I
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l J
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urna
l o
f
P
o
wer
E
lect
ro
nics
a
nd
Driv
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S
y
s
t
em
s
(
I
J
P
E
DS
)
Vo
l.
12
,
No
.
4
,
Dec
em
b
er
2
0
2
1
,
p
p
.
2
1
5
1
~
2
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I
SS
N:
2088
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8
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9
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.
1
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2151
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ttp
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A nov
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Deba
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R
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Sep
25
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2
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Oct
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,
2
0
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Th
e
m
icro
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rid
c
o
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p
t
p
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flex
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p
p
l
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t
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wh
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th
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v
e
n
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io
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l
g
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id
is u
n
a
b
le
to
su
p
p
ly
.
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h
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m
icro
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rid
stru
c
t
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b
a
se
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istri
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t
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s)
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n
d
th
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p
o
we
r
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two
r
k
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v
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rth
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s,
th
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p
o
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u
a
li
t
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(P
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t
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g
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in
th
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icro
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r
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o
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P
a
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c
u
larly
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h
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in
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o
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th
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ro
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t
h
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a
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wh
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d
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o
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s
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p
ro
p
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c
o
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tr
o
ll
e
r
is
a
n
imp
ro
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d
a
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ra
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N).
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h
e
v
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ri
o
u
s
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a
se
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ies
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k
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a
v
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b
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n
sim
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late
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with
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m
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rti
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-
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ra
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(P
I)
c
o
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ler.
He
n
c
e
in
th
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p
a
p
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r,
t
h
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ro
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u
stn
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ss
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p
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c
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tro
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a
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d
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th
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u
g
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iffere
n
t
situ
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ti
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s
.
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ey
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d
s
:
Ar
tific
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r
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etwo
r
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B
atter
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to
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Mic
r
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g
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id
Ph
o
to
v
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ll
Pro
p
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tio
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in
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r
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co
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tr
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T
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s
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p
e
n
a
c
c
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ss
a
rticle
u
n
d
e
r th
e
CC B
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SA
li
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se
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C
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p
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r
:
Su
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R
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Dep
ar
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g
in
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Un
iv
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s
ity
17
-
2
,
J
ay
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g
-
D
o
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g
,
Do
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g
-
Gu
,
Dae
jeo
n
-
3
4
6
0
6
,
R
ep
u
b
lic
o
f
Ko
r
ea
E
m
ail:
s
u
r
en
d
er
@
wsu
.
ac
.
k
r
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
co
n
v
en
tio
n
al
p
o
wer
g
e
n
e
r
atio
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r
eso
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ce
s
ar
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lim
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b
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s
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u
els
an
d
o
th
e
r
n
atu
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as,
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etc.
wh
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a
g
r
ea
t
im
p
ac
t
o
n
th
e
en
v
ir
o
n
m
e
n
t
th
r
o
u
g
h
p
o
llu
tio
n
.
T
h
u
s
,
th
e
in
teg
r
atio
n
o
f
r
en
ewa
b
l
e
en
er
g
y
s
o
u
r
ce
s
(
R
E
Ss
)
f
o
r
ce
d
in
to
th
e
co
n
v
en
tio
n
al
g
r
id
.
T
h
e
in
clu
s
io
n
o
f
R
E
Ss
lik
e
p
h
o
to
v
o
ltaic
(
PV)
,
d
iesel
g
en
er
ato
r
s
,
win
d
tu
r
b
in
es
(
W
T
s
)
,
s
m
all
h
y
d
r
o
p
o
wer
p
lan
ts
,
an
d
f
u
el
ce
lls
h
a
v
e
s
ig
n
if
ican
tly
c
h
an
g
e
d
th
e
m
icr
o
g
r
i
d
s
tr
u
ctu
r
e
an
d
th
e
AC
n
etwo
r
k
s
[
1
]
.
R
E
Ss
am
alg
am
atio
n
h
as
ch
an
g
ed
th
e
t
o
p
o
lo
g
ical
s
tr
u
ctu
r
e
o
f
th
e
p
o
wer
g
r
id
f
r
o
m
co
n
d
e
n
s
ed
g
en
er
atio
n
t
o
d
is
p
er
s
ed
p
r
o
d
u
ctio
n
,
esp
ec
ially
,
th
e
s
m
all
-
s
ca
le
g
en
er
atio
n
wh
ich
is
m
o
r
e
ac
ce
s
s
ib
le
to
t
h
e
lo
ad
p
a
n
els
[
2
]
.
I
n
v
ar
io
u
s
co
u
n
tr
ies
q
u
ality
o
f
p
o
wer
a
n
d
th
ei
r
co
n
s
is
ten
t
p
er
f
o
r
m
an
ce
o
f
u
n
ad
v
en
tu
r
o
u
s
d
is
tr
ib
u
tio
n
n
etwo
r
k
s
h
a
v
e
b
ee
n
d
em
ea
n
ed
.
So
,
th
e
n
o
ti
o
n
o
f
m
icr
o
g
r
id
(
MG
)
is
tak
en
in
to
co
n
s
id
er
atio
n
to
p
r
ev
en
t
th
ese
n
etwo
r
k
d
ef
ici
ts
[
3
]
,
[
4
]
.
T
h
e
g
en
e
r
al
ascr
ib
e
o
f
MG
ar
e
s
m
all
h
y
d
r
o
p
o
wer
p
lan
ts
,
en
er
g
y
s
t
o
r
ag
e
s
y
s
tem
s
,
PV,
win
d
tu
r
b
in
e,
etc.
[
5
]
-
[
7
]
.
MG
s
r
u
n
b
o
t
h
in
s
h
ield
ed
m
o
d
e
an
d
g
r
id
-
ass
o
ciate
d
m
o
d
e
[
8
]
.
T
h
er
e
is
a
v
ar
iatio
n
in
th
e
g
u
id
an
ce
r
eq
u
ir
em
en
ts
b
ase
d
o
n
th
e
m
o
d
e
o
f
o
p
er
atio
n
an
d
co
n
tr
o
lled
elem
en
ts
lik
e
DGs,
lo
ad
s
,
a
n
d
e
n
e
r
g
y
s
to
r
a
g
e
d
ev
ices
[
9
]
.
T
h
e
MG
s
ar
e
n
o
r
m
ally
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2088
-
8
694
I
n
t J
Po
w
E
lec
&
Dr
i
Sy
s
t,
Vo
l.
12
,
No
.
4
,
Dec
em
b
er
2
0
2
1
:
215
1
–
2
1
5
9
2152
class
if
ied
o
n
th
e
p
r
in
ci
p
le
o
f
g
r
id
in
clu
s
io
n
ty
p
e
a
n
d
in
v
er
t
er
ty
p
e
an
d
th
er
e
ar
e
o
th
e
r
ty
p
es
o
f
DGs
s
u
ch
as
g
as
tu
r
b
in
e,
m
icr
o
-
g
e
n
er
ato
r
,
an
d
in
ter
n
al
c
o
m
b
u
s
tio
n
e
n
g
in
e
-
b
ased
AC
g
r
id
wh
ic
h
is
co
n
n
ec
ted
to
th
e
co
n
v
en
tio
n
al
g
r
i
d
.
T
h
e
in
v
e
r
te
r
ty
p
es
o
f
DG
in
clu
d
e
a
p
o
we
r
elec
tr
o
n
i
cs
in
ter
f
ac
e
[
1
0
]
.
T
h
e
f
ast
d
etec
tio
n
o
f
v
ar
io
u
s
f
a
u
lts
in
th
e
is
o
lated
m
o
d
e
o
f
o
p
er
atio
n
with
o
u
t
lo
s
s
o
f
en
er
g
y
an
d
ac
h
ie
v
in
g
m
o
r
e
r
eliab
ilit
y
i
n
DC
m
icr
o
g
r
id
a
n
d
th
e
n
e
u
r
al
n
etw
o
r
k
is
tr
ain
ed
d
u
r
in
g
v
ar
io
u
s
f
au
lts
[
1
1
]
.
A
n
ew
ar
tific
ial
n
e
u
r
al
n
etwo
r
k
(
A
NN)
co
n
tr
o
l
h
as
b
ee
n
i
n
v
esti
g
ated
f
o
r
m
in
im
izi
n
g
th
e
p
o
wer
q
u
ality
is
s
u
es
with
f
ast
co
n
tr
o
l
an
d
im
p
r
o
v
e
d
r
eliab
ilit
y
an
d
th
e
r
esu
lts
wer
e
co
m
p
ar
ed
with
f
u
zz
y
PI
c
o
n
tr
o
ller
[
1
2
]
.
T
h
e
n
eu
r
al
n
etwo
r
k
is
u
s
ed
to
h
a
v
e
o
p
tim
u
m
v
o
ltag
e
an
d
to
e
x
tr
ac
t
u
ltima
te
p
o
w
er
an
d
im
p
r
o
v
em
en
t
o
f
ef
f
icien
cy
b
y
th
e
u
s
e
o
f
ANN
with
g
en
etic
alg
o
r
ith
m
(
GA)
wh
ic
h
in
cr
ea
s
es
co
n
v
e
r
g
en
ce
s
p
e
ed
[
1
3
]
.
T
h
e
lar
g
e
-
s
ca
le
b
atter
y
en
er
g
y
s
to
r
a
g
e
s
y
s
tem
s
(
B
E
SS
s
)
ar
e
in
teg
r
ated
in
to
a
m
icr
o
g
r
id
,
wh
ich
co
n
s
is
ts
o
f
a
f
ly
wh
ee
l,
win
d
tu
r
b
in
e
g
en
er
a
t
o
r
,
s
u
p
er
ca
p
ac
ito
r
,
a
n
d
m
a
g
n
etic
en
er
g
y
s
to
r
a
g
e
s
y
s
tem
[
1
4
]
-
[
1
6
]
.
T
h
e
MG
co
n
tr
o
l
s
tr
ateg
y
is
class
if
ied
as
ce
n
tr
alizin
g
an
d
d
ec
en
tr
alize
s
tr
ateg
ies
an
d
in
th
e
f
ir
s
t
ty
p
e,
th
e
en
tire
s
y
s
tem
is
r
eg
u
lated
b
y
th
e
ce
n
tr
al
co
n
tr
o
l,
a
n
d
in
th
e
s
ec
o
n
d
co
n
tr
o
l,
s
ch
em
e
b
a
s
ed
o
n
f
u
zz
y
lo
g
ic
an
d
PI
co
n
tr
o
ller
with
p
ar
ticle
s
war
m
o
p
tim
izatio
n
[
1
7
]
-
[
1
8
]
.
An
im
p
r
o
v
ed
s
lid
in
g
m
o
d
e
co
n
tr
o
ller
h
as
b
ee
n
im
p
lem
en
ted
f
o
r
f
r
e
q
u
en
c
y
co
n
tr
o
l
[
1
9
]
.
A
n
in
tellig
en
t
co
n
t
r
o
ller
in
a
b
atter
y
e
n
er
g
y
s
to
r
ag
e
s
y
s
tem
(
B
E
SS
)
f
o
r
m
ai
n
ten
an
c
e
o
f
p
o
wer
q
u
ality
is
s
u
es
is
p
r
o
p
o
s
ed
n
[
2
0
]
.
A
d
ec
e
n
tr
alize
d
co
n
tr
o
ll
er
is
u
s
ed
f
o
r
PQ
im
p
r
o
v
em
e
n
t
b
y
en
h
an
cin
g
t
h
e
co
n
v
er
ter
ef
f
icien
cy
in
a
n
AC
-
DC
m
icr
o
g
r
id
[
2
1
]
.
A
ty
p
e
-
2
Fu
zz
y
PID
co
n
tr
o
ller
h
as
b
ee
n
im
p
lem
e
n
ted
in
ass
o
ciatio
n
with
im
p
r
o
v
ed
GW
O
Op
tim
i
s
atio
n
tech
n
iq
u
es.
A
f
u
zz
y
co
n
tr
o
ller
p
er
f
o
r
m
ed
well
in
a
s
u
p
er
v
is
o
r
y
m
u
lti
-
ag
e
n
t
s
y
s
tem
f
o
r
f
r
eq
u
en
cy
co
n
tr
o
l
in
a
m
icr
o
g
r
id
[
2
2
]
-
[
2
3
]
.
Var
io
u
s
o
p
tim
ized
co
n
tr
o
ller
s
lik
e
s
l
id
in
g
m
o
d
e;
d
r
o
o
p
co
n
tr
o
l
etc
h
av
e
b
ee
n
a
p
p
lied
f
o
r
n
an
o
-
g
r
id
ap
p
licatio
n
an
d
h
o
m
e
au
to
m
at
io
n
in
r
ea
l
-
tim
e
a
p
p
licatio
n
s
[
2
4
]
-
[
2
8
]
.
I
n
th
e
ab
o
v
e
liter
atu
r
e
,
th
e
co
n
tr
o
ller
s
u
s
ed
a
r
e
n
o
t
r
o
b
u
s
t
to
h
an
d
le
th
e
n
o
n
lin
ea
r
ity
an
d
u
n
ce
r
tain
ties
.
E
v
en
if
t
h
e
ANN
u
s
ed
in
th
e
liter
atu
r
e
s
tu
d
y
h
as
lo
w
tr
ain
in
g
ca
p
ac
ity
d
u
r
i
n
g
th
e
f
a
u
lts
.
T
h
u
s
,
in
th
is
p
ap
er
,
th
e
p
er
f
o
r
m
an
c
e
o
f
th
e
n
o
n
lin
ea
r
a
u
to
r
e
g
r
ess
iv
e
ex
o
g
en
o
u
s
m
o
d
el
(
NARX)
is
in
v
esti
g
ated
in
th
e
ca
s
e
o
f
a
PV
ce
ll
-
b
ased
m
icr
o
g
r
id
in
teg
r
ated
with
B
E
SS
.
T
h
e
p
o
wer
q
u
ality
is
s
u
es
h
av
e
b
ee
n
im
p
r
o
v
ed
b
y
th
e
u
s
e
o
f
NARX,
th
r
o
u
g
h
,
th
e
lin
e
to
g
r
o
u
n
d
L
G
f
au
lt,
v
o
ltag
e
s
ag
/s
well,
u
n
b
alan
ce
d
c
o
n
d
itio
n
,
a
n
d
lin
e
im
p
ed
an
ce
f
a
u
lt
[
2
9
]
-
[
3
0
]
.
T
h
e
r
est
p
ar
t
o
f
th
e
p
a
p
er
is
o
r
g
an
ized
as
f
o
llo
ws.
Pro
b
lem
f
o
r
m
u
latio
n
is
d
is
cu
s
s
ed
in
s
ec
tio
n
2
.
T
h
e
MG
ar
ch
itectu
r
e
with
v
ar
i
o
u
s
co
m
p
o
n
en
ts
is
d
is
cu
s
s
ed
in
s
ec
tio
n
3
.
T
h
e
PI
co
n
tr
o
ller
an
d
NARX
s
tr
u
ctu
r
e
ar
e
d
escr
ib
ed
in
s
ec
tio
n
4
.
T
h
e
co
m
p
r
eh
en
s
iv
e
s
im
u
latio
n
r
esu
lts
ar
e
d
is
cu
s
s
ed
in
s
ec
tio
n
5
an
d
t
h
e
co
n
clu
s
io
n
an
d
f
u
tu
r
e
s
co
p
e
ar
e
g
iv
en
in
s
ec
tio
n
6.
2.
P
RO
B
L
E
M
F
O
R
M
U
L
AT
I
O
N
I
n
th
e
p
o
wer
s
y
s
tem
,
th
e
in
teg
r
atio
n
o
f
p
o
wer
elec
tr
o
n
ics d
ev
ices g
en
er
ates h
ar
m
o
n
ics an
d
d
ec
r
ea
s
es
th
e
p
o
wer
q
u
ality
(
PQ)
.
T
h
e
FAC
T
d
ev
ices
l
ik
e
D
-
STA
T
C
OM
ar
e
also
u
s
ed
to
r
ed
u
ce
th
e
p
o
wer
PQ
p
r
o
b
lem
s
f
o
r
th
e
u
tili
ty
.
T
h
e
m
ajo
r
p
r
o
b
lem
is
to
ac
h
iev
e
p
o
wer
q
u
alities
with
in
th
eir
s
p
ec
if
ied
r
an
g
es.
T
h
e
ac
ce
p
tab
le
r
an
g
es
s
u
ch
as
v
o
ltag
e
d
ev
iatio
n
ar
e
with
in
1
0
%,
ch
an
g
e
in
f
r
eq
u
e
n
cy
is
w
ith
in
0
.
1
Hz,
to
tal
h
ar
m
o
n
ic
d
is
to
r
tio
n
is
ar
o
u
n
d
5
%
an
d
th
e
p
o
wer
f
ac
to
r
is
to
b
e
g
r
ea
ter
th
an
0
.
9
.
T
h
u
s
,
t
o
ac
h
iev
e
th
e
ab
o
v
e
r
eq
u
ir
em
e
n
t,
an
ef
f
icien
t
a
n
d
r
eliab
le
co
n
tr
o
ller
is
to
b
e
f
o
r
m
u
lated
an
d
in
v
esti
g
ated
t
o
m
ain
tain
h
ea
lth
y
p
o
wer
q
u
ality
in
eith
e
r
m
o
d
e
o
f
wo
r
k
in
g
.
T
h
e
f
o
llo
win
g
ex
p
r
ess
io
n
s
h
av
e
b
ee
n
u
s
ed
to
a
cc
ess
th
e
PQ
is
s
u
es
in
th
e
s
u
g
g
ested
MG
to
p
o
lo
g
y
.
T
h
e
R
MS
v
al
u
e
o
f
v
o
ltag
e
ca
n
b
e
ex
p
r
ess
ed
in
s
am
p
le
p
e
r
c
y
cle
is
s
h
o
wn
as
:
=
√
1
∑
+
−
1
−
1
(
1
)
I
n
(
1
)
,
‘
m
’
is
th
e
f
u
n
d
am
en
tal
v
o
ltag
e
s
am
p
le
in
a
cy
cle.
V
j
is
th
e
j
th
s
am
p
le
v
o
ltag
e
V
i
rms
is
th
e
i
th
s
am
p
le
R
MS
v
o
ltag
e.
T
h
e
p
h
a
s
e
d
if
f
er
en
ce
b
etwe
en
p
h
ase
a
n
d
R
MS
v
o
ltag
e
is
(
m
-
1
)
s
am
p
le.
It
ca
lcu
lates th
e
tr
u
e
R
MS
v
alu
e
d
u
e
to
s
am
p
l
in
g
tech
n
iq
u
es.
T
h
e
v
o
ltag
e
s
ag
/s
well
ar
e
m
o
s
t
co
m
m
o
n
ty
p
es
o
f
s
im
ilar
ly
th
e
to
tal
h
ar
m
o
n
ic
d
is
to
r
tio
n
is
m
a
th
em
atica
lly
ca
lcu
lated
as
:
THD
v
=
√
∑
V
i
2
n
i
−
2
V
1
=
√
V
rms
2
−
V
i
2
V
1
(
2
)
THD
i
=
√
I
rms
2
−
I
i
I
1
(
3
)
T
h
e
v
o
ltag
e
s
ag
,
s
well,
an
d
h
a
r
m
o
n
ics
ar
e
th
e
m
o
s
t
co
m
m
o
n
ty
p
es
o
f
p
o
wer
q
u
alities
th
at
ap
p
ea
r
in
th
e
m
icr
o
g
r
i
d
s
.
I
n
th
e
ca
s
e
o
f
MG
o
p
er
atio
n
,
t
h
ese
p
o
wer
q
u
ality
is
s
u
es
m
ay
b
e
en
h
a
n
ce
d
b
y
u
s
in
g
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2
0
8
8
-
8
694
A
n
o
ve
l a
r
tifi
cia
l n
eu
r
a
l n
etw
o
r
k
fo
r
p
o
w
er q
u
a
lity imp
r
o
ve
men
t in
A
C
micro
g
r
id
(
Deb
a
n
i
P
r
a
s
a
d
Mis
h
r
a
)
2153
p
r
o
p
o
s
ed
co
n
tr
o
ller
f
o
r
v
ar
i
o
u
s
DE
R
s
.
I
n
th
is
wo
r
k
,
th
e
p
r
o
p
o
s
ed
co
n
tr
o
ller
is
im
p
lem
en
t
ed
in
th
e
co
m
m
o
n
in
v
er
ter
o
f
all
DGs.
T
h
e
p
r
o
j
ec
ted
m
eth
o
d
co
u
ld
b
e
test
ed
b
y
th
e
co
n
n
ec
tio
n
o
f
r
ec
tifie
r
lo
ad
s
an
d
q
u
ick
r
em
o
v
al
o
f
a
h
ea
v
y
lo
a
d
.
3.
M
I
CRO
G
R
I
D
ARCH
I
T
E
C
T
UR
E
T
h
e
MG
co
m
p
r
is
es
d
is
tr
ib
u
ted
r
en
ewa
b
le
en
er
g
y
g
en
e
r
at
o
r
s
DGs
lik
e
p
h
o
to
v
o
ltaic
ce
ll
PV
an
d
B
E
SS
s
.
All
th
e
DGs
ar
e
in
teg
r
ated
with
a
c
o
m
m
o
n
in
v
e
r
ter
an
d
it
is
co
n
n
ec
ted
to
PC
C
th
r
o
u
g
h
a
5
0
-
m
eter
lin
e.
DGs
h
av
e
th
eir
b
o
o
s
t
co
n
v
er
ter
to
s
tep
u
p
th
e
d
c
v
o
ltag
e
in
MG
o
p
e
r
atio
n
.
T
h
e
u
tili
ty
g
r
id
,
t
h
r
ee
-
p
h
ase
lo
ad
s
ar
e
co
n
n
ec
ted
to
PC
C
as d
is
p
lay
ed
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
MA
T
L
AB
/
Simu
lin
k
d
iag
r
am
o
f
th
e
PV
-
b
ased
m
icr
o
g
r
id
3
.
1
.
P
V
m
o
dellin
g
T
h
e
PV
ce
ll
g
en
er
atio
n
d
e
p
e
n
d
s
o
n
th
e
d
im
e
n
s
io
n
s
o
f
th
e
ar
r
ay
an
d
ir
r
ad
ian
ce
al
o
n
g
with
th
e
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
.
T
h
e
PV g
en
er
atio
n
o
u
tp
u
t is g
iv
e
n
b
y
[
3
1
]
.
=
(
4
)
w
h
er
e
P
pv
is
p
o
wer
g
e
n
er
atio
n
o
f
t
h
e
PV
m
o
d
el,
η
i
is
th
e
in
s
tan
t
ef
f
i
cien
c
y
o
f
th
e
PV
m
o
d
u
l
e,
A
m
is
th
e
a
r
ea
o
f
o
n
e
m
o
d
u
le
in
m
2
,
an
d
G
t
is
th
e
g
lo
b
al
ir
r
ad
ian
ce
in
ci
d
en
t
o
n
th
e
tilt
ed
p
lan
e
(
W
/m
2
)
.
T
h
e
PV
m
o
d
u
le
ef
f
icien
cy
is
g
iv
en
b
y
:
=
[
1
−
(
−
)
]
(
5
)
w
h
er
e
is
th
e
r
ef
er
en
ce
ef
f
icien
cy
,
η
p
t
e
is
p
o
wer
tr
ac
k
in
g
ef
f
i
cien
cy
(
1
f
o
r
m
ax
im
u
m
tr
ac
k
in
g
)
,
η
t
is
tem
p
er
atu
r
e
co
ef
f
icien
t,
t
c
is
PV
tem
p
er
atu
r
e
an
d
t
r
is
r
ef
e
r
en
ce
tem
p
er
at
u
r
e.
B
y
th
e
u
s
e
o
f
ANN,
th
e
MPP
v
o
ltag
e
is
f
o
r
ec
asted
an
d
th
e
co
n
v
er
ter
d
u
ty
c
y
cle
is
o
b
ta
in
ed
.
T
h
er
e
ar
e
th
r
ee
la
y
er
s
(
in
p
u
t,
o
u
tp
u
t,
a
n
d
h
id
d
en
)
in
ANN
[
1
8
]
.
T
h
e
in
p
u
t
lay
er
co
n
tai
n
s
2
n
eu
r
o
n
s
,
th
at
o
f
th
e
h
id
d
e
n
lay
e
r
is
1
0
an
d
th
e
r
e
is
o
n
ly
o
n
e
n
eu
r
o
n
in
th
e
o
u
tp
u
t
lay
er
.
T
h
e
m
ax
im
u
m
e
o
n
s
to
tr
ain
t
h
e
ANN
is
1
0
0
an
d
th
e
lea
r
n
i
n
g
r
ate
is
0
.
0
2
.
T
h
e
s
o
lar
PV d
ata
is
g
iv
en
in
T
a
b
le
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2088
-
8
694
I
n
t J
Po
w
E
lec
&
Dr
i
Sy
s
t,
Vo
l.
12
,
No
.
4
,
Dec
em
b
er
2
0
2
1
:
215
1
–
2
1
5
9
2154
T
ab
le
1.
Par
am
eter
s
o
f
s
o
lar
P
V
m
o
d
u
le
P
a
r
a
me
t
e
r
V
a
l
u
e
O
p
e
n
c
i
r
c
u
i
t
v
o
l
t
a
g
e
(
V
oc
)
3
2
.
9
2
v
o
l
t
C
u
r
r
e
n
t
a
t
s
h
o
r
t
c
i
r
c
u
i
t
(
I
sc
)
8
.
2
1
A
M
P
P
v
o
l
t
a
g
e
2
6
.
3
v
o
l
t
M
o
d
u
l
e
c
u
r
r
e
n
t
a
t
M
P
P
7
.
6
1
A
Te
mp
e
r
a
t
u
r
e
2
5
0
C
S
o
l
a
r
r
a
d
i
a
t
i
o
n
1
0
0
0
w
/
m
2
S
e
r
i
e
s PV
mo
d
u
l
e
s
8
P
a
r
a
l
l
e
l
P
V
m
o
d
u
l
e
s
63
M
a
x
i
m
u
m
p
o
w
e
r
o
f
P
V
m
o
d
u
l
e
(
P
m
)
2
0
0
w
a
t
t
3
.
2
.
B
E
SS
m
o
delin
g
I
n
g
e
n
er
al,
t
h
e
lead
-
ac
i
d
b
att
er
y
b
a
n
k
is
u
s
ed
in
th
e
MG
to
s
tr
o
n
g
ly
p
er
f
o
r
m
th
e
ch
a
r
g
in
g
an
d
d
is
ch
ar
g
in
g
p
r
o
ce
s
s
.
T
h
e
a
v
ailab
le
b
atter
y
b
a
n
k
ca
p
ac
ity
at
‘
t’
h
o
u
r
is
g
iv
en
b
y
:
=
(
−
1
)
(
1
−
)
±
[
−
(
)
(
)
]
(
6
)
I
n
(
6
)
,
C
bt
(
t)
a
n
d
C
bt
(t
-
1
)
a
r
e
th
e
watt
-
h
o
u
r
ca
p
ac
ity
o
f
th
e
b
atter
y
b
an
k
in
t
a
n
d
(
t
-
1
)
h
o
u
r
s
r
esp
ec
tiv
ely
.
η
i
n
v
an
d
ar
e
th
e
ef
f
i
cien
cies
o
f
in
v
e
r
ter
an
d
b
atter
y
r
esp
ec
tiv
ely
.
T
h
e
b
atter
y
S
OC
is
g
iv
en
by
:
=
0
−
1
∫
(
)
0
(
7
)
w
h
er
e
th
e
n
o
r
m
al
p
o
wer
o
f
t
h
e
b
atter
y
a
n
d
(
)
is
th
e
b
atter
y
cu
r
r
en
t.
T
h
e
b
atter
y
d
ata
u
s
ed
in
th
is
wo
r
k
is
g
iv
en
i
n
T
ab
le
2
.
T
ab
le
2
.
Par
am
eter
s
o
f
B
E
SS
s
S
.
N
o
.
S
i
z
e
(
A
h
)
Ef
f
i
c
a
c
y
(
%)
Le
a
s
t
C
h
a
r
g
e
(
%)
U
l
t
i
m
a
t
e
C
h
a
r
g
e
(
%)
U
l
t
i
m
a
t
e
d
i
s
c
h
a
r
g
e
r
a
t
e
H
i
g
h
e
s
t
c
h
a
r
g
e
r
a
t
e
01
2
1
6
0
85
20
80
2
0
k
w
-
4
0
k
w
4.
M
G
CO
NT
RO
L
L
E
RS
T
h
e
MG
s
tr
u
ctu
r
e
is
d
esig
n
ed
with
a
PV
ce
ll
an
d
b
atter
y
en
er
g
y
s
to
r
a
g
e
s
y
s
tem
in
MA
T
L
AB
/
Simu
lin
k
p
latf
o
r
m
.
T
h
e
in
teg
r
atio
n
o
f
r
en
ewa
b
le
DGs p
r
o
d
u
ce
s
n
o
n
lin
ea
r
ity
an
d
in
s
tab
ilit
y
wh
ich
r
ed
u
ce
s
th
e
p
o
wer
q
u
ality
is
s
u
es.
T
h
e
o
u
tp
u
t
o
f
an
y
r
e
n
ewa
b
le
-
b
ased
DE
R
is
n
o
n
lin
ea
r
a
n
d
f
lu
ctu
atin
g
.
T
o
o
b
tain
a
s
m
o
o
th
o
u
tp
u
t,
v
a
r
io
u
s
c
o
n
tr
o
l
s
ch
em
es
ar
e
u
s
ed
i
n
th
e
m
o
d
el.
Her
e
PID
co
n
tr
o
ller
,
o
p
tim
ized
PID
co
n
tr
o
ller
is
ap
p
lied
to
th
e
f
u
el
ce
ll in
v
er
ter
.
4
.
1
.
Co
nv
ent
i
o
na
l P
I
D
co
ntr
o
ller
T
h
e
PID
c
o
n
tr
o
ller
ca
n
b
e
o
p
er
ated
in
th
r
ee
m
o
d
es
s
u
ch
as
p
r
o
p
o
r
tio
n
al,
d
er
i
v
ativ
e
a
n
d
in
teg
r
al
a
n
d
is
o
n
e
o
f
lin
ea
r
co
n
tr
o
ller
wh
i
ch
also
co
n
tr
o
ls
th
e
n
o
n
li
n
ea
r
s
y
s
tem
s
with
lim
ited
ca
p
ac
ity
.
Ma
th
em
atica
lly
,
th
e
PID
co
n
tr
o
ller
is
g
i
v
en
b
y
:
(
)
=
(
)
+
∫
(
)
+
(
)
(
8
)
4
.
2
.
Art
if
ici
a
l neura
l net
wo
rk
(
ANN)
ANN
is
o
n
e
o
f
th
e
b
est
s
o
f
t
c
o
m
p
u
tin
g
m
eth
o
d
s
f
o
r
s
o
lv
in
g
n
o
n
lin
ea
r
p
r
o
b
lem
s
with
h
u
g
e
v
ar
iab
les.
T
h
e
o
u
tp
u
t u
n
it
o
f
th
e
f
ee
d
-
f
o
r
war
d
n
etwo
r
k
is
g
iv
en
b
y
:
=
(
∑
(
)
−
0
)
(
9
)
=
−
1
∑
(
−
1
)
=
0
=
1
,
2
,
…
(
1
0
)
wh
er
e
N,
H,
an
d
k
ar
e
th
e
d
im
en
s
io
n
s
o
f
th
e
in
p
u
t la
y
e
r
,
h
id
d
en
lay
er
,
an
d
o
u
tp
u
t la
y
er
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2
0
8
8
-
8
694
A
n
o
ve
l a
r
tifi
cia
l n
eu
r
a
l n
etw
o
r
k
fo
r
p
o
w
er q
u
a
lity imp
r
o
ve
men
t in
A
C
micro
g
r
id
(
Deb
a
n
i
P
r
a
s
a
d
Mis
h
r
a
)
2155
4
.
3
.
NARX
mo
del
T
h
e
NARX
is
d
ev
elo
p
e
d
f
r
o
m
a
s
et
o
f
u
n
co
n
n
ec
ted
-
tim
e
n
o
n
lin
ea
r
s
y
s
tem
s
with
th
e
n
o
n
lin
ea
r
au
to
r
eg
r
ess
iv
e
an
d
ex
o
g
en
o
u
s
in
p
u
t.
I
t
is
m
o
d
eled
with
th
e
n
o
n
lin
ea
r
a
n
d
ac
tiv
e
s
y
s
tem
i
n
clu
d
in
g
t
h
e
v
alu
es
o
f
th
e
o
u
tp
u
t
s
ig
n
al
a
n
d
it
d
e
p
en
d
s
o
n
th
e
in
p
u
t
s
ig
n
al
p
r
i
n
c
ip
le
with
th
e
p
ast
ac
tio
n
o
f
th
e
s
y
s
tem
[
1
9
]
.
T
h
e
NARX
s
y
s
tem
h
as
b
ee
n
im
p
lem
en
ted
in
tim
e
-
s
er
ies
m
o
d
elin
g
an
d
in
its
ad
ap
tiv
e
lear
n
in
g
p
r
o
ce
s
s
wh
ich
h
as
b
ee
n
wo
r
k
ed
s
u
cc
ess
f
u
lly
wit
h
s
m
all
-
s
ca
le
d
ata
[
2
0
]
.
T
h
e
n
u
m
er
ical
ex
p
r
ess
io
n
o
f
th
e
N
AR
X
m
o
d
el
is
g
i
v
en
by
:
(
)
=
(
(
−
)
,
(
−
−
1
)
,
…
,
(
−
−
)
,
(
−
1
)
,
…
,
(
−
)
)
(
1
1
)
w
h
er
e
y
(
t
)
an
d
u
(
t)
a
r
e
in
p
u
t
a
n
d
o
u
tp
u
t
s
ig
n
als
with
th
e
d
is
tin
ct
tim
e
s
tep
t.
≥
1
,
≥
1
,
≤
ar
e
th
e
m
em
o
r
y
o
r
d
e
r
s
o
f
th
e
in
p
u
t
a
n
d
o
u
tp
u
t
lay
e
r
,
an
d
F
is
a
n
o
n
lin
ea
r
m
ap
p
in
g
f
u
n
ctio
n
.
T
h
e
n
eu
r
al
n
etwo
r
k
is
ca
lled
NARX
wh
en
F
is
m
ea
s
u
r
ed
b
y
a
m
u
ltil
ay
er
p
er
ce
p
ti
o
n
(
ML
P),
ta
k
es
as
in
p
u
t
a
win
d
o
w
o
f
p
ast
in
d
ep
en
d
en
t
(
e
x
o
g
e
n
o
u
s
)
i
n
p
u
ts
an
d
p
ast
o
u
tp
u
ts
f
o
r
f
in
d
i
n
g
th
e
o
u
tp
u
t.
Her
e
x
is
ass
u
m
ed
to
b
e
th
e
s
tate
v
ar
iab
le
v
ec
to
r
an
d
x
i
(
t)
is
t
h
e
NARX’s
i
th
s
tate
v
ar
iab
le.
T
h
en
th
e
NARX
m
ay
b
e
b
ased
o
n
two
ta
p
p
ed
d
el
a
y
lin
es su
ch
as n
u
an
d
n
y
wh
ich
ar
e
u
p
d
ate
d
b
y
th
e
f
o
llo
win
g
l
aw.
(
+
1
)
=
{
(
−
)
=
(
)
=
+
+
1
(
)
1
≤
<
<
<
(
+
)
(
1
2
)
T
h
u
s
at
tim
e
‘
t’
th
at
c
o
r
r
esp
o
n
d
s
to
th
e
v
alu
e
is
g
i
v
en
b
y
(
1
3
)
-
(
1
5
)
,
(
)
=
[
(
−
−
1
)
,
…
,
(
−
−
)
,
…
,
(
−
1
)
,
…
,
(
−
)
]
(
1
3
)
T
h
e
ML
P
is
s
tr
u
ctu
r
ed
in
to
th
e
two
-
lay
er
ed
n
etwo
r
k
o
f
NARX.
N
k
is
th
e
h
id
d
en
lay
er
n
o
d
e
an
d
it
p
er
f
o
r
m
s
th
e
f
o
llo
win
g
f
u
n
ctio
n
.
(
)
=
(
+
1
)
=
[
∑
(
)
+
(
)
+
−
1
]
,
=
1
,
…
,
(
1
4
)
w
h
er
e
a
ij,
b
i,
c
i
ar
e
t
h
e
r
ea
l
-
v
al
u
ed
weig
h
ts
an
d
N.
(
)
=
∑
(
)
+
0
−
1
(
1
5
)
σ
is
th
e
h
id
d
en
n
eu
r
o
n
ac
tiv
a
t
io
n
f
u
n
ctio
n
a
n
d
a
p
p
r
o
x
im
at
ely
th
e
s
am
e
as
th
e
Hea
v
is
i
d
e
s
tep
f
u
n
ctio
n
.
Similar
to
tr
ad
itio
n
a
l
NARX
h
as
in
ad
eq
u
ate
r
esp
o
n
s
e
ap
p
r
o
ac
h
i
n
g
b
y
th
e
o
u
tp
u
t
n
eu
r
o
n
s
o
n
l
y
.
T
h
e
NARXo
u
tp
u
t
is
s
en
t
to
th
e
i
n
p
u
t
o
f
t
h
e
f
ee
d
f
o
r
war
d
n
eu
r
al
n
etwo
r
k
.
Ho
wev
er
,
it
h
as
b
ee
n
co
n
f
ir
m
ed
th
a
t
s
u
ch
a
n
e
u
r
al
n
etwo
r
k
is
h
a
v
in
g
h
ig
h
c
o
m
p
u
tatio
n
al
s
p
e
ed
an
d
is
tr
ain
ed
i
n
th
e
o
p
e
n
-
lo
o
p
s
y
s
tem
with
o
b
s
er
v
ed
tim
e
s
er
ies
d
ata.
T
h
is
is
u
n
d
er
s
to
o
d
th
at
th
is
n
etwo
r
k
is
tr
ain
ed
ju
s
t
lik
e
a
class
ic
AN
N
an
d
r
ec
u
r
s
io
n
o
u
tp
u
t.
I
n
th
is
wo
r
k
,
V
abc
,
I
abc
,
g
ate
p
u
ls
e
r
ef
er
en
c
e
s
ig
n
al,
an
d
f
r
eq
u
en
c
y
ar
e
tak
en
as
ex
o
g
en
o
u
s
in
p
u
t
a
n
d
th
eir
d
ata
s
et
is
tr
ain
ed
with
th
e
co
m
p
a
r
is
o
n
o
f
r
e
cu
r
r
en
t
in
p
u
t
lik
e
s
witch
in
g
f
r
eq
u
en
cy
.
Ho
wev
e
r
,
th
e
V
abc
, I
abc,
an
d
f
r
e
q
u
en
c
y
d
a
ta
s
et
is
tr
ain
ed
to
r
ed
u
ce
t
h
e
p
o
wer
q
u
ality
is
s
u
es.
5.
RE
SU
L
T
S
ANA
L
YS
I
S
Her
e
th
e
p
r
o
p
o
s
ed
co
n
tr
o
l
s
ch
em
e
is
ap
p
lied
to
a
m
icr
o
g
r
id
co
n
s
is
ts
o
f
PV
ce
ll
s
an
d
B
E
SS
.
T
h
e
v
ar
io
u
s
ca
s
e
s
tu
d
ies
h
av
e
b
e
en
s
im
u
lated
MA
T
L
AB
/SIM
UL
I
NK
en
v
ir
o
n
m
en
t
th
r
o
u
g
h
v
o
ltag
e
s
ag
/s
well,
u
n
b
alan
ce
d
co
n
d
itio
n
,
an
d
a
s
in
g
le
lin
e
to
g
r
o
u
n
d
f
a
u
lt.
T
h
e
m
o
d
el
is
s
im
u
lated
with
a
PID
co
n
tr
o
ller
an
d
NARX.
Fro
m
th
e
r
esu
lts
,
it
is
d
ep
icted
th
at
th
e
NARX
m
o
d
el
o
p
er
ates
b
etter
th
an
th
e
PID
co
n
tr
o
ller
in
th
e
v
ar
io
u
s
in
v
esti
g
atio
n
s
.
I
n
th
is
s
im
u
latio
n
,
th
e
PID
co
n
tr
o
ll
er
p
ar
am
eter
s
ar
e
s
elec
ted
as
k
p
is
3
1
1
.
2
2
4
,
k
i
is
0
.
3
2
1
an
d
k
d
is
4
3
.
1
1
2
.
5
.
1
.
P
o
wer
qu
a
lity
enha
ncem
ent
us
ing
NARX
m
o
del t
hro
ug
h v
o
lt
a
g
e
s
a
g
/s
well
T
h
e
v
o
ltag
e
s
ag
is
in
itiated
f
r
o
m
0
.
3
s
to
0
.
5
s
in
f
lu
e
n
ce
d
b
y
a
3
-
p
h
ase
f
a
u
lt
an
d
a
v
o
lta
g
e
s
well
is
cr
ea
ted
f
r
o
m
0
.
7
s
to
0
.
8
s
b
y
s
u
d
d
en
elim
in
atio
n
o
f
a
h
ea
v
y
lo
ad
.
I
n
th
is
ca
s
e,
th
e
s
u
g
g
est
ed
AC
m
icr
o
g
r
i
d
is
s
im
u
lated
f
o
r
0
.
9
s
an
d
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
PID
co
n
t
r
o
lle
r
is
s
h
o
wn
i
n
Fig
u
r
e
2
,
an
d
th
e
im
p
r
o
v
em
en
t
o
f
v
o
ltag
e
s
ag
/s
well
b
y
th
e
s
u
g
g
ested
co
n
tr
o
ller
h
as
b
ee
n
s
h
o
w
n
in
Fig
u
r
e
3
.
T
h
e
s
ag
an
d
s
well
h
av
e
b
ee
n
im
p
r
o
v
e
d
as
th
e
v
o
ltag
e
s
ag
f
r
o
m
1
0
0
V
to
2
0
0
V,
an
d
s
well
h
as
b
ee
n
im
p
r
o
v
e
d
f
r
o
m
3
2
0
V
to
2
2
5
V,
b
y
th
e
r
ec
o
m
m
en
d
ed
co
n
tr
o
ller
,
w
h
ich
is
p
r
esen
ted
in
Fig
u
r
e
3.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2088
-
8
694
I
n
t J
Po
w
E
lec
&
Dr
i
Sy
s
t,
Vo
l.
12
,
No
.
4
,
Dec
em
b
er
2
0
2
1
:
215
1
–
2
1
5
9
2156
Fig
u
r
e
2
.
Per
f
o
r
m
an
c
e
o
f
PID
co
n
tr
o
ller
in
v
o
ltag
e
s
ag
/s
well
Fig
u
r
e
3
.
Per
f
o
r
m
an
c
e
NARX in
v
o
ltag
e
s
ag
/s
well
5
.
2
.
P
o
wer
qu
a
lity
enha
ncem
ent
us
ing
NARX
t
hro
ug
h t
he
un
ba
la
nced
co
n
ditio
n
T
h
e
v
o
ltag
e
co
n
to
u
r
is
m
ain
t
ain
ed
d
u
r
in
g
u
n
b
alan
cin
g
in
Fig
u
r
e
4
an
d
th
e
u
n
b
ala
n
cin
g
is
in
itiated
f
r
o
m
0
.
3
s
to
0
.
5
s
a
n
d
th
e
m
o
d
el
is
s
im
u
lated
f
o
r
0
.
6
s
.
Du
r
in
g
u
n
b
alan
cin
g
co
n
d
itio
n
s
,
th
e
v
o
ltag
e
am
o
u
n
t
a
n
d
an
g
le
ar
e
r
e
g
u
lated
b
y
s
witch
in
g
o
f
in
v
er
te
r
p
u
ls
es.
T
h
e
p
u
ls
e
s
witch
in
g
co
n
tr
o
l
b
r
in
g
s
th
e
s
y
s
tem
in
to
b
alan
ce
co
n
d
itio
n
at
0
.
5
s
q
u
i
ck
ly
.
T
h
e
co
n
t
r
o
ller
o
u
tp
u
t
is
s
h
o
wn
in
Fig
u
r
e
5
an
d
it
is
s
u
p
er
io
r
to
th
e
PID
co
n
tr
o
ller
.
Fig
u
r
e
4
.
Per
f
o
r
m
an
c
e
o
f
PID
co
n
tr
o
ller
in
an
u
n
b
alan
ce
d
co
n
d
itio
n
Fig
u
r
e
5
.
Per
f
o
r
m
an
c
e
o
f
NA
R
X
co
n
tr
o
ller
in
an
u
n
b
alan
ce
d
co
n
d
itio
n
5
.
3
.
P
o
wer
qu
a
lity
enha
ncem
ent
us
ing
NARX
t
hro
ug
h
L
G
f
a
ult
A
s
in
g
le
lin
e
to
a
g
r
o
u
n
d
f
a
u
l
t
h
as
b
ee
n
in
itiated
b
etwe
en
0
.
3
s
to
0
.
5
s
,
an
d
th
e
m
o
d
el
is
s
im
u
lated
f
o
r
0
.
6
s
wh
ich
r
ed
u
ce
s
th
e
v
o
ltag
e
in
th
e
f
a
u
lty
p
h
ase
to
5
V
to
1
0
V.
As
s
h
o
w
n
in
Fig
u
r
e
6
,
d
u
r
i
n
g
L
G
f
a
u
lt
th
e
v
o
ltag
e
in
o
t
h
er
two
p
h
ase
s
r
em
ain
u
n
c
h
an
g
e
d
f
o
r
0
.
3
s
to
0
.
5
s
.
T
h
e
v
o
ltag
e
a
n
d
c
u
r
r
en
t
d
ata
d
u
r
in
g
th
e
f
au
lt
s
ar
e
tr
ain
ed
with
th
e
NARX m
o
d
el
wh
ich
im
p
r
o
v
es th
e
v
o
ltag
e
to
1
0
0
V
as sh
o
w
n
in
Fig
u
r
e
7
.
T
h
u
s
,
th
e
p
r
esen
tatio
n
o
f
th
e
p
r
o
p
o
s
ed
c
o
n
tr
o
ller
m
o
d
el
is
f
aster
an
d
e
f
f
icien
t th
an
th
e
co
n
v
en
tio
n
al
PID
co
n
tr
o
ller
.
Fig
u
r
e
6
.
p
er
f
o
r
m
an
ce
o
f
PID
co
n
tr
o
ller
d
u
r
in
g
L
G
f
au
lt
Fig
u
r
e
7
.
p
er
f
o
r
m
an
ce
o
f
NA
R
X
co
n
tr
o
ller
d
u
r
in
g
L
G
f
au
lt
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2
0
8
8
-
8
694
A
n
o
ve
l a
r
tifi
cia
l n
eu
r
a
l n
etw
o
r
k
fo
r
p
o
w
er q
u
a
lity imp
r
o
ve
men
t in
A
C
micro
g
r
id
(
Deb
a
n
i
P
r
a
s
a
d
Mis
h
r
a
)
2157
E
r
r
o
r
h
is
to
g
r
am
is
th
e
h
is
to
g
r
am
o
f
th
e
in
ac
c
u
r
ac
ies
am
o
n
g
tar
g
et
v
al
u
es
an
d
p
r
e
d
icted
v
alu
es
af
ter
tr
ain
in
g
a
f
ee
d
-
f
o
r
wa
r
d
n
e
u
r
al
n
etwo
r
k
.
As
th
ese
er
r
o
r
v
alu
es
s
h
o
w
h
o
w
esti
m
ated
v
alu
es
ar
e
v
ar
y
in
g
f
r
o
m
th
e
tar
g
et
v
alu
es,
h
en
ce
th
ese
ca
n
b
e
n
eg
ativ
e
.
B
in
s
ar
e
th
e
n
u
m
b
er
o
f
v
e
r
tical
b
ar
s
y
o
u
ar
e
n
o
ticin
g
o
n
th
e
g
r
ap
h
.
T
h
e
to
tal
er
r
o
r
r
a
n
g
e
is
d
iv
id
ed
in
to
3
0
s
m
aller
b
in
s
n
o
w.
Ax
is
r
ep
r
esen
ts
th
e
n
u
m
b
er
o
f
s
am
p
les
f
r
o
m
y
o
u
r
d
ataset,
wh
ich
lies
in
a
p
ar
ticu
lar
b
in
.
Fo
r
ex
am
p
le,
at
th
e
m
id
o
f
y
o
u
r
p
lo
t,
y
o
u
h
av
e
a
b
in
co
r
r
esp
o
n
d
in
g
to
t
h
e
er
r
o
r
o
f
0
.
0
0
1
5
0
2
a
n
d
th
e
h
ei
g
h
t o
f
t
h
at
b
in
f
o
r
t
h
e
tr
ain
in
g
d
ataset
lies
b
elo
w
b
u
t n
ea
r
to
1
5
0
,
an
d
th
e
v
alid
atio
n
an
d
test
d
ataset
l
ies
b
etwe
en
1
5
0
an
d
2
0
0
.
I
t
in
d
icate
s
th
at
s
ev
er
al
s
am
p
les
f
r
o
m
y
o
u
r
s
ev
er
al
d
atasets
h
av
e
an
in
ac
cu
r
ac
y
lies
in
th
at
s
u
b
s
eq
u
en
t
r
an
g
e.
Z
e
r
o
er
r
o
r
li
n
e
r
elate
d
to
th
e
ze
r
o
er
r
o
r
v
alu
e
o
n
th
e
er
r
o
r
ax
is
(
i.e
.
X
-
ax
is
)
.
I
n
th
is
ar
g
u
m
en
t
ze
r
o
er
r
o
r
p
o
in
t
f
alls
u
n
d
er
th
e
b
in
with
ce
n
ter
0
.
0
0
1
5
0
2
.
T
h
e
g
r
ap
h
ical
p
r
esen
tatio
n
o
f
th
e
er
r
o
r
h
is
to
g
r
a
m
o
f
th
e
NARX
m
o
d
el
i
s
d
e
p
icted
in
Fig
u
r
e
8
.
Fig
u
r
e
9
ex
p
lain
s
th
e
co
n
v
er
g
en
ce
o
f
th
e
NARX m
o
d
el
d
u
r
i
n
g
th
e
s
im
u
latio
n
.
Fig
u
r
e
8
.
MSE
h
is
to
g
r
am
u
s
in
g
ANN
alg
o
r
ith
m
Fig
u
r
e
9
.
Valid
atio
n
o
f
t
h
e
p
r
o
p
o
s
ed
NARX m
o
d
el
5
.
4
.
P
o
wer
qu
a
lity
enha
ncem
ent
us
ing
NARX
t
hro
ug
h
t
o
t
a
l ha
rmo
nic
dis
t
o
rt
io
n
T
h
e
FF
T
o
f
v
o
ltag
e
an
d
cu
r
r
en
t
at
P
C
C
is
in
v
est
ig
ated
in
th
is
ca
s
e.
T
h
e
to
tal
h
ar
m
o
n
ic
d
is
to
r
tio
n
(
T
HD)
o
f
PC
C
v
o
ltag
e
an
d
cu
r
r
en
t
ar
e
s
h
o
wn
in
Fig
u
r
e
s
1
0
to
1
3
.
T
h
e
T
HD
o
f
PC
C
v
o
ltag
e
an
d
cu
r
r
en
t
with
PID
co
n
tr
o
ller
is
8
.
9
7
%
a
n
d
9
.
6
7
%,
r
esp
ec
tiv
ely
,
an
d
th
at
o
f
v
o
ltag
e
an
d
cu
r
r
en
t
is
1
.
2
7
%
an
d
1
.
6
9
%
with
NARX c
o
n
tr
o
ller
.
T
h
u
s
,
th
e
N
AR
X
co
n
tr
o
ller
p
er
f
o
r
m
s
b
ett
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in
T
HD
ca
lcu
latio
n
.
Fig
u
r
e
1
0
.
FF
T
an
aly
s
is
o
f
PC
C
v
o
ltag
e
with
PID
co
n
tr
o
ller
Fig
u
r
e
1
1
.
FF
T
an
aly
s
is
o
f
PC
C
cu
r
r
en
t w
ith
PID
co
n
tr
o
ller
Fig
u
r
e
1
2
.
FF
T
An
aly
s
is
o
f
PC
C
v
o
ltag
e
with
NARX
co
n
tr
o
ller
Fig
u
r
e
1
3
.
FF
T
An
aly
s
is
o
f
PC
C
cu
r
r
en
t w
ith
NARX
co
n
tr
o
ller
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2088
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8
694
I
n
t J
Po
w
E
lec
&
Dr
i
Sy
s
t,
Vo
l.
12
,
No
.
4
,
Dec
em
b
er
2
0
2
1
:
215
1
–
2
1
5
9
2158
Her
e,
a
m
icr
o
g
r
id
m
o
d
el
h
as
b
ee
n
d
esig
n
ed
with
PV
ce
ll
a
s
a
DG,
a
n
d
co
n
v
er
ter
,
lo
ad
,
th
e
g
r
i
d
is
also
in
clu
d
ed
.
T
h
e
m
o
d
el
h
as
b
ee
n
s
im
u
lated
in
th
e
MA
T
L
AB
Simu
lin
k
en
v
ir
o
n
m
e
n
t
t
h
r
o
u
g
h
th
e
NARX
m
o
d
el
to
m
ain
tain
t
h
e
PQ
p
r
o
b
lem
s
.
Fro
m
th
e
s
im
u
latio
n
r
e
s
u
lts
,
it
is
n
o
ted
th
at
th
e
p
er
f
o
r
m
an
ce
o
f
NARX
is
s
u
f
f
icien
tly
b
etter
th
an
th
e
o
th
er
co
n
v
en
tio
n
al
co
n
tr
o
ller
s
lik
e
PID
.
Ho
wev
er
,
b
y
th
e
u
s
e
o
f
th
e
NARX
m
o
d
el
th
e
p
o
wer
q
u
ality
is
s
u
es
lik
e
v
o
ltag
e
s
ag
/s
well,
p
o
wer
f
ac
t
o
r
,
an
d
f
r
e
q
u
en
c
y
ar
e
m
ai
n
tain
ed
as
p
er
t
h
e
I
E
E
E
s
tan
d
ar
d
v
alu
es
.
6.
CO
NCLU
SI
O
N
I
n
th
is
wo
r
k
,
a
m
icr
o
g
r
id
s
tr
u
ctu
r
e
is
d
esig
n
ed
in
Ma
tlab
/Si
m
u
lin
k
p
latf
o
r
m
wh
ich
c
o
n
s
is
ts
o
f
a
PV
ce
ll
as
d
is
tr
ib
u
tio
n
g
en
e
r
ato
r
an
d
in
teg
r
ated
with
s
to
r
ag
e
s
y
s
tem
B
E
SS
s
.
T
h
e
PID
co
n
tr
o
l
ler
an
d
th
e
NARX
ar
e
ap
p
lied
t
o
im
p
r
o
v
e
t
h
e
q
u
ality
o
f
p
o
wer
in
a
th
r
ee
-
p
h
ase
AC
m
icr
o
g
r
id
.
T
h
e
p
r
o
jecte
d
ANN
-
b
ased
m
o
d
el
f
o
r
m
ain
tain
in
g
p
o
we
r
q
u
alit
y
is
s
u
es
is
p
r
esen
ted
in
d
etail
in
th
is
s
tu
d
y
.
I
n
th
is
p
ap
er
,
th
e
PQ
is
s
u
es
ar
e
m
o
n
ito
r
ed
t
h
r
o
u
g
h
v
o
ltag
e
s
ag
/s
well,
s
in
g
le
lin
e
to
g
r
o
u
n
d
f
au
lt,
an
d
an
u
n
b
alan
ce
d
co
n
d
itio
n
.
T
h
ese
v
ar
iab
les
ar
e
r
eg
u
lated
b
y
th
e
p
r
o
p
o
s
ed
NARX
m
o
d
el
an
d
it
is
r
ea
lized
th
at
th
e
n
ew
co
n
t
r
o
ller
im
p
lem
en
te
d
h
er
e
ev
e
n
if
in
a
ch
a
n
g
ed
en
v
ir
o
n
m
e
n
t
o
f
DGs
an
d
ir
r
esp
ec
tiv
e
o
f
th
e
g
eo
g
r
ap
h
ical
co
n
d
itio
n
o
f
th
e
MG
m
o
d
el
to
m
ai
n
tain
s
th
e
p
o
wer
q
u
ality
with
i
n
th
e
s
tan
d
a
r
d
v
alu
e.
T
h
e
ANN
lib
r
ar
y
is
u
s
ed
with
th
e
r
eq
u
ir
e
d
n
u
m
b
er
o
f
lay
er
s
an
d
n
e
u
r
o
n
s
in
ea
ch
lay
er
.
T
h
e
p
r
o
p
o
s
ed
co
n
tr
o
ller
is
d
u
ly
co
m
p
ar
ed
with
th
e
PID
co
n
tr
o
ller
s
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
is
r
esear
ch
wo
r
k
was
f
u
n
d
e
d
by
“
W
o
o
s
o
n
g
Un
iv
e
r
s
ity
’
s
Aca
d
em
ic
R
esear
ch
Fu
n
d
in
g
–
2
0
2
1
”
.
RE
F
E
R
E
NC
E
S
[1
]
T.
S
.
Us
tu
n
,
C.
Oz
a
n
so
y
a
n
d
A.
Z
a
y
e
g
h
,
“
Re
c
e
n
t
d
e
v
e
lo
p
m
e
n
ts
in
m
icro
g
rid
s
a
n
d
e
x
a
m
p
le
c
a
se
s
a
ro
u
n
d
t
h
e
wo
rld
-
A rev
iew
”
,
Ren
e
w.
S
u
sta
i
n
.
En
e
r
g
y
Rev
.
,
v
o
l.
1
5
,
n
o
.
8
,
p
p
.
4
0
3
0
–
4
0
4
1
,
Oc
t.
2
0
1
1
,
d
o
i:
1
0
.
1
0
1
6
/j
.
rse
r.
2
0
1
1
.
0
7
.
0
3
3
.
[2
]
N.
W.
A.
Li
d
u
la
a
n
d
A.
D.
R
a
jap
a
k
se
,
“
M
icro
g
r
id
s
re
se
a
rc
h
:
A
re
v
iew
o
f
e
x
p
e
rime
n
tal
m
icro
g
ri
d
s
a
n
d
tes
t
sy
ste
m
s”
,
Ren
e
w.
S
u
st
a
in
.
E
n
e
rg
y
Rev
.
,
v
o
l.
1
5
,
n
o
.
1
,
p
p
.
1
8
6
–
2
0
2
,
Ja
n
.
2
0
1
1
,
d
o
i
:
1
0
.
1
0
1
6
/j
.
rse
r.
2
0
1
0
.
0
9
.
0
4
1
.
[3
]
S
.
M
.
Ka
v
iri
,
M
.
P
a
h
lev
a
n
i,
P
.
Ja
i
n
a
n
d
A
.
Ba
k
h
sh
a
i,
“
A
re
v
iew
o
f
AC
m
icro
g
ri
d
c
o
n
tr
o
l
m
e
th
o
d
s,
”
2
0
1
7
IEE
E
8
t
h
In
ter
n
a
t
io
n
a
l
S
y
mp
o
siu
m
o
n
Po
we
r
El
e
c
tro
n
ics
fo
r
Distri
b
u
te
d
Ge
n
e
ra
ti
o
n
S
y
ste
ms
(PE
DG
)
,
2
0
1
7
,
p
p
.
1
-
8
,
d
o
i
:
1
0
.
1
1
0
9
/
P
EDG
.
2
0
1
7
.
7
9
7
2
4
9
8
.
[4
]
M
.
Kh
a
li
d
,
U.
Ak
ra
m
a
n
d
S
.
S
h
a
fiq
,
“
Op
ti
m
a
l
P
lan
n
in
g
o
f
M
u
lt
i
p
le
Distrib
u
ted
G
e
n
e
ra
ti
n
g
Un
it
s
a
n
d
S
t
o
ra
g
e
in
Ac
ti
v
e
Distrib
u
t
io
n
Ne
two
r
k
s,
”
in
IEE
E
Acc
e
ss
,
v
o
l.
6
,
p
p
.
5
5
2
3
4
-
5
5
2
4
4
,
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/ACCE
S
S
.
2
0
1
8
.
2
8
7
2
7
8
8
.
[5
]
B.
Da
s,
P
.
K.
P
a
n
ig
ra
h
i,
S
.
R.
Da
s,
D.
P
.
M
ish
ra
a
n
d
S
.
R
.
S
a
l
k
u
ti
,
“
P
o
we
r
Qu
a
li
ty
Im
p
ro
v
e
m
e
n
t
in
a
P
h
o
t
o
v
o
lt
a
ic
Ba
se
d
M
icro
g
rid
In
te
g
ra
ted
Ne
tw
o
rk
Us
in
g
M
u
lt
i
lev
e
l
I
n
v
e
rter
,
”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Eme
rg
in
g
El
e
c
tric
Po
we
r
S
y
ste
ms
,
p
p
.
0
0
0
0
1
0
1
5
1
5
2
0
2
1
0
0
4
0
,
2
0
2
1
,
d
o
i:
1
0
.
1
5
1
5
/i
jee
p
s
-
2
0
2
1
-
0
0
4
0
.
[6
]
P
.
K.
P
a
n
d
a
,
A.
S
a
h
o
o
,
A.
S
a
m
a
l,
D.
P
.
M
ish
ra
a
n
d
S
.
R.
S
a
lk
u
ti
,
“
Vo
lt
a
g
e
C
o
n
tro
l
o
f
AC
H
y
b
rid
M
icro
g
ri
d
”
,
In
ter
n
a
t
io
n
a
l
J
o
u
r
n
a
l
o
f
P
o
we
r
E
lec
tro
n
ics
a
n
d
Dr
ive
S
y
ste
m
(IJ
P
EDS
)
,
v
o
l
.
1
2
,
n
o
.
2
,
p
p
.
7
9
3
-
8
0
2
,
Ju
n
2
0
2
1
,
d
o
i:
1
0
.
1
1
5
9
1
/i
j
p
e
d
s.v
1
2
.
i2
.
p
p
7
9
3
-
8
0
2
.
[7
]
P
.
Ba
sa
k
,
S
.
Ch
o
wd
h
u
r
y
,
S
.
H.N.
De
y
a
n
d
S
.
P
.
Ch
o
w
d
h
u
ry
,
“
A l
it
e
ra
tu
re
re
v
iew
o
n
in
teg
ra
ti
o
n
o
f
d
i
strib
u
te
d
e
n
e
rg
y
re
so
u
rc
e
s
in
th
e
p
e
rsp
e
c
ti
v
e
o
f
c
o
n
tro
l
,
p
r
o
tec
ti
o
n
a
n
d
sta
b
il
it
y
o
f
m
icro
g
rid
”
,
Re
n
e
w.
S
u
sta
i
n
.
E
n
e
rg
y
Rev
.
,
v
o
l.
1
6
,
n
o
.
8
,
p
p
.
5
5
4
5
–
5
5
5
6
,
Au
g
.
2
0
1
2
,
d
o
i
:
1
0
.
1
0
1
6
/j
.
rse
r.
2
0
1
2
.
0
5
.
0
4
3
.
[8
]
S
.
R.
Da
s,
D
.
P
.
M
ish
ra
,
P
.
K.
Ra
y
,
S
.
R.
S
a
lk
u
ti
a
n
d
A.
K.
S
a
h
o
o
,
“
P
o
we
r
Qu
a
li
t
y
Im
p
ro
v
e
m
e
n
t
u
si
n
g
F
u
z
z
y
L
o
g
ic
Ba
se
d
Co
m
p
e
n
sa
ti
o
n
in
a
Hy
b
r
i
d
P
o
we
r
S
y
ste
m
,
”
I
n
ter
n
a
t
io
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.
1
2
,
n
o
.
1
,
p
p
.
5
7
6
-
5
8
4
,
M
a
r.
2
0
2
1
,
d
o
i:
1
0
.
1
1
5
9
1
/
ij
p
e
d
s.v
1
2
.
i
1
.
p
p
5
7
6
-
5
8
4
.
[9
]
T.
C.
G
re
e
n
a
n
d
M
.
P
ro
d
a
n
o
v
ic,
“
Co
n
tro
l
o
f
in
v
e
rter
-
b
a
se
d
m
icro
-
g
rid
s
”
,
E
lec
tr.
P
o
we
r
S
y
st.
Re
s.,
v
o
l
.
7
7
,
n
o
.
9
,
p
p
.
1
2
0
4
–
1
2
1
3
,
J
u
l.
2
0
0
7
,
d
o
i:
1
0
.
1
0
1
6
/
j.
e
p
sr.
2
0
0
6
.
0
8
.
0
1
7
.
[1
0
]
M
.
Ku
m
a
r
a
n
d
B.
T
y
a
g
i,
“
A
st
a
te
o
f
a
rt
re
v
iew
o
f
m
icro
g
ri
d
c
o
n
tr
o
l
a
n
d
i
n
teg
ra
ti
o
n
a
sp
e
c
ts,
”
2
0
1
6
7
th
I
n
d
ia
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
P
o
w
e
r E
lec
tro
n
i
c
s (IICP
E)
,
2
0
1
6
,
p
p
.
1
-
6
,
d
o
i:
1
0
.
1
1
0
9
/I
ICP
E.
2
0
1
6
.
8
0
7
9
4
1
1
.
[1
1
]
I.
Ve
c
h
iu
,
O.
Cu
re
a
,
A.
Ll
a
ria
a
n
d
H
.
Ca
m
b
lo
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g
,
“
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[1
2
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Q.
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a
,
J.
L
ia,
S
.
L.
Blo
n
d
a
a
n
d
C.
Wan
g
a
,
“
Artifi
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[1
3
]
Jiten
d
e
r
Ka
u
sh
a
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n
d
P
ra
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t
B
a
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P
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teg
ra
ted
AC
m
icro
g
rid
,”
S
u
sta
i
n
a
b
le
En
e
rg
y
,
Gr
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[1
4
]
A.
Re
z
v
a
n
i,
M
.
Iz
a
d
b
a
k
h
sh
a
n
d
M
.
G
a
n
d
o
m
k
a
r,
“
En
h
a
n
c
e
m
e
n
t
o
f
m
icro
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rid
d
y
n
a
m
ic
re
sp
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se
s
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d
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l
t
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ti
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si
n
g
a
rti
ficia
l
n
e
u
ra
l
n
e
two
rk
fo
r
fa
st
c
h
a
n
g
e
s
o
f
p
h
o
to
v
o
lt
a
ic
ra
d
iatio
n
a
n
d
F
LC
f
o
r
win
d
t
u
rb
in
e
,”
En
e
rg
y
S
y
ste
ms
,
v
o
l.
6,
n
o
.
4
,
p
p
.
5
5
1
–
5
8
4
,
2
0
1
5
.
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2159
[1
5
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R.
Zam
o
ra
a
n
d
A.
K.
S
riv
a
sta
v
a
,
“
Co
n
tro
ls
fo
r
m
icro
g
rid
s
wi
th
st
o
ra
g
e
:
Re
v
iew
,
c
h
a
ll
e
n
g
e
s,
a
n
d
r
e
se
a
rc
h
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s
,”
Ren
e
w.
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sta
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.
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n
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1
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[1
6
]
P
.
F
.
Rib
e
ir
o
,
B.
K.
Jo
h
n
so
n
,
M
.
L.
Cro
w,
A.
Ars
o
y
a
n
d
Y.
Li
u
,
“
En
e
rg
y
sto
ra
g
e
sy
ste
m
s
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r
a
d
v
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n
c
e
d
p
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s,
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o
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IEE
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,
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8
9
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[1
7
]
U.
Ak
ra
m
a
n
d
M
.
K
h
a
li
d
,
“
A
Co
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d
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ted
F
re
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e
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c
y
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e
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ies
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ss
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6
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S
S
.
2
0
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7
.
2
7
8
6
2
8
3
.
[1
8
]
S
.
P
a
rh
izi
,
H.
L
o
tfi
,
A.
Kh
o
d
a
e
i
a
n
d
S
.
Ba
h
ra
m
irad
,
“
S
tate
o
f
t
h
e
Art
in
Re
se
a
rc
h
o
n
M
icro
g
ri
d
s:
A
Re
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iew
,
”
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n
IEE
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Acc
e
ss
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.
3
,
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p
.
8
9
0
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S
.
2
0
1
5
.
2
4
4
3
1
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9
.
[1
9
]
H.
Be
v
ra
n
i,
F
.
Ha
b
ib
i
,
P
.
Ba
b
a
h
a
jy
a
n
i,
M
.
Wata
n
a
b
e
a
n
d
Y.
M
it
a
n
i,
“
In
telli
g
e
n
t
F
re
q
u
e
n
c
y
C
o
n
tr
o
l
in
a
n
AC
M
icro
g
ri
d
:
On
li
n
e
P
S
O
-
Ba
se
d
F
u
z
z
y
Tu
n
in
g
Ap
p
r
o
a
c
h
,
”
in
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
ma
rt
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id
,
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l.
3
,
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.
4
,
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p
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1
9
3
5
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4
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c
.
2
0
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2
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1
0
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9
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G
.
2
0
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2
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2
1
9
6
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0
6
.
[2
0
]
C.
M
u
,
Y.
Tan
g
a
n
d
H.
He
,
“
I
m
p
ro
v
e
d
S
li
d
i
n
g
M
o
d
e
De
sig
n
fo
r
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o
a
d
F
re
q
u
e
n
c
y
C
o
n
tr
o
l
o
f
P
o
we
r
S
y
ste
m
In
teg
ra
ted
a
n
Ad
a
p
ti
v
e
Lea
rn
in
g
S
trate
g
y
,
”
IEE
E
T
r
a
n
sa
c
ti
o
n
s
o
n
In
d
u
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a
l
El
e
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tro
n
ics
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6
4
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2
0
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7
.
2
6
9
4
3
9
6
.
[2
1
]
J.
Alsh
e
h
ri,
M
.
K
h
a
li
d
a
n
d
A.
Al
z
a
h
ra
n
i,
“
An
in
tell
ig
e
n
t
b
a
tt
e
ry
e
n
e
rg
y
st
o
ra
g
e
-
b
a
se
d
c
o
n
tro
ll
e
r
f
o
r
p
o
we
r
q
u
a
l
it
y
imp
ro
v
e
m
e
n
t
i
n
m
icro
g
rid
s
,”
En
e
rg
ies
,
v
o
l.
1
2
,
n
o
.
1
1
,
p
p
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2
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1
2
,
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n
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0
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9
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o
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0
.
3
3
9
0
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1
2
1
1
2
1
1
2
.
[2
2
]
P
.
Ya
n
g
,
Y.
Xia
,
M
.
Y
u
,
W.
Wei
a
n
d
Y.
P
e
n
g
,
“
A
De
c
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n
tralize
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Co
o
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n
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ti
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C
o
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tro
l
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e
t
h
o
d
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r
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Bid
irec
ti
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a
l
P
o
we
r
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n
v
e
rters
i
n
a
H
y
b
ri
d
AC
–
DC
M
icro
g
ri
d
,
”
I
EE
E
T
r
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n
sa
c
ti
o
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1
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9
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.
2
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7
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7
8
6
2
0
0
.
[2
3
]
W.
F
e
n
g
,
M
.
Ji
n
,
X
.
L
iu
,
Y.
Ba
o
,
C.
M
a
rn
a
y
,
C.
Ya
o
a
n
d
J.
Yu
,
“
A
re
v
iew
o
f
m
icro
g
rid
d
e
v
e
lo
p
m
e
n
t
i
n
t
h
e
u
n
i
ted
sta
tes
—
A
d
e
c
a
d
e
o
f
p
ro
g
re
ss
o
n
p
o
li
c
ies
,
d
e
m
o
n
stra
ti
o
n
s,
c
o
n
tro
ls,
a
n
d
so
f
twa
re
to
o
ls”
,
A
p
p
l
.
En
e
r
g
y
,
v
o
l.
2
2
8
,
p
p
.
1
6
5
6
–
1
6
6
8
,
Oc
t.
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0
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o
i:
1
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1
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a
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e
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e
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y
.
2
0
1
8
.
0
6
.
0
9
6
.
[2
4
]
F
.
M
.
Ib
a
n
e
z
,
“
An
a
l
y
z
in
g
t
h
e
N
e
e
d
fo
r
a
Ba
lan
c
i
n
g
S
y
ste
m
in
S
u
p
e
rc
a
p
a
c
it
o
r
En
e
rg
y
S
to
ra
g
e
S
y
ste
m
s,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r E
lec
tro
n
i
c
s
,
v
o
l.
3
3
,
n
o
.
3
,
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p
.
2
1
6
2
-
2
1
7
1
,
M
a
rc
h
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/T
P
EL
.
2
0
1
7
.
2
6
9
7
4
0
6
.
[2
5
]
M.
A.
Ha
n
n
a
n
,
S
.
Y.
Tan
a
n
d
A.Q.
Al
-
S
h
e
twi,
“
KP
Je
rn
Op
ti
m
ize
d
c
o
n
tro
ll
e
r
f
o
r
re
n
e
wa
b
le
e
n
e
rg
y
so
u
rc
e
s
in
teg
ra
ti
o
n
in
to
m
icro
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ri
d
:
F
u
n
c
t
io
n
s,
c
o
n
stra
in
ts
a
n
d
su
g
g
e
sti
o
n
s
”
,
J
o
u
r
n
a
l
o
f
Cle
a
n
e
r
p
ro
d
u
c
ti
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n
,
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l.
2
5
6
,
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p
.
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0
4
1
9
,
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0
2
0
,
d
o
i:
1
0
.
1
0
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6
/j
.
jcle
p
ro
.
2
0
2
0
.
1
2
0
4
1
9
.
[2
6
]
M
.
M
ir,
M
.
Da
y
y
a
n
i,
T
.
S
u
ti
k
n
o
,
M
.
M
.
Zan
ji
re
h
a
n
d
N.
Ra
z
m
jo
o
y
,
“
Emp
lo
y
in
g
a
G
a
u
ss
ian
P
a
rti
c
le
S
wa
rm
Op
ti
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iza
ti
o
n
m
e
th
o
d
f
o
r
t
u
n
i
n
g
M
u
lt
i
I
n
p
u
t
M
u
lt
i
Ou
t
p
u
t‐f
u
z
z
y
s
y
ste
m
a
s
a
n
in
teg
ra
ted
c
o
n
tr
o
ll
e
r
o
f
a
m
icro
‐g
ri
d
with
sta
b
il
it
y
a
n
a
ly
sis
,”
Co
m
p
u
t
a
ti
o
n
a
l
In
tell
ig
e
n
c
e
,
v
o
l
.
3
6
,
n
o
.
1
,
p
p
.
2
2
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,
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b
.
2
0
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0
,
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o
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1
1
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[2
7
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A.
P
a
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li
ll
o
,
D.
L.
Ca
rn
ì,
M
.
Ke
r
m
a
n
i,
L.
M
a
rti
ra
n
o
a
n
d
A.
Aie
ll
o
,
“
An
in
n
o
v
a
ti
v
e
H
o
m
e
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d
Bu
il
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in
g
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r
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n
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d
s
A
p
p
l
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c
a
ti
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n
s,
”
2
0
1
9
IEE
E
I
n
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
E
n
v
iro
n
me
n
t
a
n
d
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
2
0
1
9
IE
EE
I
n
d
u
stria
l
a
n
d
C
o
mm
e
rc
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l
Po
we
r S
y
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ms
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ro
p
e
(EE
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9
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p
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,
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9
/
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EIC.
2
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1
9
.
8
7
8
3
8
7
8
.
[2
8
]
S
.
Alfieri,
S
.
P
icc
in
i
a
n
d
M
.
Ke
rm
a
n
i,
“
F
e
a
sib
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it
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rly
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e
r
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il
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re
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l
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2
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IEE
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t
.
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v
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g
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a
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1
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IEE
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I
n
d
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stria
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d
Co
mm
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l
Po
we
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S
y
ste
ms
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ro
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(EE
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)
,
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1
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p
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EIC.
2
0
1
9
.
8
7
8
3
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2
9
.
[2
9
]
M
.
Ca
se
rz
a
M
a
g
ro
,
M
.
G
ian
n
e
tt
o
n
i,
P
.
P
in
c
e
ti
a
n
d
M
.
Va
n
ti
,
“
Re
a
l
ti
m
e
sim
u
lato
r
fo
r
m
icro
g
rid
s
,”
El
e
c
tric
Po
we
r
S
y
ste
ms
Res
e
a
r
ch
,
v
o
l
.
1
6
0
,
p
p
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8
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-
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,
2
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1
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.
e
p
sr
.
2
0
1
8
.
0
3
.
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1
8
.
[3
0
]
Ha
ss
a
n
Be
v
ra
n
i;
M
a
sa
y
u
k
i
Wata
n
a
b
e
a
n
d
Ya
su
n
o
ri
M
it
a
n
i,
“
M
ic
ro
g
ri
d
Co
n
tro
l:
C
o
n
c
e
p
ts
a
n
d
Cl
a
ss
ifi
c
a
ti
o
n
,
”
in
Po
we
r S
y
ste
m M
o
n
it
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rin
g
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Co
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l,
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E
,
2
0
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,
p
p
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1
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d
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1
1
1
8
8
5
2
4
2
2
.
c
h
0
9
.
[3
1
]
M
.
Vijay
a
k
u
m
a
r
a
n
d
S
.
Vijay
a
n
,
“
P
h
o
t
o
v
o
lt
a
ic
b
a
se
d
t
h
re
e
-
p
h
a
se
fo
u
r
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wire
se
ries
h
y
b
ri
d
a
c
ti
v
e
p
o
we
r
fil
ter
fo
r
p
o
we
r
q
u
a
li
ty
imp
r
o
v
e
m
e
n
t
,”
I
n
d
ia
n
J
.
En
g
.
M
a
ter
.
S
c
i.
,
v
o
l.
2
1
,
p
p
.
3
5
8
–
3
7
0
,
2
0
1
8
.
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