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to
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Palen
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k
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d
Dietr
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[
1
]
h
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h
lig
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ted
th
at
d
em
an
d
-
s
id
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m
a
n
ag
em
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tellig
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x
p
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in
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Ku
m
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[
2
]
d
em
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at
in
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m
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I
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2252
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8
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Ma
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p
timiz
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fo
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p
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to
vo
lta
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…
(
J
a
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K
a
th
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ve
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)
249
r
eg
u
latio
n
is
v
ital
f
o
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m
ai
n
tain
in
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s
tab
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L
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.
[
3
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s
h
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wed
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s
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a
f
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tab
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Af
s
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.
[
4
]
p
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co
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p
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.
Sh
weth
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m
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[
5
]
em
p
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at
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DC
b
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b
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s
t
co
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v
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Hy
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I
weh
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d
Ak
u
p
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[
6
]
em
p
lo
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p
ar
ticle
s
war
m
o
p
tim
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an
d
d
if
f
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y
b
r
id
s
o
lar
PV
–
h
y
d
r
o
s
y
s
tem
s
f
o
r
o
f
f
-
g
r
i
d
ap
p
li
ca
tio
n
s
,
ac
h
iev
in
g
im
p
r
o
v
e
d
e
n
er
g
y
m
a
n
ag
em
e
n
t
an
d
r
ed
u
ce
d
p
o
wer
lo
s
s
es.
Ku
m
ar
et
a
l.
[
7
]
e
v
alu
ated
a
2
0
k
W
g
r
id
-
c
o
n
n
ec
te
d
PV
in
s
tallatio
n
,
co
n
f
ir
m
in
g
th
at
p
r
o
p
e
r
s
y
s
tem
d
esig
n
e
n
s
u
r
es
s
u
s
tain
ab
le
en
er
g
y
d
eliv
er
y
.
R
ak
ib
et
a
l.
[
8
]
d
e
m
o
n
s
tr
ated
th
at
en
er
g
y
-
s
to
r
ag
e
-
b
ased
h
y
b
r
id
s
y
s
tem
s
p
r
o
v
id
e
s
ea
m
less
r
en
ewa
b
le
i
n
teg
r
atio
n
an
d
s
tr
en
g
th
e
n
g
r
id
s
tab
ilit
y
.
Ak
in
wo
la
an
d
Alk
u
h
ay
li
[
9
]
a
p
p
lied
h
y
b
r
id
PS
O
–
r
ein
f
o
r
ce
m
en
t
lear
n
i
n
g
f
o
r
ad
ap
ti
v
e
v
ir
tu
al
in
e
r
tia
co
n
tr
o
l,
en
h
a
n
cin
g
f
r
eq
u
e
n
cy
s
tab
ilit
y
in
m
u
lti
-
m
icr
o
g
r
id
PV
s
y
s
tem
s
.
Simil
ar
ly
,
Ald
u
laim
i
an
d
Ç
ev
ik
[
1
0
]
im
p
lem
en
ted
A
I
en
h
an
ce
d
MPPT
u
s
in
g
ANFI
S
-
PS
O
o
p
tim
izatio
n
to
im
p
r
o
v
e
g
r
id
-
c
o
n
n
ec
ted
PV sy
s
tem
p
er
f
o
r
m
a
n
ce
.
Vo
ltag
e
s
tab
ilit
y
r
em
ain
s
a
cr
i
tical
ch
allen
g
e
in
PV
in
teg
r
ati
o
n
.
T
h
o
ta
et
a
l
.
[
1
1
]
u
s
ed
I
E
E
E
1
4
an
d
3
0
b
u
s
s
y
s
tem
s
to
an
aly
ze
v
o
l
tag
e
s
tab
ilit
y
u
n
d
e
r
o
p
tim
ized
co
n
tr
o
l,
wh
ile
B
o
u
b
ii
et
a
l
.
[
1
2
]
c
o
m
b
in
e
d
win
d
an
d
s
o
lar
p
o
wer
with
ad
v
an
ce
d
c
o
n
tr
o
l
s
tr
ateg
ies
to
m
ain
tain
g
r
id
r
eliab
ilit
y
[
1
3
]
.
Uswar
m
an
et
a
l.
[
1
4
]
h
ig
h
lig
h
ted
th
at
h
y
b
r
id
e
n
er
g
y
s
to
r
a
g
e
in
DC
m
icr
o
g
r
id
s
s
tab
ilizes
b
u
s
v
o
ltag
e
an
d
en
h
an
ce
s
s
y
s
tem
r
esil
ien
ce
.
Ma
m
o
d
iy
a
et
a
l.
[
1
5
]
a
p
p
lied
AI
-
b
ased
h
y
b
r
id
s
o
lar
en
e
r
g
y
s
y
s
tem
s
with
s
m
ar
t
m
ater
ials
an
d
ad
ap
tiv
e
PVs
,
d
em
o
n
s
tr
atin
g
ef
f
icien
t
p
o
wer
g
en
er
atio
n
an
d
d
y
n
a
m
ic
lo
ad
m
an
a
g
em
en
t.
Al
-
W
ae
li
et
a
l.
[
1
6
]
an
d
Ka
ze
m
et
a
l
.
[
1
7
]
em
p
h
asized
th
e
r
o
le
o
f
n
eu
r
al
n
etwo
r
k
s
an
d
ex
p
e
r
im
en
tal
ev
alu
atio
n
in
p
r
e
d
ictin
g
PV/T
s
y
s
tem
p
er
f
o
r
m
an
ce
u
n
d
e
r
v
ar
iab
le
co
n
d
itio
n
s
.
Ad
v
a
n
ce
d
co
n
tr
o
l
d
ev
ices
an
d
alg
o
r
ith
m
s
f
u
r
th
er
c
o
n
tr
ib
u
te
to
v
o
ltag
e
m
a
n
ag
em
e
n
t.
L
ak
s
h
m
i
[
1
8
]
u
s
ed
a
m
o
d
i
f
ied
f
u
z
zy
lo
g
ic
co
n
tr
o
ller
f
o
r
UPQC
-
in
teg
r
ated
PV
s
y
s
t
em
s
,
wh
ile
Sra
v
an
i
an
d
So
b
h
an
[
1
9
]
ass
ess
ed
PV
p
er
f
o
r
m
an
ce
with
cu
s
to
m
p
o
wer
d
ev
ices
u
n
d
e
r
d
if
f
er
e
n
t
lo
ad
co
n
d
itio
n
s
.
Salam
a
an
d
Vo
k
o
n
y
[
2
0
]
r
ev
iewe
d
v
o
ltag
e
s
tab
ilit
y
in
d
ices,
h
ig
h
lig
h
tin
g
th
e
n
ee
d
f
o
r
ef
f
e
ctiv
e
m
etr
ics
in
g
r
id
-
co
n
n
ec
t
ed
PV
s
y
s
tem
s
.
Geb
r
ea
b
e
et
a
l
.
[
2
1
]
p
r
o
v
id
e
d
a
co
m
p
r
eh
e
n
s
iv
e
r
ev
iew
o
f
p
h
o
to
v
o
ltaic,
th
er
m
al,
an
d
h
y
b
r
id
s
y
s
tem
s
,
o
u
tlin
in
g
s
u
s
tain
ab
l
e
en
er
g
y
s
o
lu
tio
n
s
.
Dh
an
d
ap
an
i
et
a
l
.
[
2
2
]
d
em
o
n
s
tr
ated
th
at
s
o
lar
PV
in
teg
r
atio
n
en
h
a
n
ce
s
v
o
ltag
e
s
tab
ilit
y
in
ac
tiv
e
d
is
tr
ib
u
tio
n
n
etwo
r
k
s
.
Sre
en
iv
asan
et
a
l.
[
2
3
]
lev
er
ag
e
d
m
ac
h
in
e
lear
n
in
g
f
o
r
r
e
n
ewa
b
le
in
teg
r
atio
n
,
an
d
Ab
u
b
a
k
ar
et
a
l
.
[
2
4
]
o
p
tim
ize
d
s
o
lar
an
d
win
d
g
e
n
er
atio
n
u
s
in
g
h
y
b
r
id
d
ee
p
lear
n
i
n
g
ap
p
r
o
ac
h
es.
Fin
ally
,
Z
h
an
g
et
a
l.
[
2
5
]
o
p
tim
ized
p
ass
iv
e
d
am
p
in
g
f
o
r
L
C
L
-
f
ilter
ed
PV
-
s
to
r
ag
e
s
y
s
tem
s
,
en
s
u
r
in
g
im
p
r
o
v
ed
d
y
n
am
ic
r
esp
o
n
s
e
a
n
d
r
ed
u
ce
d
v
o
ltag
e
o
s
cillatio
n
s
.
2.
M
E
T
H
O
D
T
h
is
s
tu
d
y
in
tr
o
d
u
ce
s
a
m
ac
h
i
n
e
lear
n
in
g
-
b
ased
r
ea
l
-
tim
e
p
o
wer
s
tab
ilit
y
o
p
tim
izatio
n
m
o
d
el
f
o
r
PV
s
y
s
tem
s
,
in
teg
r
atin
g
h
y
b
r
id
in
d
u
cto
r
–
ca
p
ac
ito
r
(
L
C
)
p
atter
n
s
with
in
tellig
en
t
cir
c
u
it
s
witch
in
g
.
T
h
e
p
r
o
p
o
s
ed
d
esig
n
co
m
b
in
es a
p
r
ed
ictiv
e
co
n
tr
o
l la
y
er
with
a
h
ar
d
war
e
s
witch
in
g
f
r
am
ewo
r
k
to
m
ain
t
ain
v
o
ltag
e
s
tab
ilit
y
an
d
im
p
r
o
v
e
th
e
p
o
wer
f
ac
to
r
u
n
d
er
d
y
n
am
ic
e
n
v
ir
o
n
m
en
ta
l
co
n
d
itio
n
s
.
T
h
e
h
a
r
d
war
e
co
n
s
is
ts
o
f
K
p
ar
allel
L
C
b
r
an
ch
es,
ea
ch
co
m
p
r
is
in
g
an
in
d
u
cto
r
,
ca
p
ac
ito
r
,
a
n
d
MO
SF
E
T
.
At
ea
ch
co
n
tr
o
l
in
t
er
v
al,
o
n
ly
a
s
u
b
s
et
o
f
th
ese
b
r
a
n
ch
es
is
ac
tiv
ated
f
o
r
c
h
ar
g
i
n
g
o
r
d
is
ch
ar
g
i
n
g
b
ased
o
n
th
e
p
r
e
d
icted
im
p
ac
t
o
n
s
y
s
tem
s
tab
ilit
y
.
T
h
e
s
elec
tio
n
p
r
o
ce
s
s
is
d
r
iv
e
n
b
y
a
tr
ai
n
ed
m
ac
h
in
e
lear
n
i
n
g
(
ML
)
m
o
d
el
th
at
ev
alu
ates
r
ea
l
-
tim
e
PV
d
ata
an
d
h
is
to
r
ical
p
er
f
o
r
m
a
n
ce
r
ec
o
r
d
s
to
d
eter
m
in
e
th
e
o
p
tim
al
ac
tiv
atio
n
p
atter
n
.
2
.
1
.
M
a
chine le
a
rning
m
o
de
l
A
m
u
lti
-
lay
er
p
er
ce
p
tr
o
n
(
M
L
P)
n
eu
r
al
n
etwo
r
k
is
em
p
lo
y
ed
d
u
e
to
its
ab
ilit
y
to
h
an
d
l
e
n
o
n
lin
ea
r
s
y
s
tem
b
eh
av
io
u
r
,
f
ast in
f
er
e
n
ce
s
p
ee
d
,
an
d
s
u
itab
ilit
y
f
o
r
r
e
g
r
ess
io
n
-
b
ased
p
r
e
d
ictio
n
s
.
Mo
d
el
Sp
ec
if
icatio
n
s
:
i)
I
n
p
u
ts
:
PV
in
p
u
t
v
o
ltag
e
(
Vi
n
)
,
PV
in
p
u
t
cu
r
r
e
n
t
(
in
)
,
s
o
l
ar
ir
r
ad
ian
ce
,
am
b
ien
t
tem
p
er
atu
r
e,
in
d
iv
id
u
al
ca
p
ac
ito
r
v
o
ltag
es,
in
d
u
cto
r
cu
r
r
en
ts
,
an
d
t
h
e
p
r
e
v
io
u
s
ac
tiv
a
tio
n
p
atter
n
.
ii)
Ar
ch
itectu
r
e:
t
h
r
ee
h
i
d
d
en
lay
er
s
(
6
4
,
3
2
,
a
n
d
1
6
n
e
u
r
o
n
s
)
w
ith
R
eL
U
ac
tiv
atio
n
f
u
n
ctio
n
s
.
iii)
Ou
tp
u
ts
:
f
o
r
ea
ch
ca
n
d
id
ate
L
C
p
atter
n
,
th
e
m
o
d
el
p
r
e
d
icts
:
-
Po
wer
f
lu
ctu
atio
n
Δ
Pp
r
ed
)
-
Vo
ltag
e
s
tab
ilit
y
in
d
ex
(
VSI
p
r
ed
)
-
Po
wer
lo
s
s
(
Plo
s
s
,
p
r
ed
)
iv
)
L
o
s
s
f
u
n
ctio
n
:
m
ea
n
s
q
u
ar
e
d
e
r
r
o
r
(
MSE
)
b
etwe
en
p
r
ed
icted
an
d
m
ea
s
u
r
e
d
v
alu
es.
v)
Op
tim
izer
:
Ad
am
with
a
lear
n
i
n
g
r
ate
o
f
0
.
0
0
1
.
T
r
ain
in
g
d
ata
:
r
ec
o
r
d
e
d
PV
o
p
er
atio
n
al
d
ata
u
n
d
er
d
iv
e
r
s
e
ir
r
ad
ian
ce
a
n
d
lo
ad
co
n
d
itio
n
s
,
au
g
m
en
te
d
to
im
p
r
o
v
e
g
en
er
alis
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
248
-
2
5
6
250
v
i)
L
C
p
atter
n
g
en
er
atio
n
:
At
ev
er
y
co
n
tr
o
l
s
tep
,
th
e
s
y
s
tem
g
en
er
ates
a
s
et
o
f
f
ea
s
i
b
le
b
in
ar
y
ac
tiv
atio
n
p
atter
n
s
f
o
r
th
e
K
L
C
cir
cu
its
:
-
‘
1
’
in
d
icate
s
an
ac
tiv
e
b
r
a
n
ch
.
-
‘
0
’
in
d
icate
s
an
i
d
le
b
r
a
n
ch
.
Fo
r
ex
am
p
le,
with
t
h
r
ee
L
C
b
r
an
ch
es,
th
e
p
o
s
s
ib
le
p
atter
n
s
in
clu
d
e:
[
0
,
0
,
1
]
,
[
1
,
1
,
0
]
,
[
0
,
1
,
1
]
,
[
1
,
1
,
1
………
Patter
n
s
lead
in
g
to
ex
ce
s
s
iv
e
s
witch
in
g
lo
s
s
o
r
co
m
p
o
n
e
n
t o
v
er
lo
ad
in
g
ar
e
e
x
clu
d
ed
f
r
o
m
ev
alu
atio
n
.
v
ii)
Patter
n
ev
alu
atio
n
an
d
s
elec
tio
n
:
E
ac
h
g
en
e
r
ated
p
atter
n
u
n
d
er
g
o
es e
v
alu
atio
n
th
r
o
u
g
h
th
e
tr
ai
n
ed
ML
P m
o
d
el:
-
Featu
r
e
v
ec
to
r
f
o
r
m
atio
n
:
c
o
m
b
in
e
cu
r
r
en
t PV
m
ea
s
u
r
em
en
t
s
an
d
L
C
s
tates in
to
a
s
in
g
le
-
f
ea
tu
r
e
v
ec
to
r
f
o
r
ea
ch
ca
n
d
id
ate
p
atter
n
.
-
ML
p
r
ed
ictio
n
:
E
s
tim
ate
Δ
Pp
r
ed
,
VSI
p
r
ed
,
an
d
Plo
s
s
,
p
r
e
d
.
-
Sco
r
in
g
:
c
o
m
p
u
te
a
weig
h
te
d
s
co
r
e:
Sco
r
e
(
Pk
)
=w
1
⋅
VSI
p
r
ed
−w
2
⋅
Δ
Pp
r
ed
−w
3
⋅
Plo
s
s
,
p
r
ed
wh
er
e
w1
,
w2
,
w3
ar
e
tu
n
in
g
co
ef
f
icien
ts
p
r
io
r
itis
in
g
s
tab
ili
ty
,
lo
w
f
lu
ctu
atio
n
,
an
d
m
in
im
al
lo
s
s
.
-
Selectio
n
:
c
h
o
o
s
e
th
e
p
atter
n
with
th
e
h
ig
h
est s
co
r
e
f
o
r
im
m
ed
iate
ac
tiv
atio
n
.
-
Switch
in
g
:
a
ctiv
ate
s
elec
ted
b
r
an
ch
es f
o
r
r
eg
u
latio
n
; a
s
s
ig
n
n
o
n
-
s
elec
ted
b
r
a
n
ch
es to
ch
a
r
g
e
m
o
d
e.
v
iii)
R
ea
l
-
tim
e
co
n
tr
o
l p
r
o
ce
s
s
:
E
ac
h
co
n
tr
o
l c
y
cle
(
~
1
5
0
m
s
)
ex
ec
u
tes th
e
f
o
llo
win
g
s
eq
u
en
ce
:
-
Acq
u
ir
e
r
ea
l
-
tim
e
PV a
n
d
L
C
m
ea
s
u
r
em
en
ts
.
-
Gen
er
ate
all
v
alid
L
C
ac
tiv
atio
n
p
atter
n
s
.
-
Pre
d
ict
s
tab
ilit
y
an
d
lo
s
s
f
o
r
e
ac
h
p
atter
n
u
s
in
g
th
e
ML
P.
-
Select
th
e
h
ig
h
est
-
s
co
r
in
g
p
atter
n
.
-
T
r
ig
g
er
th
e
co
r
r
esp
o
n
d
i
n
g
M
OSFET
s
f
o
r
ac
tiv
atio
n
.
-
Sto
r
e
th
e
r
esu
lts
in
th
e
h
is
to
r
ic
al
d
atab
ase
f
o
r
f
u
tu
r
e
m
o
d
el
r
e
tr
ain
in
g
ix
)
Key
ad
v
an
ta
g
es
:
-
R
ap
id
d
ec
is
io
n
-
m
ak
i
n
g
:
C
o
m
p
letes
ea
ch
o
p
tim
is
atio
n
c
y
cl
e
in
~1
5
0
m
s
,
f
aster
th
an
f
u
z
zy
lo
g
ic
(
3
0
0
m
s
)
an
d
PID
(
4
5
0
m
s
)
c
o
n
tr
o
ller
s
.
-
Pro
ac
tiv
e
s
tab
ilit
y
m
an
ag
em
e
n
t
:
p
r
ed
icts
an
d
p
r
ev
e
n
ts
in
s
tab
ilit
y
b
ef
o
r
e
it o
cc
u
r
s
.
-
L
o
s
s
r
ed
u
ctio
n
:
a
v
o
id
s
u
n
n
ec
e
s
s
ar
y
ac
tiv
atio
n
o
f
id
le
o
r
s
atu
r
ated
co
m
p
o
n
en
ts
.
-
Hig
h
r
eliab
ilit
y
:
m
ain
tain
s
VSI
>
0
.
9
5
with
p
o
wer
f
lu
ctu
atio
n
s
lim
ited
to
~5
%.
2
.
2
.
H
a
rdwa
re
c
o
nfig
ura
t
i
o
n
i)
PV
s
o
u
r
ce
:
5
k
W
r
o
o
f
to
p
s
o
lar
ar
r
ay
,
p
o
ly
cr
y
s
tallin
e
m
o
d
u
les,
STC
ef
f
icien
cy
:
1
8
.
5
%,
Vo
c:
3
8
.
2
V/m
o
d
u
le
.
ii)
Po
wer
co
n
d
itio
n
in
g
u
n
it
:
b
o
o
s
t
co
n
v
er
ter
with
MO
SF
E
T
s
witch
in
g
(
I
R
FP
4
6
0
)
,
s
witch
in
g
f
r
eq
u
e
n
cy
:
2
0
k
Hz
.
iii)
I
n
d
u
cto
r
–
ca
p
ac
ito
r
(
L
C
)
n
etw
o
r
k
:
-
6
p
a
r
allel
cir
cu
its
,
ea
c
h
co
n
tai
n
in
g
1
i
n
d
u
cto
r
(
L
=
4
.
7
m
H,
co
p
p
er
co
r
e
)
a
n
d
1
ca
p
ac
ito
r
(
C
=
4
7
0
µF,
elec
tr
o
ly
tic,
4
5
0
V
r
atin
g
)
.
-
R
ated
v
o
ltag
e:
4
0
0
V,
co
n
tin
u
o
u
s
cu
r
r
e
n
t: 1
5
A
p
er
b
r
an
ch
.
iv
)
Me
asu
r
em
en
t
d
ev
ices
: H
all
-
ef
f
ec
t v
o
ltag
e
an
d
cu
r
r
en
t sen
s
o
r
s
(
±
0
.
5
% a
cc
u
r
ac
y
)
.
v)
C
o
n
tr
o
l
p
latf
o
r
m
:
T
I
T
MS3
2
0
F2
8
3
7
9
D
DSP,
2
0
0
MH
z,
1
6
-
b
it AD
C
s
am
p
lin
g
at
5
0
k
Hz
.
v
i)
Data
ac
q
u
is
itio
n
:
i
r
r
ad
ian
ce
s
e
n
s
o
r
(
±
5
W
/m
²)
,
am
b
ien
t te
m
p
er
atu
r
e
s
en
s
o
r
(
±
0
.
2
°C
)
.
2
.
3
.
So
f
t
wa
re
a
nd
a
lg
o
rit
hm
ic
s
et
up
i)
Pro
g
r
am
m
in
g
en
v
ir
o
n
m
e
n
t
: M
AT
L
AB
/Si
m
u
lin
k
R
2
0
2
4
a
with
em
b
ed
d
ed
c
o
d
er
f
o
r
DSP d
e
p
lo
y
m
en
t
ii)
Ma
ch
in
e
lear
n
in
g
m
o
d
el
:
m
u
lt
i
-
lay
er
p
er
ce
p
tr
o
n
(
ML
P)
r
eg
r
ess
io
n
m
o
d
el
-
I
n
p
u
t f
ea
tu
r
es:
-
PV v
o
ltag
e
(
Vin
)
,
PV c
u
r
r
en
t
(
I
in
)
-
I
r
r
ad
ian
ce
(
G)
,
T
em
p
er
atu
r
e
(
T
)
-
C
ap
ac
ito
r
v
o
ltag
es (
Vc₁
.
.
.
Vc₆
)
an
d
I
n
d
u
cto
r
cu
r
r
en
ts
(
I
L
₁.
.
.
I
L
₆)
-
B
in
ar
y
L
C
ac
tiv
atio
n
p
atter
n
-
Hid
d
en
lay
er
s
: 3
f
u
lly
co
n
n
ec
t
ed
lay
er
s
(
6
4
–
1
2
8
–
6
4
n
e
u
r
o
n
s
)
with
R
eL
U
ac
tiv
atio
n
-
Ou
tp
u
t
v
a
r
iab
les:
p
r
ed
icted
p
o
wer
f
lu
ctu
atio
n
(
Δ
P_
p
r
e
d
)
,
Vo
ltag
e
Stab
ilit
y
I
n
d
ex
(
VSI
_
p
r
e
d
)
,
Pre
d
icted
p
o
wer
l
o
s
s
(
Plo
s
s
_
p
r
ed
)
-
T
r
ain
in
g
m
et
h
o
d
: A
d
a
m
o
p
tim
izer
,
lear
n
in
g
r
ate
0
.
0
0
1
,
b
atch
s
ize
6
4
,
2
0
0
ep
o
ch
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Ma
ch
in
e
lea
r
n
in
g
-
b
a
s
ed
r
ea
l
-
t
ime
p
o
w
er st
a
b
ilit
y
o
p
timiz
a
ti
o
n
fo
r
p
h
o
to
vo
lta
ic
…
(
J
a
ya
s
h
r
ee
K
a
th
ir
ve
l
)
251
-
L
o
s
s
f
u
n
ctio
n
:
m
ea
n
s
q
u
ar
e
d
e
r
r
o
r
(
MSE
)
-
T
r
ain
in
g
d
ataset:
3
0
,
0
0
0
r
ea
l
-
tim
e
r
ec
o
r
d
ed
o
p
er
atio
n
al
cy
cles
(
7
0
%
tr
ain
in
g
,
1
5
%
v
ali
d
atio
n
,
1
5
%
test
in
g
)
iii)
Op
tim
izatio
n
en
g
in
e:
g
en
etic
alg
o
r
ith
m
(
GA)
f
o
r
L
C
p
atter
n
s
elec
tio
n
-
Po
p
u
latio
n
s
ize:
2
0
p
atter
n
s
p
er
cy
cle
-
Selectio
n
:
t
o
u
r
n
a
m
en
t selec
tio
n
(
s
ize
=
3
)
-
C
r
o
s
s
o
v
er
:
s
in
g
le
-
p
o
in
t,
r
ate
=
0
.
8
-
Mu
tatio
n
:
b
it
-
f
lip
,
r
ate
=
0
.
0
5
-
Fit
n
ess
f
u
n
ctio
n
:
Sco
r
e
=
w1
×
VSI
p
r
ed
−
w2
×
Δ
Pp
r
ed
−
w3
×
Plo
s
s
_
p
r
ed
,
wh
er
e
w1
=0
.
5
,
w2
=0
.
3
,
w3
=
0
.
2
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
s
tu
d
y
“
MLP
with
GA
-
b
ased
p
atter
n
s
elec
tio
n
r
ea
l
-
tim
e
p
o
wer
s
tab
ilit
y
o
p
tim
is
atio
n
f
o
r
p
h
o
to
v
o
ltaic
s
y
s
tem
s
u
s
in
g
h
y
b
r
id
in
d
u
cto
r
-
ca
p
ac
ito
r
p
att
er
n
s
”
aim
s
to
in
cr
ea
s
e
5
k
W
r
o
o
f
t
o
p
PV
ar
r
a
y
s
y
s
tem
s
'
ef
f
icien
cy
an
d
s
tab
ilit
y
.
T
h
e
e
x
p
er
im
e
n
tal
s
etu
p
co
n
s
is
ted
o
f
s
ix
p
a
r
allel
L
C
b
r
an
ch
es
(
4
.
7
m
H
in
d
u
cto
r
s
,
4
7
0
µF
ca
p
ac
ito
r
s
,
4
5
0
V
r
atin
g
)
co
n
n
ec
ted
v
ia
MO
SF
E
T
s
witch
es
co
n
tr
o
lled
b
y
th
e
DSP
-
b
ased
s
y
s
tem
.
T
h
e
s
y
s
tem
ca
n
an
ticip
ate
an
d
o
p
tim
i
z
e
elec
tr
ic
ity
o
u
tp
u
t
in
r
ea
l
tim
e,
a
d
ju
s
tin
g
to
ch
an
g
in
g
en
v
ir
o
n
m
en
tal
c
o
n
d
itio
n
s
,
b
y
u
tili
zin
g
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
.
B
y
r
ed
u
cin
g
f
lu
ctu
atio
n
s
,
th
e
in
co
r
p
o
r
atio
n
o
f
h
y
b
r
i
d
in
d
u
ct
o
r
-
ca
p
ac
ito
r
cir
cu
its
im
p
r
o
v
es
p
o
wer
s
tab
ilit
y
ev
e
n
m
o
r
e.
T
h
e
o
u
tco
m
es
s
h
o
w
n
o
tab
le
g
ain
s
in
en
er
g
y
ef
f
ici
en
cy
,
d
ec
r
ea
s
ed
s
y
s
tem
in
s
tab
ilit
y
,
an
d
im
p
r
o
v
ed
p
o
wer
q
u
ality
,
g
u
ar
an
teein
g
th
at
PV sy
s
tem
s
m
ax
im
is
e
en
er
g
y
y
ield
wh
ile
o
p
e
r
atin
g
at
th
eir
b
est u
n
d
er
d
y
n
am
ic
s
itu
atio
n
s
.
3
.
1
.
F
luct
ua
t
i
o
n
a
na
ly
s
is
o
f
po
wer
Fig
u
r
e
1
d
em
o
n
s
tr
ates
ML
-
b
a
s
ed
h
y
b
r
id
L
C
s
y
s
tem
r
ed
u
ce
s
p
o
wer
f
lu
ctu
atio
n
s
to
5
%,
co
m
p
ar
ed
to
1
2
%
in
co
n
v
en
tio
n
al
s
y
s
tem
s
a
5
8
%
im
p
r
o
v
em
en
t.
T
h
is
is
ac
h
iev
ed
th
r
o
u
g
h
ad
a
p
tiv
e
co
n
tr
o
l
o
f
L
C
p
atter
n
s
,
en
ab
lin
g
r
ea
l
-
tim
e
d
is
tu
r
b
a
n
ce
co
r
r
ec
tio
n
an
d
s
tab
le
p
o
wer
o
u
tp
u
t.
C
ir
cu
it
ac
tiv
atio
n
d
ec
is
io
n
s
ar
e
o
p
tim
ized
u
s
in
g
weig
h
ted
s
co
r
es
(
0
.
5
:0
.
3
:0
.
2
f
o
r
s
tab
ilit
y
,
f
lu
ct
u
atio
n
m
in
im
izatio
n
,
an
d
lo
s
s
r
ed
u
ctio
n
)
.
T
h
e
s
y
s
tem
m
ain
tain
s
a
v
o
ltag
e
s
tab
ilit
y
in
d
ex
(
VSI
)
a
b
o
v
e
0
.
9
5
,
o
u
tp
er
f
o
r
m
in
g
tr
a
d
itio
n
al
s
y
s
tem
s
th
at
v
ar
y
b
etwe
en
0
.
8
8
a
n
d
0
.
9
3
.
Hy
b
r
id
L
C
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ates
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.
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253
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4
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ML
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o
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el
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r
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p
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v
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ac
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tab
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N
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4.
CO
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h
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DATA AV
AI
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Data
a
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RE
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NC
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[
1
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P
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