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H
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ro
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1
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5
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ted
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1
6
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2
0
2
5
Hig
h
im
p
e
d
a
n
c
e
(HI)
fa
u
l
ts
in
m
icro
g
rid
(M
G
)
p
o
we
r
sy
ste
m
s
a
re
n
o
n
-
li
n
e
a
r,
in
term
it
ten
t,
a
n
d
h
a
v
e
lo
w
fa
u
lt
c
u
rre
n
t
m
a
g
n
i
tu
d
e
s,
m
a
k
in
g
th
e
m
c
h
a
ll
e
n
g
i
n
g
t
o
d
e
tec
t
b
y
t
y
p
ica
l
p
ro
tec
ti
v
e
sy
ste
m
s.
Co
n
se
q
u
e
n
tl
y
,
it
is
imp
e
ra
ti
v
e
to
imp
lem
e
n
t
a
so
p
h
isti
c
a
ted
p
ro
tec
ti
o
n
sy
ste
m
th
a
t
is
d
e
p
e
n
d
e
n
t
o
n
t
h
e
p
re
c
isio
n
o
f
fa
u
lt
d
e
tec
ti
o
n
.
In
t
h
is
stu
d
y
,
a
sta
c
k
in
g
e
n
se
m
b
le
c
las
sifier
(S
EC)
is
p
ro
p
o
se
d
to
d
isc
rimin
a
te
HI
fa
u
lt
fro
m
o
t
h
e
r
tran
sie
n
ts
with
in
a
p
h
o
to
v
o
l
taic
(P
V)
g
e
n
e
r
a
ted
M
G
p
o
we
r
s
y
ste
m
.
T
h
e
M
G
m
o
d
e
l
is
sim
u
late
d
with
t
h
e
in
tro
d
u
c
ti
o
n
o
f
fa
u
lt
s
a
n
d
tran
sie
n
ts.
Th
e
fe
a
t
u
r
e
s
o
f
d
a
ta
se
t
fro
m
e
v
e
n
t
sig
n
a
ls
a
re
g
e
n
e
ra
ted
u
sin
g
t
h
e
d
isc
re
te
wa
v
e
let
tran
sfo
rm
(DWT
)
tec
h
n
iq
u
e
.
Th
e
d
a
tas
e
t
is
u
se
d
to
train
t
h
e
in
d
iv
i
d
u
a
l
c
las
sifiers
(Na
ïv
e
Ba
y
e
s
(NB),
d
e
c
isio
n
t
re
e
J4
8
(DTJ),
a
n
d
K
-
n
e
a
re
st
n
e
ig
h
b
o
r
s
(KN
N))
a
t
in
i
ti
a
l
a
n
d
m
e
ta
lea
rn
e
r
in
t
h
e
fi
n
a
l
sta
g
e
o
f
S
EC
.
Th
e
S
EC
o
u
t
p
e
rfo
rm
s
o
t
h
e
r
c
las
sifica
ti
o
n
m
e
th
o
d
s
with
re
sp
e
c
t
to
a
c
c
u
ra
c
y
o
f
c
las
sifica
ti
o
n
,
ra
te
o
f
su
c
c
e
ss
in
d
e
tec
ti
n
g
HI
fa
u
lt
,
a
n
d
p
e
rfo
rm
a
n
c
e
m
e
a
su
re
s
.
Th
e
o
u
tco
m
e
s o
f
th
e
c
l
a
ss
ifi
c
a
ti
o
n
stu
d
y
c
o
n
d
u
c
ted
u
n
d
e
r
sta
n
d
a
r
d
tes
t
c
o
n
d
it
i
o
n
s
(
S
TC)
o
f
so
lar P
V
a
n
d
th
e
n
o
isy
e
n
v
iro
n
m
e
n
t
o
f
e
v
e
n
t
sig
n
a
ls
c
lea
rly
d
e
m
o
n
stra
te
th
a
t
t
h
e
S
E
C
is
m
o
re
d
e
p
e
n
d
a
b
le
a
n
d
p
e
rfo
rm
s
b
e
tt
e
r
th
a
n
t
h
e
in
d
i
v
id
u
a
l
b
a
se
c
las
sifica
ti
o
n
a
p
p
r
o
a
c
h
e
s.
K
ey
w
o
r
d
s
:
Dec
is
io
n
t
r
ee
D
is
cr
ete
wav
elet
tr
an
s
f
o
r
m
Hig
h
im
p
ed
a
n
ce
f
au
lt
Mic
r
o
g
r
id
Ph
o
to
v
o
ltaic
Stack
in
g
en
s
em
b
le
class
if
ier
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ar
an
g
ar
ajan
Vin
a
y
ag
am
Dep
ar
tm
en
t o
f
E
lectr
ical
an
d
E
lectr
o
n
ics E
n
g
in
ee
r
i
n
g
,
New
Ho
r
izo
n
C
o
lleg
e
o
f
E
n
g
in
ee
r
i
n
g
B
an
g
alu
r
u
,
I
n
d
ia
E
m
ail:
ar
ajan
in
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Mic
r
o
g
r
id
s
(
MG
s
)
a
r
e
u
s
ed
to
d
eliv
er
d
ep
e
n
d
ab
le
,
co
s
t
-
ef
f
ec
tiv
e,
an
d
d
u
r
ab
le
en
er
g
y
t
o
r
em
o
te
ar
ea
s
[
1
]
.
Ho
wev
er
,
in
teg
r
atin
g
m
u
ltip
le
d
is
tr
ib
u
ted
g
en
e
r
atio
n
(
DG)
s
o
u
r
ce
s
,
s
u
ch
as
co
n
v
en
tio
n
al,
n
o
n
-
lin
ea
r
,
an
d
i
n
ter
m
itten
t
r
en
ew
ab
le
en
er
g
y
(
R
E
)
s
o
u
r
ce
s
,
a
n
d
o
cc
u
r
r
en
ce
o
f
ab
n
o
r
m
al
e
v
en
ts
b
r
o
u
g
h
t
o
n
b
y
s
witch
in
g
tr
an
s
ien
ts
an
d
f
au
lts
(
lo
w
a
n
d
h
ig
h
im
p
ed
a
n
ce
(
HI
)
f
au
lts
)
ca
n
n
eg
ativ
ely
im
p
ac
t
th
e
MG
'
s
s
ec
u
r
ity
an
d
r
eliab
ilit
y
[
2
]
.
HI
f
a
u
lts
ar
e
p
r
ev
ale
n
t
in
MG
n
etwo
r
k
s
wh
en
co
n
d
u
cto
r
s
co
n
tact
h
i
g
h
-
r
esis
tan
ce
s
u
r
f
ac
es
lik
e
wet
s
an
d
,
asp
h
alt,
tr
ee
li
m
b
s
,
an
d
g
r
a
v
el
[
3
]
.
T
h
ese
f
a
u
lts
ca
n
ca
u
s
e
elec
tr
ical
s
h
o
c
k
,
f
ir
e,
an
d
ca
s
ca
d
in
g
f
ailu
r
e,
a
f
f
ec
tin
g
h
ea
lth
y
p
a
r
ts
o
f
th
e
MG
n
etwo
r
k
[
4
]
,
[
5
]
.
T
h
e
HI
f
au
lt
e
x
h
ib
its
a
lo
w
f
a
u
lt
cu
r
r
en
t
am
p
litu
d
e,
co
m
p
licatin
g
d
etec
tio
n
an
d
is
o
latio
n
with
co
n
v
en
tio
n
al
p
r
o
tectiv
e
r
ela
y
s
.
I
n
th
is
in
s
tan
ce
,
a
s
o
p
h
is
ticated
p
r
o
tectio
n
s
y
s
tem
is
n
ee
d
ed
to
ac
cu
r
atel
y
d
et
ec
t
an
d
d
if
f
e
r
en
tiate
HI
f
au
lts
in
MG
n
etwo
r
k
s
to
is
o
late
th
e
f
au
lty
ar
ea
m
o
r
e
q
u
ick
ly
an
d
p
r
ec
is
ely
[
6
]
.
T
h
e
r
ef
o
r
e,
to
f
in
d
th
e
p
r
o
b
lem
atic
n
etwo
r
k
s
eg
m
en
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Hig
h
imp
ed
a
n
ce
fa
u
lt d
is
crimin
a
tio
n
in
micro
g
r
id
p
o
w
er sys
tem
u
s
in
g
…
(
A
r
a
n
g
a
r
a
ja
n
V
i
n
a
ya
g
a
m
)
99
f
aster
an
d
m
o
r
e
ac
cu
r
ately
,
a
n
en
h
an
ce
d
p
r
o
tectio
n
s
y
s
tem
th
at
d
ep
en
d
s
o
n
p
r
e
cise
d
etec
tio
n
an
d
id
en
tific
atio
n
o
f
HI
f
au
lts
is
e
s
s
en
tial.
T
o
ac
co
m
p
lis
h
m
o
r
e
p
r
ec
is
e
d
etec
tio
n
an
d
d
is
cr
im
i
n
atio
n
o
f
HI
f
au
lts
in
MG
n
etwo
r
k
s
,
ad
v
an
ce
d
m
ac
h
in
e
lear
n
in
g
(
ML
)
class
if
icatio
n
tech
n
iq
u
es c
an
b
e
im
p
le
m
en
ted
.
R
esear
ch
er
s
h
av
e
u
s
ed
v
ar
io
u
s
ML
alg
o
r
ith
m
s
to
d
is
co
v
er
a
n
d
ca
teg
o
r
ize
HI
f
au
lts
in
v
ar
i
o
u
s
p
o
wer
s
y
s
tem
m
o
d
els
an
d
MG
n
etwo
r
k
s
,
en
s
u
r
in
g
a
m
o
r
e
ef
f
icie
n
t
an
d
ac
cu
r
ate
is
o
latio
n
o
f
t
h
e
p
r
o
b
lem
atic
ar
ea
.
T
h
is
s
o
p
h
is
ticated
p
r
o
tectio
n
s
y
s
tem
is
im
p
o
r
tan
t
to
e
n
s
u
r
e
th
e
s
af
e
an
d
r
eliab
le
o
p
e
r
atio
n
o
f
MG
n
etwo
r
k
s
.
Mu
lti
-
lay
er
p
er
ce
p
tr
o
n
(
ML
P)
n
eu
r
al
n
etwo
r
k
s
[
7
]
,
m
u
lti
-
cla
s
s
s
u
p
p
o
r
t v
ec
to
r
m
ac
h
in
es (
S
VM
)
[
8
]
,
[
9
]
,
f
u
zz
y
an
d
ANFI
S
tech
n
iq
u
es
[
1
0
]
,
E
lm
an
n
eu
r
al
n
etwo
r
k
s
[
1
1
]
,
Kalm
an
f
ilter
-
b
ased
tech
n
iq
u
es
[
1
2
]
,
an
d
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NN
)
[
6
]
h
av
e
b
ee
n
u
s
ed
to
d
is
cr
im
in
ate
HI
f
a
u
lts
in
p
o
wer
n
etwo
r
k
s
.
HI
f
au
lt
o
cc
u
r
r
e
n
ce
s
h
av
e
b
ee
n
an
aly
s
ed
in
MG
n
etwo
r
k
s
u
s
in
g
th
e
Naiv
e
B
ay
es
class
if
ier
[
1
3
]
an
d
in
p
h
o
to
v
o
ltaic
(
PV
)
in
teg
r
ate
d
p
o
wer
n
etwo
r
k
s
u
s
in
g
m
at
h
e
m
atica
l
m
o
r
p
h
o
lo
g
y
[
1
4
]
.
As
p
er
th
e
liter
atu
r
e,
s
in
g
le
-
b
ase
class
if
ier
s
ar
e
wid
ely
u
s
ed
,
an
d
th
ey
ar
e
g
en
e
r
ally
ef
f
ec
tiv
e
f
o
r
s
p
ec
if
ic
tas
k
s
b
u
t
m
ay
s
tr
u
g
g
le
with
g
en
er
alis
atio
n
d
u
e
to
th
eir
s
u
s
ce
p
tib
ilit
y
to
n
o
is
e
o
r
o
v
er
f
itti
n
g
.
An
en
s
em
b
le
m
o
d
el
en
h
an
ce
s
th
e
ac
cu
r
ac
y
a
n
d
c
o
n
s
is
ten
cy
o
f
s
in
g
le
-
b
ase
class
if
ier
s
b
y
tr
ain
i
n
g
m
u
ltip
le
class
if
ier
s
o
n
th
e
s
am
e
p
r
o
b
lem
[
1
5
]
.
R
esear
ch
er
s
h
av
e
u
s
ed
en
s
em
b
le
ap
p
r
o
ac
h
es
lik
e
v
o
tin
g
p
r
o
b
ab
ilit
y
[
1
6
]
,
b
a
g
g
ed
tr
e
e
[
1
7
]
,
an
d
r
an
d
o
m
f
o
r
est
[
1
8
]
to
class
if
y
elec
tr
ical
f
au
lts
in
p
o
wer
s
y
s
tem
s
.
E
n
s
em
b
le
class
if
ier
s
b
a
s
ed
o
n
ex
ten
d
ed
Kalm
an
f
ilter
s
[
1
9
]
an
d
KNN
-
b
ased
r
an
d
o
m
s
u
b
s
p
ac
e
ap
p
r
o
ac
h
es
[
3
]
h
av
e
b
ee
n
u
s
ed
to
d
is
cr
im
in
ate
h
ig
h
i
n
ten
s
ity
cu
r
r
en
t
(
HI
)
f
au
lt
in
PV
-
in
teg
r
ated
p
o
wer
n
etwo
r
k
s
.
A
b
o
o
s
tin
g
en
s
em
b
le
m
eth
o
d
[
2
0
]
an
d
v
o
tin
g
e
n
s
em
b
le
m
eth
o
d
[
2
1
]
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
f
o
r
d
etec
tin
g
an
d
class
if
y
in
g
elec
tr
ical
f
a
u
lts
.
T
h
e
r
esear
ch
s
u
g
g
ests
th
at
en
s
em
b
le
class
if
ier
s
ar
e
m
o
r
e
r
eliab
le
an
d
ef
f
icie
n
t
th
an
s
in
g
le
-
b
ase
class
if
ier
s
.
T
h
e
b
ag
g
in
g
an
d
b
o
o
s
tin
g
en
s
em
b
le
alg
o
r
ith
m
s
u
s
e
h
o
m
o
g
en
o
u
s
wea
k
class
if
ier
s
,
h
o
wev
er
an
a
d
v
an
ce
d
s
tack
in
g
en
s
em
b
le
class
if
ier
u
s
es
a
co
llectio
n
o
f
h
eter
o
g
en
eo
u
s
wea
k
class
if
ier
s
f
o
r
s
u
p
er
io
r
g
en
e
r
aliza
tio
n
ac
cu
r
ac
y
[
2
2
]
,
[
2
3
]
.
T
h
er
ef
o
r
e,
th
is
in
v
esti
g
atio
n
s
u
g
g
ests
a
s
tack
in
g
m
eth
o
d
o
lo
g
y
f
o
r
clas
s
if
y
in
g
HI
d
ef
ec
ts
f
r
o
m
o
t
h
er
tr
an
s
ien
ts
in
a
PV
-
g
en
er
ated
MG
p
o
wer
s
y
s
tem
.
T
h
e
p
r
esen
t
r
esear
ch
ad
d
r
ess
es
a
g
ap
in
th
e
liter
atu
r
e
b
y
c
o
n
ce
n
tr
atin
g
o
n
t
h
e
ev
alu
atio
n
o
f
HI
f
au
lts
u
s
in
g
a
s
tack
in
g
en
s
em
b
le
ap
p
r
o
a
ch
.
T
h
is
to
p
ic
h
as
b
ee
n
th
e
s
u
b
ject
o
f
lim
ited
in
v
esti
g
atio
n
in
PV
-
b
ased
MG
p
o
wer
s
y
s
tem
s
.
T
h
e
m
eth
o
d
is
d
esig
n
ed
to
ac
cu
r
ately
class
i
f
y
f
au
lts
in
a
n
o
is
y
en
v
ir
o
n
m
en
t
o
f
ev
e
n
t
s
ig
n
als
an
d
s
tan
d
ar
d
test
co
n
d
itio
n
s
o
f
s
o
lar
PV.
Ad
d
r
ess
in
g
th
ese
r
esear
ch
g
ap
s
i
s
cr
u
c
ial
f
o
r
im
p
r
o
v
i
n
g
f
a
u
lt
cla
s
s
if
i
ca
t
io
n
m
et
h
o
d
o
lo
g
i
es
i
n
R
E
-
i
n
t
e
g
r
ate
d
M
G
p
o
w
e
r
n
etw
o
r
k
s
a
n
d
d
ev
el
o
p
in
g
r
o
b
u
s
t e
n
s
em
b
le
-
b
ased
ap
p
r
o
a
ch
es f
o
r
r
ea
l
-
w
o
r
ld
a
p
p
licatio
n
s
.
T
h
e
s
tu
d
y
'
s
p
r
im
ar
y
c
o
n
tr
i
b
u
tio
n
s
ar
e:
−
A
s
tack
in
g
tec
h
n
iq
u
e
o
f
a
n
e
n
s
em
b
le
class
if
ier
is
p
r
o
p
o
s
ed
to
d
if
f
er
en
tiate
H
I
f
a
u
lt
f
r
o
m
o
th
er
f
a
u
lts
an
d
tr
an
s
ien
ts
in
a
PV
-
g
en
er
ated
MG
p
o
wer
s
y
s
tem
.
−
Usi
n
g
th
e
d
is
cr
ete
wav
elet
tr
an
s
f
o
r
m
(
DW
T
)
m
eth
o
d
,
f
ea
t
u
r
es
o
f
th
e
d
ataset
f
r
o
m
f
a
u
lts
an
d
tr
an
s
ien
t
s
ig
n
als ar
e
g
en
er
ated
t
o
tr
ain
t
h
e
s
u
g
g
ested
s
tack
in
g
a
n
d
in
d
i
v
id
u
al
class
if
ier
s
.
−
T
o
v
er
if
y
th
e
e
f
f
icac
y
o
f
t
h
e
s
u
g
g
ested
s
tack
in
g
tech
n
iq
u
e,
a
class
if
icatio
n
an
aly
s
is
is
p
er
f
o
r
m
ed
in
ter
m
s
o
f
ac
cu
r
ac
y
an
d
p
er
f
o
r
m
a
n
ce
m
etr
ics (
PM)
wh
ile
MG
is
o
p
er
atin
g
u
n
d
er
s
tan
d
a
r
d
test
co
n
d
itio
n
.
−
T
o
v
er
if
y
th
e
r
esil
ien
ce
o
f
th
e
s
u
g
g
ested
en
s
em
b
le
m
o
d
el,
a
class
if
icatio
n
s
tu
d
y
is
ca
r
r
ied
o
u
t
u
n
d
er
a
n
o
is
y
en
v
ir
o
n
m
e
n
t o
f
e
v
en
t sig
n
als.
S
t
r
u
c
t
u
r
e
o
f
t
h
e
m
a
n
u
s
c
r
i
p
t
:
s
ec
t
i
o
n
2
d
e
s
c
r
i
b
e
s
M
G
m
o
d
e
l
,
H
I
f
a
u
l
t
m
o
d
e
l
,
a
n
d
p
r
o
c
e
s
s
s
te
p
s
o
f
c
l
ass
i
f
i
c
at
i
o
n
m
o
d
el;
s
ec
tio
n
3
d
ef
i
n
es
D
W
T
m
eth
o
d
;
s
ec
tio
n
4
p
r
esen
ts
th
e
d
etails
o
f
m
ater
ials
an
d
m
eth
o
d
o
l
o
g
y
;
s
ec
tio
n
5
d
is
cu
s
s
es c
lass
if
icati
o
n
r
esu
lts
; a
n
d
s
ec
tio
n
6
s
u
m
m
ar
izes
r
esear
ch
f
in
d
i
n
g
s
an
d
f
u
tu
r
e
s
tep
s
.
2.
DE
SCR
I
P
T
I
O
N
O
F
P
V
I
NT
E
G
RAT
E
D
M
G
M
O
D
E
L
T
h
is
s
tu
d
y
u
s
es
th
e
e
n
s
em
b
l
e
class
if
ier
to
d
is
tin
g
u
is
h
HI
f
au
lts
f
r
o
m
o
th
e
r
tr
an
s
ien
ts
in
a
PV
-
g
en
er
ated
MG
m
o
d
el.
MA
T
L
AB
-
Simu
lin
k
is
u
s
ed
t
o
s
im
u
late
an
d
an
al
y
s
e
MG
n
et
wo
r
k
m
o
d
el.
Fig
u
r
e
1
s
h
o
ws
th
e
s
im
p
le
d
iag
r
am
o
f
an
is
lan
d
ed
PV
g
en
er
ate
d
MG
p
o
wer
s
y
s
tem
with
in
teg
r
atio
n
o
f
f
o
llo
win
g
elem
en
ts
:
s
o
lar
PV
s
y
s
tem
:
3
u
n
its
r
ated
3
0
0
k
W
p
(
1
0
0
k
W
p
/u
n
it);
DC
-
DC
p
o
wer
t
co
n
v
e
r
ter
(
2
9
0
V/5
0
0
V
DC
)
with
m
ax
im
u
m
p
o
wer
p
o
in
t
tr
ac
k
in
g
co
n
t
r
o
l;
v
o
lta
g
e
s
o
u
r
ce
in
v
er
ter
i
n
ter
f
ac
e
PV
s
o
u
r
ce
in
t
o
AC
n
etwo
r
k
th
r
o
u
g
h
tr
an
s
f
o
r
m
er
T
1
(
0
.
2
6
0
k
V/1
1
k
V,
3
0
0
k
V
A,
5
0
Hz)
;
d
iesel
en
g
in
e
g
en
er
ato
r
(
DE
G)
:
3
.
2
5
MV
A
DE
G
i
s
in
ter
co
n
n
ec
ted
to
AC
n
etwo
r
k
th
r
o
u
g
h
tr
a
n
s
f
o
r
m
er
T
2
;
AC
lo
ad
m
ax
im
u
m
ca
p
ac
ity
o
f
2
.
2
MW
at
1
1
k
V;
c
ap
ac
ito
r
b
an
k
m
ax
im
u
m
ca
p
ac
ity
o
f
7
0
0
k
VAR
at
1
1
k
V;
an
d
HI
f
a
u
lt
m
o
d
el
with
an
ti
-
p
ar
allel
d
io
d
es,
r
esis
to
r
s
(
R
1
a
n
d
R
2
)
,
an
d
v
o
ltag
e
s
o
u
r
ce
s
(
V1
an
d
V2
)
.
Th
is
s
tu
d
y
u
s
es
a
s
p
ec
if
ic
p
r
o
ce
d
u
r
e
to
g
en
er
ate
8
0
0
c
u
r
r
e
n
t
s
ig
n
al
s
am
p
les
(
1
0
0
s
am
p
les p
er
ev
en
t)
,
with
an
MG
m
o
d
el
s
im
u
latio
n
tim
e
o
f
0
.
5
s
ec
o
n
d
s
:
i)
T
h
e
HI
f
au
lt
m
o
d
el
(
Fig
u
r
e
2
(
a)
)
g
en
er
ates
n
o
n
-
lin
ea
r
v
o
ltag
e
-
cu
r
r
en
t
s
am
p
les
b
y
v
a
r
y
in
g
r
esis
to
r
v
alu
es
(
R
S1
an
d
R
S2
)
b
etwe
en
0
.
1
0
k
Ω
an
d
5
.
2
k
Ω
,
an
d
v
o
lta
g
e
s
o
u
r
ce
s
(
VS1
an
d
VS2
)
b
etw
ee
n
0
.
5
k
V
an
d
1
0
.
2
k
V
in
0
.
3
s
to
0
.
3
5
s
.
ii)
C
u
r
r
e
n
t
s
ig
n
al
s
am
p
les
f
o
r
lo
w
im
p
ed
an
ce
f
a
u
lts
(
L
GF,
L
L
GF,
L
L
L
GF,
an
d
L
L
F)
ar
e
g
en
e
r
ated
b
y
ad
ju
s
tin
g
r
esis
tan
ce
v
alu
es
(
1
0
-
1
2
0
%
Ω
)
in
0
.
3
-
0
.
3
5
s
s
tep
s
.
i
ii)
Hea
v
y
lo
ad
s
(
0
.
5
MW
-
2
.
4
MW)
a
n
d
ca
p
ac
ito
r
b
a
n
k
s
(
2
5
0
k
V
AR
-
7
0
0
k
VAR)
ar
e
tu
r
n
ed
o
n
in
s
tag
es to
g
en
er
ate
L
ST
an
d
C
ST
s
ig
n
als (
0
.
3
s
s
witch
in
g
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
9
8
-
1
09
100
2
.
1
.
H
i
g
h im
peda
nce
f
a
ult
t
r
a
ns
ient
m
o
del
T
h
e
h
ig
h
im
p
e
d
an
ce
(
H
I
)
f
a
u
lt
m
o
d
el
is
a
cir
c
u
it
th
at
m
im
ics
th
e
p
r
o
p
er
ties
o
f
t
h
e
E
m
an
u
el
m
o
d
el
[
7
]
,
co
n
s
is
tin
g
o
f
an
ti
-
p
ar
allel
d
io
d
es,
ad
ju
s
tab
le
r
esis
to
r
s
,
an
d
DC
s
o
u
r
ce
v
o
ltag
es
[
1
9
]
.
I
t
g
en
er
ates
d
is
tin
ct
n
o
n
-
lin
ea
r
v
o
ltag
e
-
cu
r
r
en
t
cu
r
v
es
b
y
v
ar
y
i
n
g
r
esi
s
tan
ce
v
alu
es
an
d
v
o
ltag
e
le
v
els.
T
h
e
HI
f
a
u
lt
v
o
ltag
e
an
d
cu
r
r
en
t
p
atter
n
s
with
in
th
e
m
icr
o
g
r
id
n
etwo
r
k
ex
h
ib
it
a
n
o
n
lin
ea
r
,
asy
m
m
etr
ical,
lo
w
-
am
p
litu
d
e
cu
r
r
en
t
wav
ef
o
r
m
d
o
m
in
ated
b
y
s
ec
o
n
d
-
an
d
th
ir
d
-
o
r
d
er
h
ar
m
o
n
ics.
T
h
e
m
o
d
el'
s
co
n
f
ig
u
r
atio
n
an
d
V
-
I
ch
ar
ac
ter
is
tics
o
f
f
au
lt si
g
n
al
a
r
e
illu
s
tr
ated
in
Fig
u
r
es 2
(
a
)
a
n
d
2
(
b
)
,
r
esp
ec
tiv
ely
.
Fig
u
r
e
1
.
PV in
teg
r
ate
d
MG
m
o
d
el
(
a)
(
b
)
Fig
u
r
e
2
.
HI
f
au
lt m
o
d
el:
(
a)
b
asic c
o
n
f
ig
u
r
atio
n
an
d
(
b
)
V
-
I
cu
r
v
es
2
.
2
.
Sig
na
l
pro
ce
s
s
ing
wit
h dis
cr
et
e
wa
v
elet
t
ra
ns
f
o
rm
(
DWT
)
DW
T
m
eth
o
d
is
u
tili
s
ed
in
t
h
is
wo
r
k
to
d
ec
o
m
p
o
s
e
th
e
HI
f
au
lt
an
d
o
t
h
er
tr
an
s
ien
t
s
ig
n
als
to
r
etr
iev
e
f
ea
tu
r
es o
f
d
ata
s
et
f
o
r
lear
n
in
g
th
e
class
if
ier
s
.
T
h
e
ex
p
r
ess
io
n
o
f
DW
T
is
wr
itten
a
s
(
1
)
[
2
4
]
.
(
,
)
=
1
√
∑
(
)
×
×
(
−
)
(
1
)
W
h
er
e
is
th
e
s
ca
lin
g
p
ar
am
et
er
,
is
th
e
tr
an
s
latio
n
p
a
r
am
ete
r
,
f
is
th
e
m
o
th
e
r
wav
elet
f
u
n
c
tio
n
,
p
an
d
m
ar
e
in
teg
er
v
ar
iab
les,
x
(
m
)
is
tim
e
s
ig
n
al,
an
d
k
is
th
e
n
u
m
b
er
o
f
s
am
p
les
of
an
in
p
u
t
s
ig
n
al.
(
2
)
-
(
4
)
ex
p
r
ess
co
-
ef
f
icien
t
an
d
en
e
r
g
y
v
alu
e
(
E
V)
[
2
2
]
.
T
h
is
s
tu
d
y
c
o
n
s
id
er
s
m
o
th
er
wav
elet
Dau
b
ec
h
i
es
4
(
d
b
4
)
f
u
n
ctio
n
an
d
d
ec
o
m
p
o
s
itio
n
lev
el
(
5
th
)
f
o
r
s
ig
n
al
d
ec
o
m
p
o
s
itio
n
.
T
h
e
ea
r
lier
s
tu
d
y
[
2
3
]
d
is
cu
s
s
es D
W
T
ap
p
r
o
ac
h
.
(
)
=
∑
(
)
×
1
×
(
2
−
)
(
2
)
(
)
=
∑
(
)
×
1
×
(
2
−
)
(
3
)
=
∑
[
|
|
2
]
=
1
+
|
|
2
(
4
)
W
h
er
e
L
F1
an
d
HF1
s
tan
d
f
o
r
lo
w
an
d
h
ig
h
p
ass
f
ilter
s
,
N
d
en
o
tes n
u
m
b
e
r
o
f
d
ec
o
m
p
o
s
itio
n
lev
el,
Ai
an
d
DJ
r
ep
r
esen
t th
e
ap
p
r
o
x
im
ate
an
d
d
etailed
co
ef
f
icien
ts
,
r
esp
ec
ti
v
ely
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Hig
h
imp
ed
a
n
ce
fa
u
lt d
is
crimin
a
tio
n
in
micro
g
r
id
p
o
w
er sys
tem
u
s
in
g
…
(
A
r
a
n
g
a
r
a
ja
n
V
i
n
a
ya
g
a
m
)
101
3.
M
AT
E
R
I
AL
S AN
D
M
E
T
H
O
D
O
F
CL
ASS
I
F
I
CA
T
I
O
N
T
h
e
co
m
p
lete
class
if
icatio
n
p
r
o
ce
s
s
,
s
h
o
wn
in
Fig
u
r
e
3
,
in
cl
u
d
es
v
ar
io
u
s
s
tep
s
o
f
an
aly
s
is
.
First,
th
e
PV
-
in
teg
r
ated
MG
m
o
d
el
is
c
r
ea
ted
,
an
d
s
im
u
latio
n
an
al
y
s
is
is
ca
r
r
ied
o
u
t
with
th
e
i
n
tr
o
d
u
ctio
n
o
f
d
if
f
er
en
t
f
au
lts
an
d
tr
an
s
ien
t
ev
en
ts
in
th
e
MA
T
L
AB
/
Si
m
u
lin
k
s
o
f
twar
e
en
v
ir
o
n
m
en
t.
Als
o
,
u
s
in
g
MA
T
L
AB
/S
im
u
lin
k
,
u
s
in
g
th
e
DW
T
tech
n
iq
u
e,
th
e
f
ea
tu
r
e
s
o
f
th
e
d
ataset
ar
e
e
x
tr
ac
ted
f
r
o
m
t
h
e
f
a
u
lts
an
d
tr
an
s
ien
t
ev
en
ts
.
T
h
e
f
ea
tu
r
es
ar
e
u
tili
s
ed
to
tr
ain
th
e
ML
class
if
ier
s
(
NB
,
DT
J
,
KNN,
an
d
SEC)
u
s
in
g
a
n
o
p
en
-
s
o
u
r
ce
to
o
l,
W
E
KA.
T
h
e
W
E
KA
p
r
o
v
id
es
s
u
p
er
v
i
s
ed
an
d
u
n
s
u
p
er
v
is
ed
ML
m
eth
o
d
s
,
in
clu
d
i
n
g
g
r
o
u
p
in
g
,
v
is
u
alis
atio
n
,
r
e
g
r
e
s
s
io
n
,
an
d
class
if
icatio
n
[
2
3
]
.
Fro
m
t
h
e
r
esu
lts
o
f
th
e
co
n
f
u
s
io
n
m
atr
i
x
,
th
e
class
if
icatio
n
ac
cu
r
ac
y
o
f
ea
c
h
class
if
icatio
n
m
eth
o
d
is
esti
m
ated
.
T
h
e
ev
e
n
ts
lik
e
n
o
r
m
al
co
n
d
itio
n
,
L
GF,
L
L
GF,
L
L
L
GF,
L
L
F,
HI
f
au
l
t,
L
ST,
an
d
C
ST
in
th
e
n
etwo
r
k
ar
e
class
if
ied
in
th
e
class
n
am
es
C
S1
to
C
S8
.
W
h
ile
tr
ain
in
g
th
e
class
if
ier
s
,
a
k
-
f
o
ld
cr
o
s
s
v
alid
atio
n
tech
n
iq
u
e
is
u
s
ed
to
lear
n
class
if
ier
s
,
wh
ich
is
m
o
r
e
ef
f
ec
tiv
e
th
a
n
th
e
h
o
ld
o
u
t
d
ata
s
et
m
eth
o
d
a
n
d
c
a
n
o
v
e
r
co
m
e
o
v
e
r
f
itti
n
g
is
s
u
es
with
lim
ited
d
atasets
[
3
]
,
[
2
3
]
.
A
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
s
tr
ateg
y
h
as
b
ee
n
s
u
cc
ess
f
u
l
in
e
v
alu
atin
g
class
if
ier
ef
f
ec
tiv
en
ess
,
o
f
f
er
i
n
g
ac
cu
r
ate
ap
p
r
o
x
im
atio
n
s
f
o
r
class
if
icatio
n
ac
cu
r
ac
y
ac
r
o
s
s
v
ar
io
u
s
task
s
[
3
]
,
[
2
3
]
.
T
h
is
s
tu
d
y
u
s
es
a
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
m
eth
o
d
o
lo
g
y
to
tr
a
in
class
if
icatio
n
m
o
d
els,
f
o
c
u
s
in
g
o
n
p
r
o
p
o
s
ed
s
tack
in
g
(
SEC)
an
d
in
d
i
v
id
u
al
class
if
ier
s
(
N
B
,
DT
J
,
an
d
KNN)
.
Fin
ally
,
th
e
class
if
icatio
n
m
o
d
els
ef
f
ec
tiv
ely
id
en
tify
f
au
lt
ty
p
es
b
ased
o
n
th
ese
p
r
ed
ictio
n
s
.
I
n
ca
s
e
o
f
a
f
au
lt,
th
e
p
r
o
ce
d
u
r
e
is
co
m
p
leted
with
a
tr
ip
s
ig
n
al
an
d
p
ass
es
to
th
e
p
r
o
tectiv
e
s
y
s
tem
,
o
r
r
ep
ea
ted
in
ca
s
e
o
f
n
o
r
m
al
co
n
d
itio
n
s
.
I
n
ad
d
itio
n
,
th
e
m
ater
ials
,
m
eth
o
d
s
,
an
d
co
n
ce
p
ts
o
f
ea
ch
ML
alg
o
r
ith
m
e
m
p
lo
y
e
d
in
th
is
s
tu
d
y
ar
e
d
elin
ea
ted
as f
o
llo
ws:
3
.
1
.
M
a
t
er
ia
ls
T
h
e
m
ater
ials
an
d
to
o
ls
u
s
ed
in
th
is
s
tu
d
y
ar
e
s
u
m
m
ar
ized
a
s
f
o
llo
ws:
−
MA
T
L
AB
/S
im
u
lin
k
(
R
2
0
1
9
b
)
s
o
f
twar
e
to
o
l:
u
s
ed
f
o
r
M
G
m
o
d
el
d
ev
elo
p
m
en
t/s
im
u
latio
n
an
d
s
ig
n
al
p
r
o
ce
s
s
in
g
(
w
av
elet
to
o
l b
o
x
)
with
DW
T
ap
p
r
o
ac
h
.
−
W
E
KA
(
V
3
.
9
.
6
)
o
p
en
-
s
o
u
r
ce
to
o
l: m
ac
h
in
e
lear
n
in
g
m
o
d
els
(
f
o
r
class
if
icatio
n
o
f
ev
en
ts
)
.
−
Per
s
o
n
al
co
m
p
u
ter
:
I
n
tel
i5
C
PU r
u
n
n
in
g
at
2
.
4
GHz
an
d
1
6
GB
o
f
R
AM
.
−
Data
s
et:
ex
tr
ac
ted
f
ea
tu
r
es f
r
o
m
th
e
s
im
u
lated
cu
r
r
en
t sig
n
al
s
(
f
au
lts
/tra
n
s
ien
ts
)
.
3
.
2
.
M
et
ho
ds
T
h
e
o
v
e
r
all
m
eth
o
d
o
lo
g
y
ad
o
p
ted
in
th
is
s
tu
d
y
c
o
n
s
is
ts
o
f
th
e
f
o
llo
win
g
s
tag
es:
−
MG
m
o
d
el
s
im
u
latio
n
an
al
y
s
is
: w
ith
in
tr
o
d
u
ctio
n
o
f
f
a
u
lts
an
d
tr
an
s
ien
ts
s
witch
in
g
.
−
Sig
n
al
p
r
o
ce
s
s
in
g
a
n
aly
s
is
:
ap
p
ly
DW
T
m
eth
o
d
(
d
ec
o
m
p
o
s
e
th
e
s
ig
n
als
to
ex
t
r
ac
t
f
e
atu
r
es
f
r
o
m
th
e
ev
alu
ated
wav
elet
co
e
f
f
icien
ts
)
.
−
C
las
s
if
icatio
n
an
aly
s
is
: tr
ain
th
e
class
if
ier
m
o
d
els (
NB
,
DT
J
,
KNN
,
an
d
SEC)
an
d
e
v
alu
at
e
th
e
r
esu
lts
.
−
Sy
s
tem
o
p
er
atio
n
:
g
e
n
er
ates
t
h
e
tr
ip
s
ig
n
al
o
n
f
au
lt
d
etec
tio
n
a
n
d
r
ep
ea
ts
th
e
p
r
o
ce
s
s
in
ca
s
e
o
f
n
o
f
au
lt
co
n
d
itio
n
s
.
Fig
u
r
e
3
.
B
lo
ck
d
iag
r
am
o
f
cla
s
s
if
icatio
n
p
r
o
ce
s
s
M
i
c
r
o
g
r
i
d
M
o
d
el
D
e
v
e
lo
p
m
e
n
t
/
S
i
m
u
l
a
t
i
o
n
E
x
t
r
a
c
t
ed
F
ea
t
u
r
es
f
r
o
m
F
a
u
lt
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2
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3.
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.
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po
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s
s
if
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ho
d:
s
t
a
c
k
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la
s
s
if
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SE
C)
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s
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u
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4
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w
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e
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{A
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X
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(
d
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Tr
a
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me
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a
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l
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r
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K
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h
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d
d
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t
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t
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S
t
a
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a
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s
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Eₐ
(
x
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(
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,
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x
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S
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x
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w
h
e
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e
M
=
8
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
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E
n
g
I
SS
N:
2252
-
8
7
9
2
Hig
h
imp
ed
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n
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fa
u
lt d
is
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a
tio
n
in
micro
g
r
id
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er sys
tem
u
s
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g
…
(
A
r
a
n
g
a
r
a
ja
n
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i
n
a
ya
g
a
m
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103
Fig
u
r
e
4
.
Pro
p
o
s
ed
s
tack
in
g
e
n
s
em
b
le
class
if
icatio
n
m
o
d
el
4.
RE
SU
L
T
S
ANA
L
YS
I
S AN
D
DIS
CU
SS
I
O
N
T
h
e
MG
m
o
d
el
was
s
im
u
late
d
with
v
ar
io
u
s
class
ev
en
ts
,
in
clu
d
in
g
n
o
r
m
al,
HI
F,
L
GF,
an
d
L
L
GF
(
Fig
u
r
es
5
(
a
)
t
o
5
(
d
)
)
a
n
d
L
L
L
GF,
L
L
F,
L
ST,
an
d
C
ST
(
F
ig
u
r
es
6
(
a)
t
o
6
(
d
)
)
.
T
h
e
r
esu
l
ts
o
f
d
ec
o
m
p
o
s
in
g
s
ig
n
als
o
f
v
ar
io
u
s
ev
en
ts
u
s
in
g
th
e
DW
T
ap
p
r
o
ac
h
ar
e
ex
p
lain
ed
in
s
ec
tio
n
4
.
1
.
Ad
d
itio
n
ally
,
in
s
ec
tio
n
4
.
2
,
th
e
r
esu
lts
o
f
th
e
clas
s
if
icatio
n
an
aly
s
is
f
o
r
th
e
ca
s
e
s
o
f
PV
-
c
o
n
n
ec
ted
MG
u
n
d
e
r
s
tan
d
ar
d
test
co
n
d
itio
n
s
an
d
ev
en
t sig
n
als ex
p
o
s
ed
t
o
n
o
is
y
en
v
ir
o
n
m
en
ts
ar
e
d
is
cu
s
s
ed
.
Fig
u
r
e
5
.
Sig
n
als o
f
p
h
ase
cu
r
r
en
t
: (
a)
n
o
r
m
al
,
(
b
)
HI
f
a
u
l
t
,
(
c)
L
GF
,
an
d
(
d
)
L
L
GF
Fig
u
r
e
6
.
Sig
n
als o
f
p
h
ase
cu
r
r
en
t:
(
a)
L
L
L
GF
,
(
b
)
L
L
F
,
(
c
)
C
ST
,
an
d
(
d
)
L
ST
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
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,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
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6
:
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8
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1
09
104
4
.
1
.
Resul
t
s
o
f
deco
m
po
s
ed
s
ig
na
ls
T
h
is
an
aly
s
is
g
en
er
ated
wav
e
let
co
ef
f
icien
ts
b
y
d
ec
o
m
p
o
s
i
n
g
HI
f
au
lt
a
n
d
o
th
e
r
tr
an
s
ie
n
t
cu
r
r
en
t
s
ig
n
als
at
s
ev
er
al
lev
els.
(
4
)
a
s
s
es
s
ed
f
ea
tu
r
e
en
er
g
y
u
s
in
g
r
ec
o
v
er
ed
co
ef
f
icien
ts
.
SEC
an
d
o
th
er
s
in
g
le
b
ase
class
if
ier
s
(
N
B
,
DT
J
,
an
d
KNN)
wer
e
tr
ain
ed
u
tili
s
in
g
th
ese
f
ea
tu
r
es
f
o
r
class
if
icatio
n
.
Fo
r
d
ec
o
m
p
o
s
in
g
s
ig
n
als,
d
ec
o
m
p
o
s
itio
n
at
5
th
lev
el,
th
e
m
o
t
h
er
wav
elet
f
u
n
ctio
n
d
b
4
,
a
n
d
a
s
am
p
lin
g
f
r
eq
u
en
cy
o
f
2
4
k
Hz
wer
e
tak
en
in
to
ac
co
u
n
t.
Fig
u
r
es
7
(
a)
to
7
(
d
)
d
ep
ict
d
ec
o
m
p
o
s
ed
wav
ef
o
r
m
s
f
o
r
n
o
r
m
al
,
HI
f
au
lt,
L
GF,
an
d
C
ST
ev
en
ts
in
an
is
lan
d
e
d
MG
n
etwo
r
k
,
alo
n
g
wit
h
wav
elet
c
o
ef
f
icien
t
r
ep
r
e
s
en
tatio
n
s
.
T
h
ese
d
ec
o
m
p
o
s
itio
n
s
h
elp
ed
id
en
tif
y
d
is
tin
g
u
is
h
in
g
f
ea
tu
r
es
o
f
e
ac
h
class
ev
en
t
in
th
e
n
etwo
r
k
.
No
s
p
ik
es
wer
e
o
b
s
er
v
ed
in
th
e
wav
ef
o
r
m
c
o
e
f
f
icien
ts
o
f
t
h
e
n
o
r
m
al
s
tate
e
v
en
t,
b
u
t
s
p
ik
es
wer
e
o
b
s
er
v
e
d
f
o
r
L
GF
an
d
C
ST
ev
en
ts
.
L
GF
cu
r
r
en
t
m
ag
n
it
u
d
e
was
lar
g
er
th
an
th
e
HI
f
au
lt'
s
s
m
all
cu
r
r
en
t.
Similar
d
ec
o
m
p
o
s
itio
n
p
r
o
ce
d
u
r
es we
r
e
ap
p
lie
d
to
ex
t
r
ac
t w
av
elet
co
ef
f
icien
ts
f
r
o
m
all
o
th
er
f
au
lt a
n
d
tr
a
n
s
ien
t e
v
en
ts
.
(
a
)
(
b
)
(
c)
(
d
)
Fig
u
r
e
7
.
R
esu
lts
o
f
d
ec
o
m
p
o
s
ed
s
ig
n
als:
(
a)
n
o
r
m
al
s
tate
,
(
b
)
HI
f
au
lt
,
(
c)
L
GF
,
an
d
(
d
)
C
ST
4
.2
.
Resul
t
s
o
f
cla
s
s
if
ica
t
io
n
T
h
e
s
tu
d
y
u
s
ed
attr
ib
u
tes
f
r
o
m
f
au
lts
an
d
tr
an
s
ien
t
s
ig
n
als
to
tr
ain
class
if
ier
s
u
s
in
g
a
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
tech
n
iq
u
e
[
2
3
]
.
T
h
e
d
is
cr
im
in
atio
n
o
f
HI
f
a
u
lt f
r
o
m
o
th
er
f
au
lts
an
d
tr
a
n
s
ien
ts
in
PV g
en
er
ated
MG
n
etwo
r
k
s
was
co
n
s
id
er
ed
.
T
h
e
ac
cu
r
ac
y
an
d
HI
f
au
lt
s
u
cc
ess
r
ate
o
f
en
s
em
b
le
(
SEC)
a
n
d
in
d
iv
id
u
al
(
NB
,
DT
J
,
an
d
KNN)
class
if
ier
s
w
er
e
ev
alu
ated
b
ased
o
n
co
n
f
u
s
io
n
m
atr
ix
o
u
tc
o
m
es.
T
h
e
d
e
f
in
itio
n
o
f
ac
cu
r
ac
y
an
d
s
u
cc
ess
r
ate
o
f
HI
f
au
lt is
ex
p
r
ess
ed
as
(
8
)
a
n
d
(
9
)
[
3
]
.
C
las
s
if
icatio
n
a
cc
u
r
ac
y
=
C
or
r
e
c
tl
y
c
las
s
if
ied
ins
tanc
e
s
T
otal
numb
e
r
of
ins
tanc
e
s
×
1
0
0
%
(
8
)
HI
f
au
lt su
cc
esiv
e
r
ate
=
C
or
r
e
c
tl
y
c
las
s
if
ied
ins
tanc
e
s
of
e
ve
nt
T
otal
numb
e
r
of
ins
tanc
e
s
of
e
ve
nt
×
1
0
0
%
(
9
)
4
.
2
.
1
.
Cla
s
s
if
ica
t
io
n
a
na
ly
s
is
:
i
n P
V
co
nn
ec
t
ed
MG
net
wo
rk
(
un
der
s
t
a
nd
a
rd
t
est
co
nd
it
io
n
)
T
h
is
an
aly
s
is
d
is
tin
g
u
is
h
ed
b
etwe
en
f
au
lts
an
d
tr
an
s
ien
ts
in
PV
-
g
en
er
ated
MG
p
o
wer
s
y
s
t
em
.
Fro
m
th
e
r
esu
lts
o
f
c
o
n
f
u
s
io
n
m
atr
ix
(
T
ab
les
2
to
5
)
,
class
if
icatio
n
ac
cu
r
ac
y
,
H
I
f
a
u
lt
s
u
cc
ess
r
ate,
an
d
p
er
f
o
r
m
a
n
ce
in
d
ices
wer
e
ass
es
s
ed
.
Acc
o
r
d
in
g
to
t
h
e
r
esu
lts
,
th
e
NB
class
if
ier
in
co
r
r
ec
tly
class
if
ied
1
4
0
in
s
tan
ce
s
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Hig
h
imp
ed
a
n
ce
fa
u
lt d
is
crimin
a
tio
n
in
micro
g
r
id
p
o
w
er sys
tem
u
s
in
g
…
(
A
r
a
n
g
a
r
a
ja
n
V
i
n
a
ya
g
a
m
)
105
co
m
p
ar
ed
t
o
8
0
a
n
d
7
0
f
o
r
DT
J
(
9
0
%
an
d
8
2
%)
an
d
KNN,
r
esp
ec
tiv
ely
.
T
h
is
r
esu
lted
in
lo
wer
ac
cu
r
ac
y
a
n
d
HI
f
au
lt
s
u
cc
ess
r
ate
co
m
p
a
r
e
d
to
DT
J
(
9
1
.
2
5
%
a
n
d
7
8
%)
a
n
d
KNN
(
9
1
.
2
5
%
an
d
8
8
%)
c
lass
if
ier
s
.
Stack
in
g
class
if
ier
h
ad
h
ig
h
er
ac
cu
r
ac
y
(
9
6
.
2
5
%)
an
d
HI
f
au
lt
s
u
cc
e
s
s
r
ate
(
9
2
%)
th
an
b
ase
class
i
f
ier
s
.
T
h
e
s
tack
in
g
class
if
ier
h
ad
3
0
m
is
class
if
ie
d
in
s
tan
ce
s
,
th
e
f
ewe
s
t.
Ov
er
all,
th
e
p
r
o
p
o
s
ed
s
tack
in
g
m
o
d
el
s
u
r
p
ass
es
b
ase
class
if
icatio
n
m
eth
o
d
s
with
r
esp
ec
t
to
ac
cu
r
ac
y
an
d
r
ate
o
f
s
u
cc
ess
in
d
etec
tin
g
HI
Fau
lt
in
PV
-
co
n
n
ec
te
d
MG
n
etwo
r
k
s
.
T
ab
le
2
.
C
lass
if
icatio
n
r
esu
lts
:
NB
c
lass
if
ier
Ev
e
n
t
s
C
l
a
s
ses
C
S
1
C
S
2
C
S
3
C
S
4
C
S
5
C
S
6
C
S
7
C
S
8
I
n
c
o
r
r
e
c
t
l
y
c
l
a
ss
i
f
i
e
d
C
o
r
r
e
c
t
l
y
c
l
a
ssi
f
i
e
d
N
o
r
mal
C
S
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
LG
F
C
S
2
12
88
0
0
0
0
0
0
12
88
LLG
F
C
S
3
0
0
82
10
0
0
8
0
18
82
LLLGF
C
S
4
4
12
4
80
0
0
0
0
20
80
LLF
C
S
5
10
10
0
0
80
0
0
0
20
80
H
I
f
a
u
l
t
C
S
6
0
0
0
0
0
80
0
20
20
80
LST
C
S
7
0
0
12
0
0
0
62
26
38
62
C
S
T
C
S
8
0
12
0
0
0
0
0
88
12
88
O
v
e
r
a
l
l
a
c
c
u
r
a
c
y
=
8
2
.
5
%
a
n
d
s
u
c
c
e
s
s rat
e
o
f
H
I
f
a
u
l
t
=
8
0
%
T
ab
le
3
.
C
lass
if
icatio
n
r
esu
lts
:
DT
J
c
lass
if
ier
Ev
e
n
t
s
C
l
a
s
ses
C
S
1
C
S
2
C
S
3
C
S
4
C
S
5
C
S
6
C
S
7
C
S
8
I
n
c
o
r
r
e
c
t
l
y
c
l
a
ss
i
f
i
e
d
C
o
r
r
e
c
t
l
y
c
l
a
ssi
f
i
e
d
N
o
r
mal
C
S
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
LG
F
C
S
2
0
96
4
0
0
0
0
0
4
96
LLG
F
C
S
3
0
10
90
0
0
0
0
0
10
90
LLLGF
C
S
4
0
0
4
96
0
0
0
0
4
96
LLF
C
S
5
0
12
0
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.
C
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if
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KNN
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Ev
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t
s
C
l
a
s
ses
C
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S
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C
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SEC
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2
.
2
.
Resul
t
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f
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ly
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PM)
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2
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Ev
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t
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l
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s
ses
C
S
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S
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3
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S
4
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5
C
S
6
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S
7
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Evaluation Warning : The document was created with Spire.PDF for Python.
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(
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(
)
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p
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p
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ates
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ated
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aly
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u
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icati
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t
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2
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A
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C
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1
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0
5
95
3
97
1
99
C
S
2
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0
4
96
2
98
2
98
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S
3
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4
96
3
97
1
99
C
S
4
1
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0
6
94
3
97
1
99
C
S
5
90
15
85
12
88
11
89
C
S
6
92
16
84
12
88
10
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S
7
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13
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11
89
10
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S
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5
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f
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I
f
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l
t
9
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8
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9
0
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5.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
p
r
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p
o
s
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a
s
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g
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em
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le
class
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ier
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d
if
f
e
r
en
tiate
b
etwe
en
HI
f
au
lts
an
d
tr
an
s
ien
t
s
in
a
PV
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g
en
e
r
ated
Mic
r
o
g
r
id
p
o
wer
s
y
s
tem
.
T
h
e
DW
T
ap
p
r
o
ac
h
is
em
p
l
o
y
ed
in
th
e
p
r
e
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s
tag
e
o
f
d
ata
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n
aly
s
is
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ex
tr
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t
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ain
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et
f
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tu
r
es
f
r
o
m
tr
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s
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ts
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d
f
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lt
ev
e
n
ts
.
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h
e
p
r
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p
o
s
ed
s
tack
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g
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o
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el
h
as
two
s
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s
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in
g
in
d
iv
id
u
al
class
if
ier
s
(
NB
,
DT
J
,
an
d
KNN)
u
s
in
g
a
1
0
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f
o
l
d
cr
o
s
s
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v
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n
m
eth
o
d
an
d
lear
n
in
g
th
e
m
eta
-
lear
n
er
(
KNN)
to
o
b
ta
in
tar
g
eted
class
lab
els.
T
h
e
o
u
tco
m
es
f
r
o
m
th
is
s
tu
d
y
ar
e
as
f
o
llo
ws:
i)
Acc
o
r
d
in
g
to
th
e
o
u
tco
m
es
o
f
th
e
an
al
y
s
is
in
th
e
MG
n
et
wo
r
k
(
at
STC),
t
h
e
s
u
g
g
ested
m
o
d
el
o
u
tp
er
f
o
r
m
s
NB
,
DT
J
,
an
d
KNN
wi
th
r
es
p
ec
t
to
ac
cu
r
ac
y
(
9
6
.
2
5
%)
an
d
r
ate
o
f
s
u
cc
ess
(
9
2
%)
in
d
e
tectin
g
HI
f
au
lts
.
ii)
T
h
e
s
u
g
g
ested
s
tack
in
g
m
o
d
e
l
ac
h
iev
es
9
1
%
ac
cu
r
ac
y
a
n
d
8
4
%
s
u
cc
ess
r
ate
in
d
etec
tin
g
HI
f
au
lts
,
ev
en
in
n
o
is
y
ev
en
t
d
ata.
iii)
T
h
e
p
er
f
o
r
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[
1
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N
.
H
a
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[
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