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1.
I
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Ov
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tan
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[
1
-
3
]
.
Ov
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[
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.
Dif
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-
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Hill
C
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HC
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[
1
1
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in
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(
I
NC
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[
13
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1
4
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1
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[
18
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19
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an
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[
2
0
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21]
.
T
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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10
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5084
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w
t
h
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iag
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th
e
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g
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e
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te
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t
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n
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o
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t c
h
o
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NN
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P
T
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u
r
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iag
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a
m
o
f
p
r
o
p
o
s
ed
P
V
ar
r
ay
2.
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AT
H
E
M
AT
I
CAL
M
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DE
L
I
N
G
O
F
T
H
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P
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A
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lar
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ll
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te
d
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y
a
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r
r
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o
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r
ce
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p
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io
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k
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n
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ar
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it
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d
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ter
n
a
l r
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ta
n
ce
s
R
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n
d
R
s
h
,
[
22
]
as r
ep
r
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t in
Fig
u
r
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2.
Fig
u
r
e
2
.
Ma
th
e
m
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h
e
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asic e
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u
atio
n
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e
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o
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e:
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1
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ph
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r
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N:
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s
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F
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3
a
n
d
t
h
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Vs
v
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e
ch
ar
ac
ter
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s
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s
h
o
w
n
i
n
F
i
g
u
r
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4
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1
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ar
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o
f
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el
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a
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a
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M
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m
p
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i
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i
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8
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3
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Fig
u
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4
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I
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:
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3.
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s
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n
F
i
g
u
r
e
5
.
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h
e
co
n
tr
o
l
m
eth
o
d
o
l
o
g
y
in
th
is
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n
v
er
ter
ap
p
l
y
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y
co
n
tr
o
l
th
e
d
u
t
y
-
c
y
cle
(
D)
o
f
th
e
p
o
w
er
tr
a
n
s
i
s
to
r
,
th
is
lead
to
ch
an
g
i
n
g
th
e
lo
ad
v
o
ltag
e
[
23
,
24
]
.
W
h
en
th
e
p
o
w
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tr
an
s
is
to
r
is
t
u
r
n
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h
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co
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s
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t
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r
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o
f
f
t
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g
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e
i
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th
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co
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w
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s
th
e
s
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r
ce
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is
co
n
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ted
to
th
e
lo
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th
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u
g
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th
e
d
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lead
to
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e
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ac
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r
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w
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Vin
=
1
1
−
D
(7
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Fig
u
r
e
5
.
DC
ch
o
p
p
er
4.
M
AXI
M
U
M
P
O
WE
R
P
O
I
NT
T
RA
CK
I
N
G
(
M
P
P
T
)
As
w
as
ea
r
lier
clar
i
f
ied
,
MP
PT
p
r
o
ce
d
u
r
es
ar
e
r
eq
u
ir
ed
in
p
h
o
to
v
o
ltaic
u
s
es
,
in
f
ac
t
t
h
at
th
e
m
a
x
i
m
u
m
p
o
w
er
o
f
P
V
ce
ll
v
ar
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n
s
w
it
h
I
n
te
n
s
i
t
y
o
f
s
u
n
r
ad
iatio
n
an
d
te
m
p
er
at
u
r
e
s
o
th
at,
th
e
u
s
e
o
f
MP
PT
s
y
s
t
e
m
s
is
e
x
ce
p
tio
n
all
y
e
s
s
e
n
tial
to
attai
n
th
e
g
r
ea
test
p
o
w
er
f
r
o
m
a
s
u
n
o
r
ien
ted
ar
r
a
y
[
25
].
T
h
e
s
i
m
p
le
s
ch
e
m
e
o
f
MP
P
T
co
n
tr
o
l
is
to
d
is
co
v
er
v
o
ltag
e
an
d
cu
r
r
en
t
r
ef
er
e
n
ce
at
m
ax
i
m
u
m
p
o
w
er
u
n
d
er
v
ar
io
u
s
s
tates
o
f
s
u
n
ill
u
m
i
n
at
io
n
an
d
te
m
p
er
atu
r
e
b
y
alter
in
g
th
e
esti
m
at
io
n
o
f
lo
ad
R
.
Fig
u
r
e
6
d
em
o
n
s
tr
a
tes
th
e
(
I
-
V)
an
d
(
P
-
V)
cu
r
v
e
s
.
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er
atio
n
al
p
o
in
t
(
OP
R
1
)
is
th
e
g
r
ea
t
est
p
o
w
er
p
o
in
t
(
MPP
)
esteem
i
n
th
e
co
n
d
itio
n
ir
r
ad
iatio
n
(
G
1
)
,
te
m
p
er
atu
r
e
(
T
1
)
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d
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d
(R
1
)
.
I
f
th
e
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m
i
n
atio
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ar
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m
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1
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2
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te
m
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er
atu
r
e
v
ar
y
f
r
o
m
T
1
to
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2
,
th
e
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-
V
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en
d
m
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v
e
f
r
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m
t
h
e
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en
d
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1
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1
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e
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en
d
(
G
2
,
T
2
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.
L
o
ad
s
tate
m
u
s
t
b
e
v
ar
ied
f
r
o
m
R
1
to
R
2
to
o
b
tain
MPP
at
(
O
P
R
2
)
[
25
].
Ma
n
y
al
g
o
r
ith
m
s
li
k
e
(
P
&
O)
,
(
I
NC
)
,
in
tellig
e
n
t
tech
n
iq
u
e
lik
e
(
F
L
)
an
d
(
NN)
ca
n
b
e
em
p
lo
y
ed
to
ac
co
m
p
l
is
h
t
h
e
au
to
m
a
tic
tr
ac
in
g
.
W
e
g
en
er
all
y
f
o
cu
s
o
n
th
e
P
&
O
a
n
d
NN
tech
n
iq
u
e
s
.
Fig
u
r
e
6
.
MP
PT
cu
r
v
e
5.
M
P
P
T
B
Y
P
E
RT
URB
& O
B
SE
RV
E
(
P
&O
)
M
E
T
H
O
D
T
h
e
im
p
o
r
tan
t
p
ar
t
o
f
a
P
V
ar
r
an
g
e
m
e
n
t
is
to
Fi
n
d
th
e
m
ax
i
m
u
m
p
o
w
er
(
MP
P)
o
f
s
o
lar
ce
ll,
b
ec
au
s
e
o
f
n
o
n
li
n
ea
r
f
ea
tu
r
e
s
a
n
d
s
m
all
ef
f
icie
n
c
y
o
f
p
h
o
to
v
o
lta
ic
g
r
o
u
p
s
.
T
h
e
P
&
O
m
et
h
o
d
i
s
o
n
e
o
f
t
h
e
b
est
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u
lar
l
y
u
s
ed
MP
P
T
ap
p
r
o
a
ch
es
f
o
r
it
s
ea
s
i
n
e
s
s
a
n
d
s
i
m
p
licit
y
o
f
ex
ec
u
tio
n
[
7
,
8]
.
I
n
t
h
is
tec
h
n
iq
u
e,
th
e
v
o
lta
g
e
s
et
i
s
a
s
li
g
h
t
d
is
t
u
r
b
ed
(
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e
o
r
f
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an
d
th
e
r
ea
l
r
ate
o
f
th
e
p
o
w
er
P
(
k
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m
p
ar
e
d
to
th
e
p
r
io
r
attain
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al
u
e
P
(
k
-
1
)
.
T
h
e
f
lo
w
c
h
ar
t o
f
t
h
e
P
&
O
m
o
d
e
is
d
i
s
p
la
y
ed
in
F
ig
u
r
e
7.
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
-
8708
C
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mp
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y
s
t
a
t
e
e
r
r
o
r
w
h
er
e
t
h
e
P&
O
c
au
s
e
o
s
c
i
ll
a
t
i
o
n
a
r
o
u
n
d
th
e
M
PP
.
T
h
e
c
o
m
m
u
n
al
d
if
f
i
cu
l
t
in
P&
O
t
e
ch
n
i
q
u
e
i
s
th
e
a
r
r
an
g
em
en
t
v
o
l
t
a
g
e
d
is
tu
r
b
e
d
e
a
c
h
M
PP
T
d
u
r
a
t
i
o
n
.
On
c
e
th
e
M
PP
i
s
o
b
t
a
i
n
e
d
,
t
h
e
o
u
t
p
u
t
en
e
r
g
y
f
lu
ctu
a
t
es
a
r
o
u
n
d
th
e
w
av
e
,
l
e
a
d
in
g
t
o
a
l
o
s
s
o
f
p
o
w
e
r
in
th
e
p
h
o
t
o
e
le
c
t
r
i
c
s
y
s
t
em
.
T
h
is
is
p
a
r
t
i
cu
l
a
r
ly
a
c
cu
r
a
t
e
in
f
ix
e
d
o
r
s
l
o
w
-
ch
an
g
in
g
a
tm
o
s
p
h
e
r
e
s
.
Fig
u
r
e
7
.
C
lass
ical
P
&
O
s
c
h
e
m
e
f
lo
w
c
h
ar
t
6.
ARTI
F
I
CI
AL
N
E
URA
L
NE
WO
RK
S
A
r
ti
f
icial
Ne
u
r
al
Net
w
o
r
k
(
ANN)
ar
e
u
s
u
al
l
y
r
ec
o
g
n
ized
a
s
a
to
o
ls
p
r
o
p
o
s
in
g
a
n
u
n
u
s
u
a
l
w
a
y
to
r
eso
lv
e
m
u
lti
f
ar
io
u
s
p
r
o
b
lem
s
.
A
N
N
is
an
e
x
ac
t
m
o
d
el
t
h
at
atte
m
p
ts
to
p
r
eten
d
to
b
u
ild
an
d
f
u
n
ctio
n
o
r
g
a
n
ic
n
eu
r
al
n
et
w
o
r
k
s
.
A
NN
is
a
d
ata
tr
ea
tin
g
s
c
h
e
m
e.
I
t
co
n
t
ain
s
a
n
u
m
b
er
o
f
s
i
m
p
le
e
x
c
ee
d
in
g
l
y
co
n
s
is
ten
t
p
r
o
ce
s
s
o
r
s
(
ele
m
e
n
ts
)
r
ec
o
g
n
i
ze
d
as n
e
u
r
o
n
s
.
T
h
ese
n
eu
r
o
n
s
ar
e
co
n
n
ec
ted
to
ea
ch
o
th
er
b
y
a
h
u
g
e
n
u
m
b
er
o
f
w
ei
g
h
ts
l
in
k
s
to
b
e
a
n
et
w
o
r
k
.
S
u
ch
n
et
w
o
r
k
s
h
av
e
e
x
tr
a
o
r
d
in
ar
y
e
x
a
m
p
le
ac
k
n
o
w
led
g
m
e
n
t
a
n
d
lear
n
i
n
g
ca
p
ac
ities
.
L
ate
u
s
es
o
f
A
NN
h
av
e
d
e
m
o
n
s
tr
ated
th
at
t
h
e
y
ta
k
e
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itical
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o
ten
tial
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n
q
u
er
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g
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h
e
h
ar
d
d
u
tie
s
o
f
d
ata
ta
k
i
n
g
ca
r
e
o
f
a
n
d
cla
r
if
icatio
n
.
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ltil
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ed
f
ee
d
f
o
r
w
ar
d
(
b
ac
k
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r
o
p
ag
atio
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B
P
N)
ANN
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s
t
h
e
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est
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er
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y
h
eld
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o
r
t
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ti
lized
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y
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ev
er
al
r
eq
u
es
ts
.
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t
in
v
o
l
v
e
s
an
in
p
u
t
la
y
er
,
o
n
e
o
r
f
u
r
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h
er
h
id
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en
s
l
a
y
er
s
,
an
d
a
tar
g
et
la
y
er
[
1
,
2
0
]
.
7.
M
P
P
T
B
Y
USI
NG
P
RO
P
O
S
E
D
NE
URA
L
NE
T
WO
RK
I
n
o
r
d
er
to
u
s
e
A
NN
as M
P
P
T
co
n
tr
o
lle
r
f
o
r
P
V
s
y
s
te
m
,
w
e
f
o
llo
w
th
e
f
o
llo
w
in
g
s
tep
s
:
-
Step
1
:
C
h
o
o
s
i
n
g
t
h
e
s
tr
u
ct
u
r
e
o
f
t
h
e
p
r
o
p
o
s
ed
NN
:
i
n
th
i
s
r
esear
ch
t
h
e
p
r
o
p
o
s
ed
n
et
w
o
r
k
c
h
o
s
en
h
av
e
th
r
ee
la
y
er
s
,
t
w
o
v
r
iab
les
(
ir
r
ad
ian
ce
G
an
d
te
m
p
er
atu
r
e
T
)
ar
e
u
s
ed
as
i
n
p
u
t
s
i
n
s
tead
o
f
v
o
lta
g
e
a
n
d
cu
r
r
en
t
th
at
u
s
ed
i
n
P
&
O
m
e
th
o
d
,
ten
n
e
u
r
o
n
s
i
n
h
id
d
en
la
y
er
w
it
h
lo
g
s
ig
m
o
id
ac
ti
v
atio
n
f
u
n
cti
n
an
d
th
e
(
Mo
d
u
latio
n
s
ig
n
al
M)
is
u
s
ed
as
o
u
tp
u
t
f
o
r
NN.
T
h
e
p
r
o
p
o
s
ed
A
N
N
ar
ch
itect
u
r
e
is
s
h
o
w
n
in
F
ig
u
r
e
8
.
-
Step
2
:
T
r
ain
in
g
th
e
n
eu
r
al
n
e
t
w
o
r
k
:
T
o
tr
ain
th
e
n
eu
r
al
n
e
t
w
o
r
k
it
is
r
eq
u
ir
ed
to
o
b
tain
e
x
a
m
p
le
p
atter
n
as
in
p
u
t
an
d
tar
g
et.
P
V
s
y
s
te
m
w
it
h
P
&
O
is
d
esi
g
n
ed
an
d
s
i
m
u
lated
i
n
o
r
d
er
t
o
o
b
tain
tr
ain
i
n
g
d
ata
to
tr
ain
t
h
e
NN
as
w
ell
a
s
to
co
m
p
ar
e
th
e
r
es
u
lts
.
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lar
g
e
n
u
m
b
er
o
f
ex
a
m
p
le
p
atter
n
ar
e
ta
k
e
n
f
o
r
d
if
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er
en
t
co
n
d
itio
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o
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te
m
p
er
at
u
r
e
an
d
illu
m
in
at
i
o
n
f
r
o
m
t
h
e
s
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m
u
lated
s
y
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te
m
u
n
d
er
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&
O
co
n
tr
o
ller
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o
f
f
li
n
e
tr
ain
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n
g
b
y
er
r
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r
b
ac
k
p
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g
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m
et
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o
d
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g
(
L
e
v
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n
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er
g
Ma
r
q
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ar
d
t
L
M)
w
a
y
i
s
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s
ed
to
tr
ain
th
e
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NN
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n
m
atlab
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y
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co
m
m
a
n
d
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i
n
ce
th
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o
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ith
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m
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e
to
s
o
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o
n
-
li
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r
p
r
o
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lem
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ell
a
s
m
o
r
e
r
o
b
u
s
t t
h
an
o
t
h
er
tech
n
iq
u
e.
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Fig
u
r
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9
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Fig
u
r
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P
r
o
p
o
s
ed
A
NN
ar
ch
i
tectu
r
e
Fig
u
r
e
9
.
MP
PT
b
y
p
r
o
p
o
s
ed
NN
8.
SI
M
UL
AT
I
O
N
R
E
S
UL
T
S
T
o
v
alid
ate
th
e
s
tu
d
y
s
tated
in
ea
r
lier
u
n
it
s
,
a
s
tan
d
alo
n
e
P
V
s
ch
e
m
e
j
o
in
ed
to
a
d
c
b
o
o
s
t
co
n
v
er
ter
an
d
s
i
m
u
lated
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s
in
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M
A
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A
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NK.
T
w
o
MP
PT
s
tr
ateg
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s
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e
d
esig
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ith
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r
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ig
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r
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n
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r
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it M
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ith
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ig
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lated
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it M
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ch
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m
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k
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h
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d
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g
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ac
k
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ate
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e
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ted
w
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en
s
u
d
d
en
ch
a
n
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e
in
ir
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r
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m
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d
is
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V
s
y
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te
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MP
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r
ti
f
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Ne
u
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al
Net
w
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k
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A
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h
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e
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p
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ed
th
at
th
e
p
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o
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s
ed
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co
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tr
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n
i
m
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e
th
e
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f
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c
y
o
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V
ar
r
a
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y
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ed
u
c
in
g
t
h
e
o
s
cillatio
n
ar
o
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n
d
th
e
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P
an
d
t
h
er
ef
o
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e
d
ec
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ea
s
e
t
h
e
p
o
wer
lo
s
s
es
th
a
t
n
o
ted
in
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V
w
i
th
P
&
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w
a
y
,
as
w
e
l
l
as
th
e
tr
a
n
s
ie
n
t
r
esp
o
n
s
e
o
f
A
N
N
is
b
et
ter
th
a
n
P
&
O
co
n
t
r
o
ller
w
h
e
n
s
u
d
d
en
ch
an
g
e
o
f
ir
r
ad
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n
o
r
te
m
p
e
r
atu
r
e
o
cc
u
r
.
RE
F
E
R
E
NC
E
S
[1
]
M
.
T
.
M
a
k
h
lo
u
f
i,
M
.
S
.
Kh
ired
d
i
n
e
,
Y.
A
b
d
e
ss
e
m
e
d
,
a
n
d
A
.
Bo
u
t
a
rfa
,
“
T
r
a
c
k
in
g
P
o
w
e
r
P
h
o
to
v
o
l
taic
S
y
st
e
m
u
sin
g
A
rti
f
icia
l
Ne
u
ra
l
Ne
t
w
o
rk
Co
n
tro
l
S
trate
g
y
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
I
n
telli
g
e
n
t
S
y
ste
ms
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
v
o
l.
6
,
n
o
.
1
2
,
p
p
.
1
7
-
2
6
,
2
0
1
4
.
[2
]
J.
Jia
n
g
,
T
.
Hu
a
n
g
Y.
Hs
i
a
o
a
n
d
C.
Ch
e
n
,
“
M
a
x
im
u
m
P
o
w
e
r
T
ra
c
k
in
g
f
o
r
P
h
o
t
o
v
o
lt
a
ic
P
o
w
e
r
S
y
st
e
m
,
”
T
a
mk
a
n
g
J
o
u
rn
a
l
o
f
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
,
v
o
l.
8
,
n
o
.
2
,
p
p
.
1
4
7
-
1
5
3
,
2
0
0
2
.
[3
]
S
.
D.
S
tallo
n
,
K.
V
.
Ku
m
a
r,
S
.
S
.
Ku
m
a
r,
a
n
d
J.
Ba
b
y
,
“
S
i
m
u
latio
n
o
f
Hig
h
S
tep
-
Up
DC
–
DC
Co
n
v
e
rter
f
o
r
P
h
o
t
o
v
o
lt
a
ic
M
o
d
u
le
A
p
p
li
c
a
ti
o
n
u
sin
g
M
AT
LAB/S
IM
U
L
INK,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
In
telli
g
e
n
t
S
y
ste
ms
a
n
d
Ap
p
li
c
a
ti
o
n
s
,
v
o
l
.
5
,
n
o
.
7
,
p
p
.
7
2
-
8
2
,
2
0
1
3
.
[4
]
Z.
M
.
A
b
d
u
a
ll
a
h
,
O.
T
.
M
a
h
m
o
o
d
,
a
n
d
A
.
M
.
T
.
I.
A
l
-
n
a
ib
,
“
P
h
o
t
o
v
o
lt
a
ic
Ba
tt
e
r
y
Ch
a
rg
in
g
S
y
ste
m
Ba
se
d
o
n
P
IC
1
6
F
8
7
7
A
M
icro
c
o
n
tro
l
ler
,”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
En
g
in
e
e
rin
g
a
n
d
A
d
v
a
n
c
e
d
T
e
c
h
n
o
l
o
g
y
(
IJ
EA
T
)
,
v
o
l.
3,
n
o
.
4
,
p
p
.
2
7
-
3
1
,
2
0
1
4
.
[5
]
R.
De
Ke
y
se
r,
J.
Bo
n
il
la,
I.
S
.
M
e
m
b
e
r,
C.
Io
n
e
sc
u
,
a
n
d
J.
A
l
a
rc
o
n
,
“
A
c
o
m
p
a
ra
ti
v
e
stu
d
y
o
f
se
v
e
r
a
l
c
o
n
tro
l
tec
h
n
iq
u
e
s
a
p
p
l
ied
t
o
a
b
o
o
st
c
o
n
v
e
rter,”
IEE
E
1
0
t
h
In
t
Co
n
f
o
n
Op
ti
mis
a
ti
o
n
o
f
E
lec
trica
l
a
n
d
El
e
c
tro
n
ic
Eq
u
i
p
me
n
t
OP
T
IM
,
Bra
so
v
Ro
m
a
n
ia,
p
p
.
7
1
-
7
8
,
2
0
0
6
.
[6
]
M
.
Hla
il
i
a
n
d
H.
M
e
c
h
e
rg
u
i,
“
Co
m
p
a
riso
n
o
f
Diffe
re
n
t
M
P
P
T
A
l
g
o
rit
h
m
s
w
it
h
a
P
ro
p
o
se
d
O
n
e
Us
in
g
a
P
o
w
e
r
Esti
m
a
to
r
f
o
r
G
rid
Co
n
n
e
c
ted
P
V S
y
ste
m
s,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
P
h
o
t
o
e
n
e
rg
y
,
v
o
l
.
2
0
1
6
,
p
p
.
1
-
10,
2
0
1
6
.
[7
]
N.
F
e
m
ia,
G
.
P
e
tro
n
e
,
G
.
S
p
a
g
n
u
o
lo
,
a
n
d
M
.
V
it
e
ll
i,
“
Op
ti
m
iza
ti
o
n
o
f
p
e
rtu
rb
a
n
d
o
b
se
rv
e
m
a
x
i
m
u
m
p
o
w
e
r
p
o
in
t
trac
k
in
g
m
e
th
o
d
,
”
IEE
E
T
ra
n
sa
c
t
io
n
s
o
n
Po
we
r E
lec
tro
n
ics
,
v
o
l.
2
0
,
n
o
.
4
,
p
p
.
9
6
3
–
9
7
3
,
2
0
0
5
.
[8
]
T
.
Ch
a
it
a
n
y
a
,
Ch
.
S
a
ib
a
b
u
,
a
n
d
J.
S
.
K
u
m
a
ri
,
“
M
o
d
e
li
n
g
a
n
d
S
im
u
latio
n
o
f
P
V
A
rra
y
a
n
d
it
s
P
e
rf
o
rm
a
n
c
e
En
h
a
n
c
e
m
e
n
t
Us
in
g
M
P
P
T
(P
&
O)
Tec
h
n
iq
u
e
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Co
mp
u
ter
S
c
in
c
e
a
n
d
Co
mm
u
n
icti
o
n
.
Ne
two
rk
s
,
v
o
l.
1
,
n
o
.
1
,
p
p
.
9
-
1
6
,
2
0
1
1
.
[9
]
K.
S
a
id
i,
M
.
M
a
a
m
o
u
n
,
a
n
d
M
.
B
o
u
n
e
k
h
la,
“
A
n
e
w
h
ig
h
p
e
rf
o
rm
a
n
c
e
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riab
le
ste
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e
p
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rtu
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n
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se
rv
e
M
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T
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f
o
r
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o
t
o
v
o
lt
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ic
sy
ste
m
,
”
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ter
n
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l
J
o
u
rn
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o
f
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o
we
r
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e
c
tro
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ics
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n
d
Dr
ive
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st
e
m
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0
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0
]
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.
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ll
im
a
ll
a
,
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n
d
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.
K.
M
is
h
ra
,
“
A
No
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A
d
a
p
ti
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P
&
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M
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o
rit
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m
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u
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e
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IEE
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ra
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n
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e
rg
y
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o
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io
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s
,
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p
.
1
–
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,
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0
1
4
.
[1
1
]
M
.
I.
Ba
h
a
ri,
P
.
T
a
ra
ss
o
d
i,
Y.
M
.
Na
e
in
i,
A
.
K.
Kh
a
li
lab
a
d
,
a
n
d
P
.
S
h
iraz
i,
“
M
o
d
e
li
n
g
a
n
d
S
im
u
latio
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Hill
Cli
m
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ig
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T
A
lg
o
rit
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m
f
o
r
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o
V
o
l
taic
A
p
p
li
c
a
ti
o
n
,
”
I
n
ter
n
a
ti
o
n
a
l
S
y
mp
o
siu
m
o
n
P
o
we
r
El
e
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tro
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ics
,
El
e
c
trica
l
Dr
ive
s,
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to
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ti
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d
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p
.
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-
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0
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4
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2
0
1
6
.
[1
2
]
H.
A
.
S
u
jo
n
o
,
R.
S
u
li
sty
o
wa
ti
,
A
.
S
a
f
i,
a
n
d
C.
W
.
P
rian
a
n
d
a
,
“
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h
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a
rm
W
it
h
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m
u
m
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r
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t
T
ra
k
e
r
Us
in
g
Hill
Cli
m
b
in
g
A
l
g
o
rit
h
m
,”
AR
PN
J
o
u
rn
a
l
o
f
En
g
in
e
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rin
g
a
n
d
A
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li
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o
.
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p
.
4
1
6
7
–
4
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7
2
,
2
0
1
8
.
[1
3
]
R.
I.
P
u
tri
,
S
.
W
ib
o
w
o
,
a
n
d
M
.
Rif
a
,
“
M
a
x
i
m
u
m
p
o
w
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trac
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re
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tal
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n
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u
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m
e
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o
d
,
”
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e
rg
y
Pro
c
e
d
ia
,
v
o
l.
6
8
,
p
p
.
2
2
-
3
0
,
2
0
1
5
.
[1
4
]
N.
E.
Zak
z
o
u
k
,
M
.
A
.
El
sa
h
a
rt
y
,
A
.
K.
A
b
d
e
lsa
la
m
,
A
.
A
.
He
lal,
a
n
d
B.
W
.
W
il
li
a
m
s,
“
I
m
p
ro
v
e
d
p
e
rf
o
r
m
a
n
c
e
lo
w
-
c
o
st
in
c
re
m
e
n
tal
c
o
n
d
u
c
tan
c
e
P
V
M
P
P
T
tec
h
n
iq
u
e
,
”
IET
Ren
e
wa
b
le
Po
we
r
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n
e
ra
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n
,
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l.
1
0
,
n
o
.
4
,
p
p
.
5
6
1
-
5
7
4
,
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0
1
6
.
[1
5
]
S
.
Kh
a
d
i
d
ja,
M.
M
o
u
n
tas
sa
r
,
a
n
d
B.
M
'
h
a
m
e
d
“
Co
m
p
a
ra
ti
v
e
st
u
d
y
o
f
In
c
re
m
e
n
tal
Co
n
d
u
c
ta
n
c
e
a
n
d
P
e
rt
u
rb
&
Ob
se
rv
e
M
P
P
T
M
e
th
o
d
s
f
o
r
P
h
o
to
v
o
lt
a
ic
S
y
ste
m
,
”
2
0
1
7
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Gr
e
e
n
En
e
rg
y
Co
n
v
e
rs
io
n
S
y
ste
ms
(
GECS
)
,
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m
m
a
m
e
t,
p
p
.
1
-
6
,
2
0
1
7
.
[1
6
]
A
.
A
.
A
b
d
u
lraz
z
a
q
a
n
d
A
.
H.
A
li
,
“
E
ff
icie
n
c
y
P
e
rf
o
r
m
a
n
c
e
s
o
f
Tw
o
M
P
P
T
A
l
g
o
rit
h
m
s
f
o
r
P
V
S
y
ste
m
W
it
h
Diff
e
r
e
n
t
S
o
lar
P
a
n
e
ls
Irra
d
ian
c
e
s,”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Po
we
r
El
e
c
tro
n
ics
a
n
d
Dr
ive
S
y
ste
m
(
IJ
PE
DS
)
,
v
o
l.
9
,
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o
.
4
,
p
p
.
1
7
5
5
-
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7
6
4
,
2
0
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8
.
[1
7
]
A
.
S
.
S
a
m
o
sir,
H.
G
u
s
m
e
d
i,
S
.
P
u
rw
i
y
a
n
ti
,
a
n
d
E.
Ko
m
a
las
a
ri,
“
M
o
d
e
li
n
g
a
n
d
S
im
u
latio
n
o
f
F
u
z
z
y
L
o
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ic
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a
s
e
d
M
a
x
i
m
u
m
P
o
w
e
r
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n
t
T
ra
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k
in
g
(
M
P
P
T
)
f
o
r
P
V
A
p
p
li
c
a
ti
o
n
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
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o
mp
u
ter
En
g
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n
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rin
g
(
IJ
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)
,
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l.
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o
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p
p
.
1
3
1
5
-
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3
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3
,
2
0
1
8
.
[1
8
]
H.
Bo
u
n
e
c
h
b
a
,
A
.
Bo
u
z
id
,
K.
Na
b
ti
,
a
n
d
H.
Be
n
a
ll
a
,
“
Co
m
p
a
riso
n
o
f
p
e
rtu
rb
&
o
b
se
rv
e
a
n
d
f
u
z
z
y
lo
g
ic
in
ma
x
i
m
u
m
p
o
w
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r
p
o
in
t
trac
k
e
r
f
o
r
P
V sy
ste
m
s,”
En
e
rg
y
Pro
c
e
d
ia
,
v
o
l.
5
0
,
p
p
.
6
7
7
-
6
8
4
,
2
0
1
4
.
[1
9
]
H.
Otm
a
n
e
,
M
.
Yo
u
ss
e
f
,
B.
M
o
k
h
tar,
a
n
d
A
.
In
f
o
,
“
Co
m
p
a
ra
ti
v
e
a
n
a
ly
sis
o
f
c
a
sc
a
d
e
d
F
u
z
z
y
-
P
I
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tro
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se
d
-
M
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P
T
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n
d
p
e
rtu
r
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n
d
o
b
se
rv
e
M
P
P
T
in
a
g
rid
-
c
o
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n
e
c
ted
P
V
sy
ste
m
o
p
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ra
ti
n
g
u
n
d
e
r
d
if
f
e
re
n
t
we
a
th
e
r
a
n
d
lo
a
d
i
n
g
c
o
n
d
i
ti
o
n
s,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Po
we
r
El
e
c
tro
n
ics
a
n
d
Dr
ive
S
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ste
m
(
IJ
PE
DS
)
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v
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l
.
1
0
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.
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,
p
p
.
1
9
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4
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0
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9
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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R.
M
.
Esse
f
i,
M
.
S
o
u
issi,
a
n
d
H.
H.
A
b
d
a
ll
a
h
,
“
M
a
x
i
m
u
m
P
o
w
e
r
P
o
in
t
T
ra
c
k
in
g
Co
n
tro
l
U
sin
g
Ne
u
ra
l
Ne
t
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s
f
o
r
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tan
d
-
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lo
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h
o
t
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o
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m
s,”
In
ter
n
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ti
o
n
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l
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o
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rn
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l
o
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M
o
d
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rn
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li
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e
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n
d
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n
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l.
3
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no
.
3
,
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p
.
5
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5
,
2
0
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4
.
[2
1
]
F
.
S
a
a
d
a
o
u
i
1
,
K.
M
a
m
m
a
r,
a
n
d
A
.
H
a
z
z
a
b
,
“
M
o
d
e
li
n
g
o
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h
o
to
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o
lt
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ic
s
y
ste
m
w
it
h
m
a
x
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m
u
m
p
o
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e
r
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o
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trac
k
in
g
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o
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tro
l
b
y
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e
u
ra
l
n
e
t
w
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rk
s,”
In
te
rn
a
ti
o
n
a
l
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o
u
rn
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l
o
f
P
o
we
r
El
e
c
tro
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n
d
Dr
ive
S
y
ste
m
(
I
J
PE
DS
)
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l.
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0
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o
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p
.
1
5
7
5
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[2
2
]
H.
Ya
ti
m
i
a
n
d
E.
A
ro
u
d
a
m
,
“
M
a
th
e
m
a
ti
c
a
l
M
o
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e
li
n
g
a
n
d
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im
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lat
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n
o
f
P
h
o
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o
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o
lt
a
ic
P
o
w
e
r
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o
u
rc
e
u
sin
g
M
a
tl
a
b
/
S
im
u
li
n
k
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
r
n
a
l
o
f
In
n
o
v
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ti
o
n
a
n
d
A
p
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e
d
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tu
d
ies
,
v
o
l.
1
6
,
n
o
.
2
,
p
p
.
3
2
2
-
3
3
0
,
2
0
1
6
.
[2
3
]
A
.
P
ra
d
h
a
n
a
n
d
B.
P
a
n
d
a
,
“
A
S
im
p
li
f
ied
De
sig
n
a
n
d
M
o
d
e
li
n
g
o
f
Bo
o
st
Co
n
v
e
rter
f
o
r
P
h
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to
v
o
lt
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ic
S
y
te
m
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
8
,
n
o
.
1
,
p
p
.
1
4
1
-
1
4
9
,
2
0
1
8
.
[2
4
]
R.
P
a
la
n
isa
m
y
,
K.
V
ij
a
y
a
k
u
m
a
r
,
V
.
V
e
n
k
a
tac
h
a
lam
,
R.
M
.
Na
ra
y
a
n
a
n
,
D.
S
a
ra
v
a
n
a
k
u
m
a
r,
a
n
d
K.
S
a
ra
v
a
n
a
n
,
“
S
im
u
latio
n
o
f
v
a
rio
u
s
DC
-
DC
c
o
n
v
e
rters
f
o
r
p
h
o
t
o
v
o
lt
a
ic
sy
ste
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
tric
a
l
a
n
d
Co
mp
u
ter
E
n
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
9
,
n
o
.
2
,
p
p
.
9
1
7
-
9
2
5
,
2
0
1
9
.
[2
5
]
S
.
A
ji
,
D.
A
ji
a
tm
o
,
I.
Ro
b
a
n
d
i
,
a
n
d
H.
S
u
ry
o
a
t
m
o
jo
,
“
M
p
p
t
Ba
se
d
On
F
u
z
z
y
L
o
g
ic
Co
n
tro
ll
e
r
(F
L
C)
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