an
d
,
lev
e
r
a
g
in
g
p
a
s
t
an
d
p
r
esen
t
d
ata
[
2
1
]
.
I
n
tu
r
n
,
th
e
Ad
ap
ti
v
e
Al
g
o
r
ith
m
s
u
tili
ze
th
ese
f
o
r
ec
asts
to
d
y
n
am
ically
a
d
ju
s
t
th
e
o
p
er
atio
n
o
f
th
e
DC
-
DC
co
n
v
er
ter
,
en
s
u
r
in
g
o
p
ti
m
al
ef
f
icien
cy
an
d
r
eliab
ilit
y
u
n
d
er
v
ar
y
in
g
c
o
n
d
itio
n
s
.
T
h
is
is
wh
er
e
th
e
co
r
e
in
n
o
v
atio
n
lies
,
m
o
v
in
g
b
ey
o
n
d
tr
ad
itio
n
al
c
o
n
tr
o
l
s
tr
ateg
ies
to
a
m
o
r
e
r
esp
o
n
s
iv
e,
d
ata
-
d
r
i
v
en
ap
p
r
o
ac
h
.
T
h
e
DC
-
DC
c
o
n
v
er
ter
b
lo
ck
is
d
ep
icted
as
r
ec
eiv
in
g
in
s
tr
u
ctio
n
s
f
r
o
m
th
e
ML
m
o
d
el.
I
t
is
th
e
h
ea
r
t
o
f
th
e
co
n
v
er
s
io
n
p
r
o
ce
s
s
,
tr
an
s
f
o
r
m
in
g
th
e
v
ar
iab
le
in
p
u
t
f
r
o
m
r
en
ewa
b
le
s
o
u
r
ce
s
in
to
a
s
tab
le
o
u
tp
u
t
th
at
m
ee
ts
th
e
lo
ad
r
eq
u
ir
em
en
ts
.
A
n
o
p
tio
n
al
en
er
g
y
s
to
r
ag
e
s
y
s
tem
b
lo
ck
ca
n
b
e
in
cl
u
d
ed
f
o
r
s
y
s
tem
s
th
at
in
co
r
p
o
r
ate
s
to
r
ag
e
s
o
lu
tio
n
s
,
m
an
a
g
ed
b
y
th
e
ML
m
o
d
el
to
o
p
tim
ize
en
er
g
y
av
ailab
ilit
y
[
2
2
]
.
Fig
u
r
e
4
.
Pro
p
o
s
ed
lo
a
d
-
b
alan
cin
g
in
ML
-
b
ased
DC
-
DC
co
n
v
er
s
io
n
u
s
in
g
r
en
ewa
b
le
e
n
er
g
y
r
eso
u
r
ce
s
E
n
er
g
y
d
is
tr
ib
u
tio
n
is
h
a
n
d
led
b
y
th
e
lo
a
d
m
an
a
g
em
en
t
b
lo
ck
,
wh
ich
en
s
u
r
es
th
at
th
e
co
n
v
er
ted
en
er
g
y
is
ef
f
icien
tly
allo
ca
ted
ac
r
o
s
s
d
if
f
er
en
t
l
o
ad
s
,
m
ain
tain
i
n
g
s
y
s
tem
b
alan
ce
.
T
h
e
p
er
f
o
r
m
a
n
c
e
m
o
n
ito
r
i
n
g
b
lo
ck
f
o
r
m
s
a
cr
itical
f
ee
d
b
ac
k
lo
o
p
,
s
en
d
in
g
r
ea
l
-
tim
e
p
er
f
o
r
m
a
n
ce
d
ata
b
ac
k
to
th
e
ML
m
o
d
el.
T
h
is
allo
ws
f
o
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
307
-
3
1
6
312
co
n
tin
u
o
u
s
lear
n
in
g
an
d
s
y
s
tem
o
p
tim
izatio
n
b
ased
o
n
ac
tu
al
o
p
er
atio
n
al
m
etr
ics.
Fin
ally
,
em
b
ed
d
e
d
with
in
th
e
ML
m
o
d
el
is
th
e
co
n
tr
o
l
u
n
it,
wh
ich
im
p
le
m
en
ts
th
e
d
ec
is
io
n
s
o
f
t
h
e
a
d
ap
tiv
e
alg
o
r
i
th
m
s
.
T
h
is
in
cl
u
d
es
ad
ju
s
tin
g
th
e
DC
-
DC
co
n
v
er
ter
'
s
p
ar
am
eter
s
an
d
m
an
ag
in
g
th
e
en
er
g
y
s
to
r
a
g
e
to
ad
ap
t
t
o
ch
an
g
in
g
en
er
g
y
s
u
p
p
ly
an
d
d
em
a
n
d
co
n
d
itio
n
s
[
2
3
]
,
[
2
4
]
.
Dir
ec
tio
n
al
ar
r
o
ws
with
in
th
e
d
iag
r
am
illu
s
tr
ate
th
e
f
lo
w
o
f
d
ata
a
n
d
e
n
er
g
y
th
r
o
u
g
h
t
h
e
s
y
s
tem
,
em
p
h
asizin
g
th
e
in
ter
co
n
n
ec
t
ed
n
ess
o
f
ea
ch
co
m
p
o
n
e
n
t.
Fro
m
th
e
in
itial
en
er
g
y
ca
p
tu
r
e
to
th
e
f
in
al
lo
ad
d
is
tr
ib
u
tio
n
,
ea
ch
s
tep
is
in
f
o
r
m
ed
b
y
ML
-
d
r
iv
en
i
n
s
ig
h
ts
,
en
s
u
r
in
g
th
at
th
e
s
y
s
tem
o
p
e
r
ates
at
p
ea
k
ef
f
icien
cy
.
T
h
is
p
r
o
p
o
s
ed
b
l
o
ck
d
iag
r
a
m
n
o
t
o
n
ly
s
h
o
wca
s
es
th
e
o
p
er
atio
n
al
f
r
am
ewo
r
k
b
u
t
also
h
ig
h
lig
h
ts
th
e
tr
an
s
f
o
r
m
ativ
e
p
o
ten
tial
o
f
in
teg
r
atin
g
ML
in
t
o
r
e
n
ewa
b
le
en
er
g
y
co
n
v
er
s
io
n
s
y
s
tem
s
,
p
r
o
m
is
in
g
en
h
an
c
ed
ef
f
icien
cy
an
d
ad
a
p
tab
ilit
y
[
2
5
]
.
6.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
ab
le
1
s
h
o
ws
th
e
s
im
u
latio
n
p
ar
am
eter
s
s
et
f
o
r
th
e
p
er
f
o
r
m
an
ce
an
aly
s
is
o
f
th
e
p
r
o
p
o
s
ed
m
ac
h
in
e
lear
n
in
g
-
b
ased
DC
-
DC
co
n
v
e
r
s
io
n
alg
o
r
ith
m
(
ML
-
DC
2
A)
.
T
h
is
an
aly
s
is
is
d
esig
n
ed
to
ass
es
s
th
e
s
y
s
tem
's
ef
f
icien
cy
,
r
eliab
ilit
y
,
an
d
ad
ap
tab
ilit
y
to
v
a
r
iab
le
en
er
g
y
s
u
p
p
lies
an
d
lo
a
d
d
em
a
n
d
s
.
T
h
e
p
ar
am
ete
r
s
ar
e
ca
r
ef
u
lly
ch
o
s
en
to
p
r
o
v
id
e
a
co
m
p
r
e
h
en
s
iv
e
an
d
r
ea
lis
tic
ev
alu
atio
n
o
f
th
e
s
y
s
tem
'
s
p
er
f
o
r
m
a
n
ce
u
n
d
er
a
v
ar
iety
o
f
co
n
d
itio
n
s
th
at
m
im
ic
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
s
.
T
ab
le
1
.
Simu
latio
n
p
ar
am
eter
s
f
o
r
p
r
o
p
o
s
ed
ML
-
DC
2
A
I
.
NO
P
a
r
t
i
c
u
l
a
r
s
R
a
n
g
e
1
Tr
a
i
n
i
n
g
D
a
t
a
S
i
z
e
6
m
o
n
t
h
s t
o
2
y
e
a
r
s
2
F
e
a
t
u
r
e
S
e
t
Ti
me
o
f
d
a
y
,
t
e
m
p
e
r
a
t
u
r
e
,
h
u
mi
d
i
t
y
,
w
i
n
d
s
p
e
e
d
3
M
o
d
e
l
Ty
p
e
C
N
N
4
H
y
p
e
r
p
a
r
a
me
t
e
r
s
Le
a
r
n
i
n
g
r
a
t
e
:
0
.
0
1
,
La
y
e
r
s
:
5
5
Tr
a
i
n
i
n
g
A
l
g
o
r
i
t
h
m
S
t
o
c
h
a
st
i
c
G
r
a
d
i
e
n
t
D
e
sce
n
t
(
S
G
D
)
6
I
n
p
u
t
V
o
l
t
a
g
e
R
a
n
g
e
1
0
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I
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ab
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5
p
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k
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y
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u
p
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m
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s
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.
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y
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e
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er
g
y
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t
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ab
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6
s
h
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a
s
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by
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
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2252
-
8
9
3
8
I
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tif
I
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tell
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14
,
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Fig
u
r
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.
s
h
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p
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aly
s
is
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s
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with
co
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m
eth
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s
.
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d
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s
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s
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r
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v
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ll sy
s
tem
s
tab
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y
.
T
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le
5
.
Pre
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m
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im
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latio
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ete
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[
4
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N
.
El
sa
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d
,
H
.
M
o
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d
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si
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c
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EEE
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r
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6
.
[
5
]
M
.
S
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l
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m
i
,
F
.
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ma
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d
,
a
n
d
M
.
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3
.
[
6
]
A
.
C
h
u
b
,
D
.
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i
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n
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k
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v
,
R
.
K
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.
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[
7
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B
.
M
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K
a
v
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N
.
S
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mi
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[
8
]
T.
N
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a
n
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t
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[
9
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T.
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1
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.
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1
1
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[
1
2
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,
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.
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[
1
3
]
V
.
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b
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si
,
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.
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m,
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[
1
4
]
Y.
-
F
.
W
a
n
g
,
L.
-
K
.
X
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C
.
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C
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