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w
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t
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v
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-
bas
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d S
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z
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l
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hm
(
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S
A
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w
as
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v
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l
ope
d t
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m
i
ne t
he opt
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m
al
s
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z
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on w
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c
h w
as
l
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s
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we
v
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per
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n pr
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s
:
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r
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-
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ed P
hot
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ol
t
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l
gor
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S
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.
C
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2
01
7
I
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s
t
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t
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A
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s r
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1
. In
t
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dec
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R
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e and w
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l
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ot
be d
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et
ed t
hr
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out
t
i
m
e
[
1]
.
T
her
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a f
ew
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y
p
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of
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i
t
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due t
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t
a
l
l
at
i
on
[
12]
[
1
4]
.
2.
R
e
sea
r
ch
M
et
h
o
d
2
.1
.
A
s
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por
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r
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W
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.
H
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175
5.
4
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[
4]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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4
752
I
J
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V
o
l.
8
,
N
o.
1
,
O
c
t
o
ber
20
17
:
1
69
–
1
76
170
2
.
2
.
S
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l
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c
ti
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f S
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C
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p
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s
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P
V
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d i
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v
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[
13
]
.
2
.
2
.
1
P
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v
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l
ta
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c
M
o
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l
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T
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m
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[
5]
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_s
t
c
, i
n
V
,
th
e
t
em
per
at
ur
e
c
oef
f
i
c
i
ent
f
or
open
c
i
r
c
ui
t
v
ol
t
ag
e,
ɣ
V
oc
,
i
n
%
p
er
°C
,
t
h
e t
em
per
at
ur
e
c
oef
f
i
c
i
ent
f
or
s
hor
t
c
i
r
c
ui
t
c
ur
r
en
t
,
ɣ
Isc
,
i
n
%
p
er
°C
,
t
he
w
i
dt
h
of
e
ac
h
P
V
m
odul
e
i
n
m
i
s
des
c
r
i
bed
as
L
wi
d
th
,
L
l
en
g
t
h
i
s
t
h
e
l
en
gt
h
of
eac
h
P
V
m
odul
e
i
n m
and
t
h
e t
e
m
per
at
ur
e c
oef
f
i
c
i
ent
f
or
m
ax
i
m
u
m
pow
er
,
ɣ
Pmp
,
i
n
%
per
°C
.
2
.
2
.
2
I
n
v
er
t
er
A
not
her
m
ai
n
c
om
ponent
of
a
G
C
P
V
s
y
s
t
em
i
s
i
n
v
er
t
er
.
T
he
i
n
v
e
r
t
er
c
o
nv
er
t
s
t
he
D
C
el
ec
t
r
i
c
i
t
y
f
r
om
t
he P
V
ar
r
ay
i
n
t
o
A
C
el
ec
t
r
i
c
i
t
y
t
hat
m
at
c
hes
t
he
gr
i
d
-
e
l
ec
t
r
i
c
i
t
y
.
T
he r
at
i
ngs
of
i
n
v
er
t
er
r
equ
i
r
ed f
or
t
he s
i
z
i
ng pr
oc
es
s
ar
e t
he m
ax
i
m
u
m
i
nput
v
o
l
t
a
ge r
at
i
ng
of
t
he i
nv
er
t
er
,
V
ma
x
_
in
v
i
n
V
,
m
i
ni
m
u
m
Max
i
m
u
m
P
ow
er
P
oi
nt
T
r
ac
k
er
s
(
MP
P
T
)
i
nput
v
ol
t
a
ge
of
i
n
v
er
t
er
,
V
mi
n_
i
n
v_
MPPT
i
n V
,
nom
i
nal
o
ut
put
po
w
er
of
i
n
v
er
t
er
,
P
n
o
m
i
n
al
_
i
nv
i
n
W
,
m
ax
i
m
u
m
i
npu
t
c
ur
r
ent
of
t
he
i
n
v
er
t
er
,
I
dc
_i
n
v
i
n
A
a
nd ef
f
i
c
i
enc
y
of
i
n
v
er
t
er
,
η
in
v
in
%
.
2
.
2
.
3
S
i
te
L
a
y
o
u
t
T
he
s
el
ec
t
ed
s
i
t
e
enc
om
pas
s
es
appr
ox
i
m
at
el
y
199
,
90
0.
2
m
2
o
f
l
and
ar
ea
av
ai
l
a
bl
e
f
or
P
V
ar
r
a
y
i
ns
t
a
l
l
at
i
on us
i
n
g f
r
ee
-
s
t
and
i
ng m
ode an
d t
he
er
ec
t
i
on of
po
w
er
hous
e
.
T
he i
ns
t
a
l
l
at
i
on
of
P
V
ar
r
a
y
i
s
di
s
t
r
i
but
ed i
n f
or
m
of
bl
oc
k
s
t
o
f
ac
i
l
i
t
a
t
e m
ai
nt
enanc
e ac
t
i
v
i
t
i
es
.
I
n add
i
t
i
on,
a
r
es
er
v
ed
per
i
m
et
er
ar
ea s
u
r
r
oundi
ng t
he p
l
a
nt
i
s
a
l
l
o
w
ed f
or
s
i
m
i
l
ar
pur
pos
e.
T
he
di
s
t
anc
e f
r
om
t
he
l
as
t
P
V
ar
r
a
y
bl
oc
k
t
o t
he
edg
e of
t
he
l
a
nd
bor
d
er
i
s
s
et
t
o
be
4 m
et
er
s
,
ex
c
e
pt
f
or
on
e s
i
de
of
t
he
l
an
d
w
hi
c
h
al
l
o
w
s
6
m
et
er
s
di
s
t
anc
e f
r
om
t
he l
a
s
t
ar
r
a
y
bl
oc
k
t
o t
he
bor
d
er
of
t
he
l
a
nd f
or
t
he
c
o
ns
t
r
uc
t
i
o
n
of
p
o
w
er
h
ous
e,
t
he
num
ber
of
r
o
w
s
per
b
l
oc
k
,
N
r
o
w
_
b
l
o
c
k
i
n
i
nt
eg
er
s
,
t
he
l
engt
h
and
w
i
dt
h of
t
he us
a
bl
e
l
a
nd ar
ea f
or
t
he s
ol
ar
f
ar
m
,
denot
ed as
L
D
1
an
d L
D2
r
es
pec
t
i
v
e
l
y
i
n
m
et
er
s
,
t
he
r
es
er
v
e
di
s
t
anc
e
ar
ou
nd
t
he
a
v
a
i
l
a
bl
e
s
pa
c
e
ar
ea,
r
s
v
x
,
r
s
v
z
a
nd
r
s
v
y
i
n
m
et
er
s
,
t
he
hor
i
z
ont
al
g
ap
bet
w
een
t
h
e a
dj
ac
ent
P
V
ar
r
a
y
bl
oc
k
s
,
f
x
i
n m
et
er
s
,
t
h
e g
ap
b
et
w
ee
n t
he
P
V
m
odul
es
,
G
i
n
m
et
er
s
and
t
he
r
es
er
v
e
ar
e
a
f
or
bui
l
d
t
he
po
w
er
h
ous
e
r
s
v
_
ph_
y
i
n
m
.
T
he
P
V
ar
r
a
y
i
s
t
i
l
t
e
d
at
a
t
i
l
t
ang
l
e
β
i
n
°.
T
he
w
i
dt
h
of
t
he
ar
ea
c
o
v
er
e
d
b
y
a
P
V
m
odul
e
t
i
l
t
e
d
a
t
β
°,
W
1
_PV
i
n
m
et
er
s
,
t
he
hei
g
ht
of
a
P
V
m
odul
e
t
i
l
t
ed
at
β
°,
H
1
_P
V
i
n
m
et
er
s
,
t
he
l
eng
t
h
r
equi
r
e
d
f
or
t
he m
et
al
l
i
c
s
par
s
at
a
v
er
t
i
c
al
l
i
n
e,
B
1
_v
e
r
t
i
n m
e
t
er
s
,
t
h
e t
ot
al
l
e
ngt
h of
t
h
e
v
er
t
i
c
al
s
par
s
of
eac
h
s
i
de of
a v
er
t
i
c
a
l
l
i
ne,
B
to
t_v
e
rt
i
n m
et
er
s
,
t
he
t
ot
al
num
ber
of
v
er
t
i
c
al
l
i
n
e,
n
b_
ve
rt
i
n i
nt
e
ger
s
,
t
he
t
ot
a
l
l
en
gt
h
of
r
ow
i
n
a
bl
oc
k
,
L
T_row
i
n
m
et
er
s
,
t
he
t
ot
al
l
engt
h
of
t
he
i
nt
er
m
edi
at
e
s
par
s
of
e
ac
h
s
i
de of
a v
er
t
i
c
a
l
l
i
n
e,
B
2
_v
e
r
t
i
n m
et
er
s
,
t
he t
ot
al
v
o
l
um
e of
t
he f
oundat
i
o
n of
t
he c
o
nc
r
et
e bas
es
,
B
B_
co
n
cre
t
e
i
n
m
et
er
s
3
,
t
he
c
o
nc
r
et
e
w
a
l
l
’
s
h
ei
g
ht
a
nd
t
h
e
c
onc
r
et
e
r
es
pon
di
n
g
t
hi
c
k
nes
s
,
h
w
_
h
e
i
gh
t
and
t
w_t
h
i
ck
i
n
m
et
er
s
.
T
hes
e
i
m
por
t
ant
par
am
et
er
s
w
i
l
l
be
us
e
d
i
n
s
i
z
i
n
g
pr
oc
e
dur
e
t
o
de
t
er
m
i
ne
t
he o
pt
i
m
al
s
i
z
i
ng of
s
ol
ar
f
ar
m
.
2
.
3
.
D
e
v
e
l
o
p
m
e
n
t o
f I
te
r
a
t
i
v
e
-
b
a
s
e
d
S
i
z
i
n
g
A
l
g
o
r
i
th
m
(I
S
A
)
C
on
v
en
t
i
o
na
l
S
i
z
i
ng A
l
go
r
i
t
hm
(
C
S
A
)
w
as
i
n
i
t
i
al
l
y
de
v
e
l
op
ed
as
t
h
e
b
as
i
c
s
i
z
i
ng
al
g
or
i
t
hm
i
n t
hi
s
s
t
ud
y
.
T
he C
S
A
r
equ
i
r
es
t
he des
i
gner
t
o s
el
ec
t
a s
et
of
P
V
m
odul
e and i
n
v
er
t
er
bef
or
e he
or
s
he pr
oc
eeds
w
i
t
h
t
he s
i
z
i
ng
pr
oc
es
s
.
I
f
t
her
e ar
e
m
or
e t
han o
ne c
o
m
bi
nat
i
on
of
P
V
m
odul
es
and i
n
v
er
t
er
s
,
t
he
C
S
A
n
eeds
t
o b
e r
epe
at
e
d
[
15]
[
16]
.
T
he C
S
A
m
et
hod i
s
s
ee
m
s
t
o be
v
e
r
y
t
i
m
e
c
ons
u
m
i
ng
w
he
n
i
t
s
deal
w
i
t
h
num
er
ous
s
et
s
of
s
y
s
t
em
c
o
m
ponent
s
.
T
hus
,
I
t
er
at
i
v
e
-
bas
ed
S
i
z
i
ng A
l
g
or
i
t
hm
(
I
S
A
)
has
b
een
de
v
el
op
ed t
o ev
al
u
at
e e
v
er
y
pos
s
i
bl
e
s
et
of
s
y
s
t
em
c
o
m
ponent
s
s
uc
h
t
hat
t
he
opt
i
m
al
s
et
c
o
ul
d
be
det
er
m
i
ned i
n
a s
i
n
gl
e
r
un.
S
t
ep
1:
D
e
t
er
m
i
ne t
he r
ang
e of
opt
i
m
al
n
um
ber
of
P
V
m
odul
es
,
N
t
f
or
a s
pec
i
f
i
c
i
n
v
er
t
e
r
bas
ed
on t
he o
pt
i
m
al
r
an
ge
of
i
nv
er
t
er
-
to
-
P
V
ar
r
a
y
s
i
z
i
ng r
at
i
o
i
n
i
nt
eger
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
EC
S
IS
S
N
:
2
502
-
4
752
S
i
z
i
ng O
pt
i
m
i
z
at
i
on
of
L
ar
g
e
-
Sc
a
l
e
G
r
id
-
C
o
nn
ec
t
ed
…
(
Muha
mma
d Z
ak
y
i
z
z
u
dd
i
n
B
i
n
R
)
171
_
_
×
1
≤
≤
_
_
×
2
P
no
m
i
na
l
_
i
nv
i
s
t
he
n
om
i
nal
ou
t
put
po
w
er
of
t
he
i
n
v
er
t
er
i
n
W
w
hi
l
e
P
mp_
stc
i
s
t
he
m
ax
i
m
u
m
po
w
er
of
t
he P
V
m
odul
e a
t
s
t
a
ndar
d t
es
t
c
on
di
t
i
o
ns
i
n
W
.
f
d
1
and f
d
2
r
epr
es
ent
s
t
he m
i
ni
m
u
m
and
m
ax
i
m
u
m
of
i
nv
er
t
er
-
to
-
P
V
ar
r
a
y
s
i
z
i
ng
r
at
i
o.
I
n
Mal
a
y
s
i
a,
f
d
1
and
f
d
2
w
er
e
f
ound
t
o
be
1.
0
0
and
0.
90
r
es
pec
t
i
v
e
l
y
[
6]
.
S
t
ep
2:
D
et
er
m
i
ne t
h
e p
o
t
ent
i
al
m
ax
i
m
u
m
open c
i
r
c
ui
t
v
o
l
t
a
ge,
V
oc_
ma
x
i
n
V
a
nd t
h
e
m
i
ni
m
u
m
v
ol
t
a
ge a
t
m
ax
i
m
um
pow
er
,
V
mp
_
m
in
i
n
V
f
or
t
he c
hos
e
n P
V
m
odul
e
.
_
=
_
×
1
+
100%
×
_
−
25
_
=
_
×
1
+
100
%
×
_
−
25
w
her
e
V
o
c
_s
t
c
an
d
V
m
p
_s
t
c
ar
e t
he op
en c
i
r
c
ui
t
v
ol
t
ag
e and t
h
e v
o
l
t
a
ge at
t
h
e m
a
x
i
m
u
m
po
w
er
of
t
he
P
V
m
odul
e a
t
S
T
C
.
γ
i
s
t
h
e t
em
per
at
ur
e c
oef
f
i
c
i
ent
v
a
l
u
e
i
n
%
p
e
r
deg C
w
h
i
l
e
T
c_mi
n
a
nd
T
c_
ma
x
ar
e
t
he
m
i
ni
m
u
m
and
m
ax
i
m
u
m
ef
f
ec
t
i
v
e
c
e
l
l
t
em
per
at
ur
e
r
es
pe
c
t
i
v
e
l
y
i
n
deg
C
.
S
te
p
3
:
D
et
er
m
i
ne t
he m
ax
i
m
u
m
and
m
i
ni
m
u
m
al
l
ow
abl
e num
ber
of
P
V
m
odul
es
per
s
t
r
i
ng,
N
s_
ma
x
a
nd
N
s_
min
i
n i
nt
eg
er
s
.
_
=
ma
x
_
×
1
_
_
=
mi
n
_
_
×
2
_
×
V
ma
x
_
in
v
i
s
t
he
m
ax
i
m
u
m
i
np
ut
v
o
l
t
ag
e
r
at
i
n
g
of
t
he
i
n
v
er
t
er
i
n
V
w
hi
l
e
V
min
_
i
n
v_MPP
T
is
t
h
e
m
i
ni
m
u
m
MP
P
T
w
i
ndo
w
v
o
l
t
ag
e of
t
he
i
n
v
er
t
er
i
n
V
.
T
he v
al
ues
of
s
af
et
y
m
ar
gi
n
f
s1
,
f
s2
,
ar
e 0.
95
and
1.
10
w
h
i
l
e
t
he
v
al
ue f
or
c
abl
e
l
os
s
f
ac
t
or
,
f
c
a
b
i
s
0.
9
5 r
es
pec
t
i
v
e
l
y
[
7]
.
S
t
ep
4:
D
et
er
m
i
ne t
he
m
ax
i
m
u
m
nu
m
ber
of
s
t
r
i
ngs
i
n
par
al
l
e
l
,
N
pm
ax
i
n i
nt
eg
er
s
.
=
_
_
×
3
w
her
e
I
d
c
_
i
nv
i
s
t
h
e
m
ax
i
m
u
m
i
nput
c
ur
r
ent
of
t
he
i
n
v
e
r
t
er
i
n
A
an
d
I
sc_
st
c
i
s
t
he
v
al
u
e
of
t
he s
hor
t
c
i
r
c
ui
t
c
ur
r
ent
of
P
V
m
odul
e a
t
t
he S
T
C
i
n A
.
A
v
a
l
ue
of
1.
25
w
as
c
hos
e
n f
or
f
s3
i
n t
hi
s
s
t
u
d
y.
S
t
ep
5:
D
et
er
m
i
ne
t
h
e
op
t
i
m
al
P
V
ar
r
a
y
c
onf
i
g
ur
at
i
on
f
or
eac
h
i
n
v
er
t
er
b
y
s
el
ec
t
i
ng
t
h
e
num
ber
of
P
V
m
odul
es
pe
r
s
t
r
i
ng,
N
s_PV
a
nd t
h
e nu
m
ber
o
f
par
al
l
e
l
s
t
r
i
ngs
,
N
p
_P
V
.
T
he t
ot
al
num
ber
of
P
V
m
odul
es
c
on
nec
t
ed
t
o e
ac
h i
nv
er
t
er
,
N
P
V_INV
i
n i
nt
eg
er
s
i
s
c
al
c
u
l
at
e
d us
i
n
g
_
=
_
×
_
A
t th
i
s
s
ta
g
e
,
N
PV_
INV
m
us
t
be i
n t
h
e r
an
ge
of
N
t
.
S
t
ep
6:
D
et
er
m
i
ne
t
h
e r
eq
ui
r
ed t
ot
a
l
num
ber
of
P
V
m
odul
es
f
or
t
he
w
h
ol
e s
o
l
ar
f
ar
m
,
N
PV_a
ll
i
n i
nt
e
ger
s
t
ha
t
ar
e
di
s
t
r
i
but
e
d t
o
t
he
t
ot
a
l
n
um
ber
of
i
nv
er
t
er
s
f
or
t
he s
ol
ar
f
ar
m
,
x
i_
in
v
.
_
=
_
_
_
=
_
_
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN
:
25
02
-
4
752
I
J
E
EC
S
V
o
l.
8
,
N
o.
1
,
O
c
t
o
ber
20
17
:
1
69
–
1
76
172
_
1
=
_
×
_
=
_
−
_
1
w
her
e
P
arr_re
q
i
s
t
he
t
ot
a
l
ar
r
a
y
po
w
er
c
apac
i
t
y
r
eq
ui
r
ed
f
r
o
m
t
he
s
ol
ar
f
ar
m
i
n
W
.
I
n
t
h
i
s
s
t
ud
y
,
5
M
W
s
ol
ar
f
ar
m
i
s
s
et
f
or
t
he
des
i
gn
c
as
e s
t
u
d
y
.
N
a
c
t
ua
l
_
1
i
s
t
he
ac
t
u
al
t
ot
al
num
ber
s
of
t
he
P
V
m
odul
es
d
es
i
gn
ed
f
or
t
he
s
ol
a
r
f
ar
m
i
n
i
nt
eger
s
w
hi
l
e
N
b
al
i
s
t
h
e
ba
l
anc
e
of
t
he
P
V
m
odul
es
t
hat
w
i
l
l
b
e c
on
nec
t
ed
t
o
a
s
i
ngl
e i
nv
er
t
er
.
S
t
ep
7:
D
et
er
m
i
ne t
he
w
i
dt
h of
t
he
bl
oc
k
,
W
T
_
bl
oc
k
i
n m
et
er
s
.
_
=
1
_
×
_
1
_
=
ℎ
×
co
s
w
her
e
L
w
id
th
i
s
t
h
e
w
i
dt
h
of
eac
h
P
V
m
odul
e
i
n
m
et
er
s
.
T
he
v
al
u
e
of
t
i
l
t
ed
ang
l
e
β
°
us
ed
i
n t
h
i
s
s
t
ud
y
i
s
10° f
ac
i
ng s
out
h bas
e
d o
n t
h
e
al
l
oc
at
e
d s
ol
ar
f
ar
m
w
hi
c
h i
s
at
K
u
al
a
T
er
engganu
,
T
er
engg
anu
[
4]
an
d
t
he
t
y
p
i
c
a
l
v
a
l
ue
us
e
d f
or
N
r
o
w
_
b
l
o
c
k
i
s
bet
w
ee
n 3
t
o 4
[
8]
.
T
hen,
t
he
v
a
l
ue s
e
l
ec
t
e
d t
o
be
us
ed i
n t
h
i
s
s
t
ud
y
i
s
3.
S
t
ep
8:
D
et
er
m
i
ne t
he
m
ax
i
m
u
m
hei
ght
,
H
T_
b
l
o
c
k
of
eac
h bl
oc
k
i
n m
et
er
s
.
_
=
1
_
×
_
1
_
=
ℎ
×
si
n
S
t
ep
9:
D
et
er
m
i
ne t
he r
eq
u
i
r
ed d
i
s
t
anc
e be
t
w
ee
n adj
a
c
ent
bl
oc
k
s
i
n f
r
ont
-
bac
k
pos
i
t
i
o
n,
F
y
_
ad
j
i
n
m
et
er
s
.
=
23
.
4°
si
n
360
−
80
365
ℎ
=
(
12
−
)
×
15°
_
=
_
×
si
n
×
co
s
×
co
s
ℎ
−
co
s
×
si
n
si
n
×
si
n
+
co
s
×
co
s
×
co
s
ℎ
w
her
e
δ
d
ec
an
d
ω
h
ou
r
ar
e
t
h
e
s
ol
ar
dec
l
i
nat
i
o
n
ang
l
e,
i
n
°
and
t
he
s
ol
ar
hour
ang
l
e,
i
n
°
r
es
pec
t
i
v
el
y
. N
e
x
t,
N
d
a
y
i
s
t
he d
a
y
num
ber
of
t
he
y
ear
and T
i
s
t
h
e ac
t
u
al
t
i
m
e i
n
hour
b
et
w
e
en
0
and
24
h
our
s
[
9]
.
φ
l
a
t
i
s
de
s
c
r
i
bed
as
t
he
l
at
i
t
u
de
of
t
he
s
i
t
e
i
n
°.
I
n
t
h
i
s
s
t
ud
y
,
φ
l
a
t
i
s
s
et
t
o
be
5.
32N
[
4]
.
I
n
d
et
er
m
i
ni
ng
F
y
_
ad
j
,
o
nl
y
t
he
hi
ghes
t
v
al
ue of
F
y
_
ad
j
ob
t
ai
ned
w
i
l
l
b
e us
ed
as
t
he
di
s
t
anc
e
bet
w
ee
n f
r
ont
an
d
bac
k
bl
oc
k
s.
S
t
ep
10:
D
et
er
m
i
ne
t
he
t
ot
al
num
ber
of
v
er
t
i
c
al
and
h
or
i
z
ont
al
P
V
m
odu
l
es
ar
r
angem
ent
,
N
b
_v
e
r
t
an
d
N
b
_h
o
r
z
i
n i
nt
e
ge
r
s
.
_
=
2
−
+
_
+
_
_
ℎ
=
1
−
+
_
ℎ
ℎ
+
×
_
+
w
her
e
L
l
e
ng
t
h
is
t
h
e l
eng
t
h
of
eac
h P
V
m
odul
e
,
i
n m
et
er
s
.
T
he v
a
l
ue
of
f
x
us
ed
i
n t
hi
s
s
t
ud
y
i
s
2 m
and
G
i
s
0.
02
m
.
S
t
ep
11:
D
et
er
m
i
ne
t
h
e
t
ot
al
n
um
ber
of
ar
r
a
y
bl
oc
k
s
i
n
f
r
ont
-
bac
k
pos
i
t
i
ons
,
N
b
l
o
c
k
_
P
V
in
i
nt
e
ger
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
EC
S
IS
S
N
:
2
502
-
4
752
S
i
z
i
ng O
pt
i
m
i
z
at
i
on
of
L
ar
g
e
-
Sc
a
l
e
G
r
id
-
C
o
nn
ec
t
ed
…
(
Muha
mma
d Z
ak
y
i
z
z
u
dd
i
n
B
i
n
R
)
173
_
=
_
_
×
_
S
t
ep
12:
D
e
t
er
m
i
ne
t
he
ac
t
ual
di
m
ens
i
on
of
t
he
r
e
qui
r
ed
i
ns
t
a
l
l
at
i
on
ar
e
a
f
or
t
he
l
ar
ge
-
s
c
al
e G
C
P
V
s
y
s
t
em
,
D
1
_D
I
M
and
D
2
_D
I
M
i
n m
et
er
s
.
D
1
_D
I
M
i
s
t
he t
ot
a
l
ac
t
ua
l
di
m
ens
i
on of
t
he
hor
i
z
ont
al
P
V
m
odul
e ar
r
a
ngem
ent
a
nd
D
2
_D
I
M
i
s
t
h
e
t
ot
al
ac
t
u
al
d
i
m
ens
i
on of
t
he v
er
t
i
c
al
P
V
m
odul
e ar
r
ang
em
ent
.
1
_
=
_
ℎ
×
ℎ
2
_
=
_
×
ℎ
1
_
≤
1
2
_
≤
2
I
n or
der
t
o m
a
k
e s
ur
e
t
he r
equ
i
r
ed
i
ns
t
a
l
l
at
i
on ar
e
a i
s
be
t
w
ee
n t
h
e r
an
ges
of
t
he
av
a
i
l
ab
l
e s
pac
e ar
ea
,
t
h
e c
ond
i
t
i
ons
ab
ov
e
w
er
e i
m
pl
e
m
ent
ed.
S
t
ep
13:
D
et
er
m
i
ne t
he t
ot
al
l
e
ngt
h of
t
he m
et
al
l
i
c
s
pa
r
s
,
B
t
ot
_
s
p
ar
s
i
n m
et
er
s
us
ed f
or
t
he
m
ount
i
ng s
t
r
uc
t
ur
es
us
i
ng.
_
=
1
_
×
_
1
_
=
2
×
_
+
_
+
_
+
2
_
+
2
×
ℎ
_
=
×
2
2
_
=
1
_
=
_
×
ℎ
2
_
=
_
2
_
=
_
_
_
=
2
+
2
_
×
ℎ
_
ℎ
ℎ
×
_
ℎ
×
ℎ
×
_
S
t
ep
14:
D
et
er
m
i
ne t
h
e t
o
t
al
ar
e
a c
o
v
er
ed
f
or
t
he
l
ar
g
e
-
s
c
al
e G
C
P
V
s
y
s
t
em
i
n m
et
er
s
2
.
_
=
2
−
(
+
)
ℎ
_
=
1
−
+
_
ℎ
=
_
×
ℎ
_
L
vert_
A
a
nd
L
h
or
z
_A
ar
e
t
he
v
er
t
i
c
al
an
d
hor
i
z
ont
al
l
e
ngt
h
of
t
he
av
ai
l
ab
l
e
s
pac
e
t
o
i
ns
t
a
l
l
t
he
P
V
m
odul
e
i
n m
et
er
s
a
nd
A
L
an
d
i
s
t
he
a
v
a
i
l
abl
e
ar
ea t
hat
c
an
us
ed
t
o
i
ns
t
al
l
t
he
P
V
m
odul
e,
i
n
m
et
er
s
2
.
T
he t
ot
al
ar
ea
c
ov
er
ed
f
or
t
he
G
C
P
V
s
y
s
t
em
i
nc
l
udi
ng
t
he s
p
ac
e
r
e
qui
r
e
d
t
o
bu
i
l
d
t
he p
o
w
er
h
ous
e.
L
D1
and L
D2
f
or
t
hi
s
s
t
ud
y
ar
e
44
7.
1
0
2 m
eac
h.
2
.
3
.
1
E
v
a
l
u
a
ti
o
n
o
f T
e
c
h
n
i
c
a
l
P
e
r
fo
r
m
a
n
c
e
I
n
d
i
c
a
to
r
T
he
t
ec
hni
c
a
l
per
f
or
m
anc
e
i
nd
i
c
at
or
us
e
d
i
n
t
h
i
s
s
t
ud
y
i
s
t
h
e
P
er
f
or
m
anc
e
R
at
i
o
(
P
R
)
.
T
he s
t
eps
t
o
w
ar
ds
d
et
er
m
i
ni
n
g t
h
e P
R
of
t
he
des
i
gn a
r
e ex
pl
a
i
n
ed
bel
o
w
.
S
t
ep
1:
D
et
er
m
i
ne
t
he
F
i
n
al
Y
i
el
d,
Y
F
i
n
k
W
h
f
or
t
he
pl
a
nt
and
t
h
e
r
educ
t
i
on
f
ac
t
or
due
t
o t
em
per
at
ur
e,
f
t
e
m
p
_a
v
e
us
i
ng
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN
:
25
02
-
4
752
I
J
E
EC
S
V
o
l.
8
,
N
o.
1
,
O
c
t
o
ber
20
17
:
1
69
–
1
76
174
=
(
_
100
0
)
×
×
_
×
×
×
_
_
=
1
+
100
(
−
25
)
w
her
e
P
S
H
i
s
P
eak
S
un
H
our
i
n ho
ur
,
f
d
ir
i
s
di
r
t
f
ac
t
or
c
ons
i
der
i
n
g d
i
r
t
a
nd du
st
ac
c
u
m
ul
at
i
on
on
P
V
m
odul
es
an
d
f
a
g
e
i
s
t
he
a
gi
n
g f
ac
t
or
of
t
he
P
V
m
odul
e.
n
p
v
_
i
nv
i
s
t
he
ex
pec
t
e
d
c
abl
i
ng ef
f
i
c
i
enc
y
f
r
o
m
t
he P
V
ar
r
a
y
t
o i
n
v
er
t
er
.
S
t
ep
2:
D
et
er
m
i
ne t
he
ac
t
u
al
r
at
ed p
o
w
er
of
t
he
P
V
ar
r
a
y
,
P
arr_stc
in
W
.
_
=
_
×
_
×
_
S
t
ep
3:
D
et
er
m
i
ne t
he
ex
p
ec
t
ed a
nnu
al
S
p
ec
i
f
i
c
Y
i
el
d
,
SY
of
t
he
pl
ant
i
n k
W
h per
k
W
p.
=
_
10
00
⁄
S
t
ep
4:
D
et
er
m
i
ne t
he
P
er
f
or
m
anc
e R
at
i
o,
PR
of
t
he p
l
ant
i
n
%
.
=
_
100
0
⁄
×
1
PR
i
s
t
he
r
a
t
i
o
of
t
he
ex
pe
c
t
ed
en
er
g
y
ou
t
put
f
r
o
m
t
he
s
y
s
t
em
w
i
t
h
r
es
pec
t
t
o
t
h
e
i
d
ea
l
ener
g
y
o
ut
p
ut
i
s
t
heor
e
t
i
c
a
l
l
y
a
v
a
i
l
abl
e
[1
0
,
1
1]
.
2.
3.
2
C
u
c
k
o
o
S
e
a
r
c
h
(C
S
)
A
l
g
o
r
i
th
m
Define the Cuckoo Search
(
CS
)
parameters
Initialize the initial host nest
/
old population
Evaluate the fitness of old population
Modify the old population using Levy Flight
to generate new population
Evaluate the fitness of new population
Choose current best
t
<
max
_
gen
Start
End
Yes
No
Select number of worst population using Pa
and the rest are abandon population
Modify the selected worst population using
Levy Flight
Evaluate the fitness of worst population
Combine worst population
,
before and after
Levy Flight
Choose the best population using Pa
Combine the best population with the
abandon population
F
i
gur
e
1
.
F
l
o
w
c
har
t
of
s
i
z
i
n
g opt
i
m
i
z
at
i
on
us
i
n
g C
uc
k
oo S
e
ar
c
h (
C
S
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
EC
S
IS
S
N
:
2
502
-
4
752
S
i
z
i
ng O
pt
i
m
i
z
at
i
on
of
L
ar
g
e
-
Sc
a
l
e
G
r
id
-
C
o
nn
ec
t
ed
…
(
Muha
mma
d Z
ak
y
i
z
z
u
dd
i
n
B
i
n
R
)
175
F
i
gur
e
1 s
ho
w
ed t
he f
l
o
w
c
h
ar
t
of
s
i
z
i
ng
opt
i
m
i
z
at
i
on us
i
ng
C
S
al
gor
i
t
hm
.
Lat
er
,
C
S
al
g
or
i
t
hm
w
i
l
l
be
us
ed
as
a
c
o
m
par
i
s
on i
n t
er
m
of
t
ec
hni
c
al
per
f
or
m
anc
e
i
ndi
c
at
or
w
i
t
h
t
he
I
S
A
m
et
hod.
3
.
R
e
s
u
l
t a
n
d
D
i
s
c
u
s
s
i
o
n
3
.
1
M
a
x
i
m
i
z
i
n
g
P
e
r
fo
r
m
a
n
c
e
R
a
ti
o
T
hi
s
s
ec
t
i
on des
c
r
i
bes
t
he
r
es
ul
t
s
of
G
C
P
V
s
y
s
t
em
des
i
gn us
i
ng I
S
A
i
n m
ax
i
m
i
z
i
ng P
R
.
T
he
per
f
or
m
anc
e
of
I
S
A
i
n
m
ax
i
m
i
z
i
ng
P
R
i
s
s
ho
w
n
i
n
T
abl
e
1
.
T
he
t
ot
a
l
c
om
put
at
i
on
t
i
m
e
or
el
a
ps
ed
t
i
m
e
w
as
f
oun
d t
o
be
3,
11
5.
9
987
s
ec
onds
w
h
i
l
e t
he
m
ax
i
m
u
m
P
R
w
as
di
s
c
ov
er
ed
t
o
be
92.
0
872
2 %
us
i
ng
P
V
m
odul
e c
o
de
21 a
nd
i
n
v
er
t
er
c
o
de 1
0.
T
he opt
i
m
al
P
R
obt
ai
n
ed
i
n I
S
A
i
s
us
ed as
b
enc
hm
ar
k
f
or
t
he s
i
z
i
ng
al
g
or
i
t
hm
s
us
i
ng C
I
s
at
l
at
er
s
t
ag
e.
T
abl
e
1
.
S
i
z
i
ng
R
es
ul
t
of
l
ar
ge
-
s
c
al
e G
C
P
V
S
y
s
t
em
bas
ed
on P
er
f
or
m
anc
e R
at
i
o (
P
R
)
us
i
ng
I
S
A
S
i
z
i
ng par
a
m
et
er
s
V
al
ue
P
V
m
odul
e c
ode,
x
1
21
I
nv
er
t
er
c
ode,
x
2
10
N
s
_pv
,
i
n i
nt
eger
17
N
p_pv
,
i
n
i
n
t
eger
1
N
bal
,
i
n
i
nt
eger
4
X
i
,
i
n i
nt
eger
1,
131
N
P
V
_al
l
,
i
n
i
n
t
eger
19,
231
P
ar
r
_s
t
c
,
i
n
w
at
t
(
W
)
per
i
nv
er
t
er
4,
420
N
b_
v
er
t
,
i
n
i
nt
eger
505
N
b_hor
z
,
i
n i
n
t
eger
15
N
bl
oc
k
_P
V
,
i
n i
nt
eger
13
Y
F
(
kW
h
)
7,
144.
926
S
Y
(
k
W
h per
k
W
p)
1,
616.
499
P
R
(
%
)
92.
08722
E
l
aps
ed
T
i
m
e
,
t
(
s
)
3,
115.
9987
3
.
8
M
axi
m
i
z
a
ti
o
n
o
f P
e
r
fo
r
m
a
n
c
e
R
a
ti
o
u
s
i
n
g
C
u
c
k
o
o
S
e
a
r
c
h
O
p
ti
m
i
z
a
ti
o
n
A
l
g
o
r
i
th
m
(C
S
)
T
he
m
ax
i
m
i
z
at
i
on of
P
R
i
n
C
S
-
bas
e
d s
i
z
i
ng a
l
gor
i
t
hm
us
i
ng d
i
f
f
er
ent
popul
at
i
on s
i
z
e i
s
t
abu
l
at
ed i
n T
abl
e
2
.
T
he
m
a
x
i
m
u
m
P
R
ac
hi
ev
e
d
w
i
t
h al
l
p
opu
l
at
i
o
n s
i
z
es
i
s
92.
0813
2%
w
i
t
h 5
bec
om
es
t
he
m
i
ni
m
u
m
popul
at
i
o
n
s
i
z
e
d
i
s
c
ov
er
ed
f
or
t
he
CS
-
bas
e
d
s
i
z
i
ng
a
l
g
or
i
t
hm
.
I
n
s
hor
t
,
C
S
h
ad
f
ai
l
e
d
t
o
as
s
i
s
t
t
h
e
s
i
z
i
ng
al
gor
i
t
hm
i
n
ac
hi
e
v
i
ng
t
h
e
o
pt
i
m
al
P
R
a
l
t
h
oug
h
t
he
e
l
aps
e
d
t
i
m
e of
t
he opt
i
m
al
C
S
w
i
t
h
popu
l
at
i
o
n of
5
w
as
appr
ox
i
m
at
el
y
4 t
i
m
es
f
as
t
er
t
han I
S
A
.
T
abl
e
2
.
S
i
z
i
ng r
es
ul
t
s
us
i
n
g di
f
f
er
ent
num
ber
of
i
t
er
at
i
on
f
or
C
S
bas
ed
on
P
er
f
or
m
an
c
e R
at
i
o (
P
R
)
.
R
es
ul
t
s
N
um
ber
of
P
op
ul
at
i
ons
IS
A
5
10
15
20
25
x1
*
21
8
4
2
3
9
x2
#
10
10
10
10
10
10
N
s
_pv
,
i
n i
nt
e
ge
r
17
14
15
15
15
14
N
p_pv
, i
n i
nt
ege
r
1
1
1
1
1
1
N
bal
,
i
n i
nt
e
ge
r
4
0
0
9
7
7
X
i
,
i
n i
nt
e
ge
r
1,
1
31
1,
1
71
1,
1
30
1,
1
69
1,
1
49
1,
1
90
N
PV
a
l
l
,
i
n i
nt
eg
er
19,
231
16,
394
16,
950
17,
544
17,
242
16,
667
P
ar
r
s
t
c
,
i
n W
4,
4
20
4,
2
70
4,
4
25
4,
2
75
4,
3
50
4,
2
00
N
b_v
e
r
t
,
i
n i
nt
e
ge
r
505
524
375
518
518
524
N
b_hor
z
,
i
n i
nt
eg
er
15
172
14
14
14
172
N
bl
oc
k
_
pv
,
i
n
i
nt
e
ger
13
11
16
12
12
11
Y
F
,
i
n k
W
h
7,
1
44.
92
6
6,
9
02.
00
9
6,
9
26.
81
7
6,
9
10.
09
1
7,
0
31.
32
1
6,
7
88.
86
1
S
Y
,
i
n k
W
h per
kW
p
1,
6
16.
49
9
1,
6
16.
39
6
1,
5
65.
38
2
1,
6
16.
39
6
1,
6
16.
39
6
1,
6
16.
39
6
P
R,
in
%
92.
0
87
22
92.
081
3
2
92.
081
32
92.
081
32
92.
081
32
92.
081
32
E
l
aps
e
d T
i
m
e,
i
n
s
ec
o
nds
3,
1
15.
99
8
7
754
.
51
7
8
187
5.
3
75
1
331
6.
0
37
1
571
6.
5
13
5
854
6.
8
00
1
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN
:
25
02
-
4
752
I
J
E
EC
S
V
o
l.
8
,
N
o.
1
,
O
c
t
o
ber
20
17
:
1
69
–
1
76
176
4
.
C
o
n
c
l
u
s
i
o
n
T
hi
s
paper
pr
es
ent
ed
t
he s
i
z
i
ng opt
i
m
i
z
at
i
on of
l
ar
g
e
-
s
c
a
le
G
r
id
-
C
onn
ec
t
ed
(
G
C
P
V
)
s
y
s
t
em
us
i
ng
C
uc
k
oo
S
ear
c
h
(
C
S)
.
F
i
r
s
t
l
y
,
t
he
s
i
z
i
ng
al
gor
i
t
hm
i
ni
t
i
a
l
l
y
i
n
v
o
l
v
ed
t
he
dev
el
opm
ent
of
C
S
A
f
or
l
ar
ge
-
s
c
al
e G
C
P
V
s
y
s
t
em
c
ons
i
der
i
ng s
i
ngl
e s
et
of
s
y
s
t
e
m
c
o
m
ponent
s
f
or
opt
i
m
i
z
i
ng
a
per
f
or
m
an
c
e
i
ndi
c
at
or
f
or
t
he
des
i
gn.
N
ex
t
,
an
i
t
er
at
i
v
e
-
b
as
ed
s
i
z
i
ng
a
l
gor
i
t
hm
(I
S
A
)
w
as
de
v
e
l
o
ped
w
i
t
h
c
ons
i
der
a
t
i
o
n
of
d
i
f
f
er
ent
p
os
s
i
bl
e
s
et
s
of
P
V
m
odul
e
s
and
i
n
v
er
t
er
s
w
hi
c
h
w
er
e
s
t
or
ed
i
n a
c
om
pone
nt
dat
abas
e.
T
he I
S
A
i
s
c
apab
l
e
of
s
ear
c
hi
n
g f
or
t
he o
pt
i
m
al
s
et
of
P
V
m
odul
e and
i
n
v
er
t
er
t
hat
pr
od
uc
es
t
he o
pt
i
m
al
per
f
or
m
anc
e of
t
h
e pr
os
pec
t
i
v
e G
C
P
V
s
y
s
t
em
.
T
he
r
es
ul
t
s
f
r
o
m
I
S
A
w
er
e
t
hen
us
ed
as
be
n
c
h
m
ar
k
f
or
t
he
CS
-
bas
ed
s
i
z
i
ng
al
gor
i
t
hm
.
A
t
l
a
t
er
s
t
age,
C
S
-
bas
e
d s
i
z
i
ng a
l
g
or
i
t
hm
w
as
de
v
e
l
o
ped t
o d
et
er
m
i
ne t
he o
pt
i
m
al
s
et
o
f
P
V
m
odul
e a
nd
i
n
v
er
t
er
t
h
at
pr
oduc
es
t
he
op
t
i
m
al
p
er
f
or
m
a
nc
e of
t
he pr
os
pec
t
i
v
e
la
r
g
e
-
sca
l
e
G
C
PV
s
y
s
t
e
m
.
T
he
r
es
ul
t
s
s
how
ed
t
ha
t
I
S
A
w
as
bet
t
er
t
han
C
S
i
n
t
er
m
of
pr
oduc
i
ng
o
pt
i
m
u
m
f
i
t
nes
s
v
a
l
ue.
H
o
w
ev
er
,
C
S
w
as
bet
t
er
t
h
an I
S
A
i
n
pr
o
duc
i
n
g l
o
w
er
c
om
put
at
i
on t
i
m
e.
5.
A
c
kn
o
w
l
ed
g
em
en
t
T
hi
s
w
or
k
w
as
s
upp
or
t
ed
i
n
par
t
b
y
t
he F
und
am
ent
al
R
es
ear
c
h G
r
ant
S
c
hem
e (
F
R
G
S
)
,
Mi
n
i
s
t
r
y
of
E
duc
at
i
o
n (
R
ef
:
600
-
R
MI
/
F
R
G
S
5/
3 (
120
/
201
5)
)
and
U
ni
v
er
s
i
t
i
T
ek
nol
ogi
M
A
R
A
(
U
i
T
M)
Mal
a
y
s
i
a.
R
ef
er
en
ces
[
1
]
I.
D
i
n
c
e
r
.
R
enew
abl
e
en
er
gy
a
nd
s
us
t
ai
n
abl
e
de
v
e
l
o
pm
e
nt
:
a
c
r
uc
i
al
r
e
v
ie
w
.
R
enew
.
S
u
s
t
a
i
n.
E
ner
g
y
Re
v
.
2
000
;
4
(
2
):
157
–
175
,
.
[2
]
S
.
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,
A
.
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.
O
m
ar
,
S
.
I
.
S
ul
ai
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an,
A
.
H.
Ha
r
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s
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hot
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ol
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ai
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y
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t
.
M
al
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i
a G
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een T
ec
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gy
C
or
por
at
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on
.
201
3.
[3
]
S
. I. S
ul
ai
m
an
.
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nt
e
l
l
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gent
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i
z
i
ng and O
ut
p
ut
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i
on T
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or
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r
i
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onn
ec
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ed P
hot
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ys
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e
m
.
U
n
i
v
er
s
i
t
i
T
ek
n
ol
og
i
M
ar
a (
U
i
T
M
)
.
2012.
[4
]
S.
I
.
Su
l
a
i
m
a
n
,
S.
Sh
a
a
ri
,
A.
M
.
O
m
a
r.
S
ol
ar
I
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r
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di
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t
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on D
a
t
a f
or
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al
ay
s
i
a
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t
.
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us
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ai
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a
bl
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ev
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opm
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Au
t
h
o
r
i
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y
M
a
l
a
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s
i
a
(SED
A)
.
20
12.
[5
]
B
. P
a
r
i
d
a
, S
. In
i
y
a
n
,
R
.
G
o
i
c
.
A
r
ev
i
ew
of
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ol
ar
phot
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ai
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t
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ogi
es
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S
us
t
a
i
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ner
g
y
Re
v
.
2
011
;
15
(
3
)
:
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25
–
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636
.
[6
]
S
. E
. D
. A
. M
a
l
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a
.
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ui
de
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er
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i
na
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o
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of
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he S
us
t
ai
nabl
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ner
gy
D
e
v
el
op
m
en
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A
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M
al
ay
s
i
a
.
201
1
;
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: 1
–
32
.
[7
]
S.
E.
D
.
A.
M
.
(SED
A).
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A M
a
l
a
y
s
i
a
G
ri
d
-
C
onne
c
t
e
d P
hot
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ol
t
ai
c
S
y
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es
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gn C
our
s
e
.
1
s
t
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S
us
t
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l
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D
ev
el
op
m
ent
A
ut
hor
i
t
y
M
al
ay
s
i
a (
S
E
D
A
).
2014
.
[8
]
S.
Sh
a
a
r
i
,
A.
M
.
O
m
a
r,
A.
H
.
H
a
ri
s
,
S.
I
.
Su
l
a
i
m
an.
S
ol
ar
P
hot
ov
ol
t
a
i
c
P
ow
er
:
D
es
i
g
ni
ng
G
r
i
d
-
C
onnec
t
ed S
y
s
t
em
s
.
K
e
m
en
t
e
r
i
an T
enag
a,
T
ek
n
ol
og
i
H
i
j
au d
an A
i
r
,
P
ut
r
aj
ay
a
.
20
10.
[9
]
H
ei
nr
i
c
h H
ab
er
l
i
n.
P
h
ot
ov
ol
t
a
i
c
s
S
y
s
t
em
D
es
i
gn &
P
r
ac
t
i
c
e
.
2’
nd.
J
ohn
W
i
l
ey
&
S
on
s
,
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d,
T
he
A
t
r
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um
,
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out
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n G
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h
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t
S
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ex
P
019 85Q
,
U
ni
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ed K
i
ng
dom
,
201
2.
[
10]
N
. I. A
. A
z
i
z
.
F
i
r
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l
y
A
l
gor
i
t
h
m
f
or
O
pt
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m
al
S
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z
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ng o
f
S
t
an
d
-
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one P
h
ot
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ai
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S
y
s
t
em
.
U
ni
v
er
s
i
t
i
T
ek
nol
og
i
M
ar
a (
U
i
T
M
)
.
2016.
[
11]
N
.I.
A
b
d
u
l
A
z
i
z
,
S
.I
.
S
u
l
a
i
m
a
n
,
S
.
S
h
a
a
r
i
,
I.
M
u
s
i
r
i
n
,
K
.
S
opi
an
.
O
p
ti
m
al
s
i
z
i
n
g
of
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t
and
-
al
o
ne
phot
ov
ol
t
ai
c
s
y
s
t
e
m
by
m
i
ni
m
i
z
i
ng t
he l
o
s
s
of
pow
er
s
up
pl
y
pr
obab
i
l
i
t
y
.
So
l
a
r En
e
rg
y
.
2
017
;
150
(
1
):
220
-
22
8.
[
12]
S
.I.
S
u
l
a
i
m
a
n
,
T
.K
.A
.
R
a
h
m
a
n
,
I.
M
u
s
i
r
i
n
,
S
.
S
h
a
a
r
i
,
K
.
S
opi
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.
A
n
i
nt
e
l
l
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gent
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et
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od f
or
s
i
z
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n
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opt
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m
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o
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n gr
i
d
-
c
o
nne
c
t
e
d phot
ov
ol
t
ai
c
s
y
s
t
em
.
So
l
a
r En
e
rg
y
.
201
2
;
86
(
7
):
2
067
-
2082
.
[
13]
S
.
I.
S
u
l
a
i
m
a
n
,
T
.
K
.
A
.
R
a
h
m
a
n
,
I.
M
u
s
i
r
i
n
.
Mu
l
t
i
-
obj
ec
t
i
v
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ev
ol
ut
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on
ar
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pr
ogr
am
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i
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g
f
or
opt
i
m
a
l
g
ri
d
-
c
onn
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t
ed ph
ot
ov
ol
t
ai
c
s
y
s
t
em
de
s
i
gn
.
I
nt
er
nat
i
on
al
R
e
v
i
e
w
on
E
l
ec
t
r
ic
a
l
E
ng
i
n
eer
i
ng.
201
0
:
5
(6
):
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936
-
2
944
.
[
14]
S
. I. S
u
l
a
i
m
a
n
,
T
. K
. A
. R
a
h
m
a
n
,
I. M
u
s
i
r
i
n
.
N
ov
el
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nt
e
l
l
i
g
ent
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or
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ed
phot
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s
y
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t
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m
de
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I
nt
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nat
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R
ev
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on M
odel
l
i
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d S
i
m
ul
at
i
on
s
.
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0
;
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(
4
):
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-
652.
[
15]
M
.Z. R
o
s
s
e
l
an,
S
.
I
.
S
ul
ai
m
an
,
N
. O
th
m
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S
i
z
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i
m
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z
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on of
l
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i
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t
ed
phot
o
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ol
t
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s
y
s
t
em
u
s
i
ng
dol
phi
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c
ho
l
oc
at
i
on al
gor
i
t
hm
.
in
2017 9
th
I
nt
er
nat
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ona
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C
onf
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enc
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n
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om
put
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y
dney
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18
-
21 F
ebr
uar
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2017
l;
1
06
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11
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SC
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S
}
[
16]
M
.
Z
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R
os
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el
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I
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ul
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m
an
,
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.
i
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I
nt
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nat
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,
S
y
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,
18
-
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F
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uar
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017;
80
-
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.
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