Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
7,
No.
3,
June
2017,
pp.
1112
–
1124
ISSN:
2088-8708
1112
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
Comparati
v
e
Study
in
Determining
F
eatur
es
Extraction
f
or
Islanding
Detection
using
Data
Mining
T
echnique:
Corr
elation
and
Coefficient
Analysis
Aziah
Khamis
*1
,
Y
an
Xu
2
,
and
Azah
Mohamed
3
1
F
aculty
of
Electrical
Engineering,
Uni
v
ersiti
T
eknikal
Malaysia
Melaka,
Malaysia
1
School
of
Electrical
and
Information
Engineering,
The
Uni
v
ersity
of
Sydne
y
,
NSW
,
Australia
2
School
of
Electrical
and
Electronic
Engineering,
Nan
yang
T
echnological
Uni
v
ersity
,
Sing
apore
3
Department
of
Electrical,
Electronic
and
System
Engineering,
Uni
v
ersiti
K
ebangsaan
Malaysia,
Malaysia
Article
Inf
o
Article
history:
Recei
v
ed
Oct
24,
2016
Re
vised
Feb
8,
2017
Accepted
Feb
22,
2017
K
eyw
ord:
Islanding
Detection
Distrib
uted
Generation
Data-mining
Random
F
orest
ABSTRA
CT
A
comprehensi
v
e
comparison
study
on
the
data
mining
based
approaches
for
detecting
is-
landing
e
v
ents
in
a
po
wer
distrib
ution
system
with
in
v
erter
-based
distrib
uted
generations
is
presented.
The
important
features
for
each
phase
in
the
island
detection
scheme
are
in
v
esti-
g
ated
in
detail.
These
features
are
e
xtracted
from
the
time-v
arying
measurements
of
v
oltage,
frequenc
y
and
total
harmonic
distortion
(THD)
of
current
and
v
oltage
at
the
point
of
com-
mon
coupling.
Numerical
studies
were
conducted
on
the
IEEE
34-b
us
system
considering
v
arious
scenarios
of
islanding
and
non-islanding
conditions.
The
features
obtained
are
then
used
to
train
se
v
eral
data
mining
techniques
such
as
decision
tree,
support
v
ector
machine,
neural
netw
ork,
bagging
and
random
forest
(RF).
The
simulation
results
sho
wed
that
the
im-
portant
feature
paramet
ers
can
be
e
v
aluated
based
on
the
correlation
between
the
e
xtracted
features.
From
the
results,
the
four
important
features
that
gi
v
e
accurate
islanding
detec-
tion
are
the
fundamental
v
oltage
THD,
fundamental
current
THD,
rate
of
change
of
v
oltage
magnitude
and
v
oltage
de
viation.
Comparison
studies
demonstrated
the
ef
fecti
v
eness
of
the
RF
method
in
achie
ving
high
accurac
y
for
islanding
detection.
Copyright
c
2017
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Aziah
Khamis
F
aculty
of
Electrical
Engineering,
Uni
v
ersiti
T
eknikal
Malaysia
Melaka,
Malaysia.
Uni
v
ersiti
T
eknikal
Malaysia
Melaka,Hang
T
uah
Jaya,
76100
Durian
T
ungg
al,
Melaka,
Malaysia.
aziah83@gmail.com
1.
INTR
ODUCTION
A
small
localized
po
wer
source
called
as
distrib
uted
generation
(DG)
bec
omes
an
alternati
v
e
to
b
ulk
electric
generation
due
to
yearly
demand
gro
wth.
These
DGs
can
be
in
the
form
of
wind
f
arm,
micro
h
ydro
turbine
and
photo
v
oltaic
(PV)
generator
.
Generall
y
,
these
DGs
are
in
the
range
of
kW
up
to
MW
with
se
v
eral
adv
antages
such
as
en
vironmental
benefits,
impro
v
ed
reliabil
ity
,
increased
ef
ficienc
y
,
impro
v
ed
po
wer
quality
and
reduced
transmission
and
distrib
ution
line
losses
[1–3].
Ho
we
v
er
,
one
of
the
major
dra
wbacks
of
DGs
is
when
subjected
to
islanding
mode
of
operation.
Islanding
is
referred
as
disconnection
of
the
main
source
in
which
it
can
be
operated
either
intentional
or
unintentional.
When
disconnection
occurs,
the
acti
v
e
part
of
the
distrib
ution
system
should
sense
the
disconnection
from
the
main
grid
and
shut
do
wn
the
DGs,
where
island
operation
is
prohibited
or
control
action
must
be
acti
v
ated
to
stabilize
the
islanded
part
of
system
[4,
5].
Islanding
operation
has
some
benefits
b
ut
se
v
eral
dra
wbacks
are
still
observ
ed,
especially
in
unintentional
islanding
e
v
ents
which
may
cause
problems
related
to
po
wer
quality
,
safety
,
v
oltage
and
frequenc
y
stabilities,
and
interference
[6,
7].
V
arious
techniques
ha
v
e
been
de
v
eloped
to
detect
islanding.
Islanding
techniques
can
generally
be
classified
into
remote
and
local
methods.
Remote
methods
are
based
on
communication
between
the
po
wer
utility
and
the
DGs.
Remote
methods
are
highly
reliable,
b
ut
the
practical
implementation
of
these
schemes
can
be
infle
xible,
comple
x
and
J
ournal
Homepage:
http://iaesjournal.com/online/inde
x.php/IJECE
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
,
DOI:
10.11591/ijece.v7i3.pp1112-1124
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1113
e
xpensi
v
e.
F
or
instance,
the
cost
of
implementing
a
remote
method
can
be
e
xtremely
e
xpensi
v
e
especially
when
it
is
implemented
in
netw
orks
that
do
not
initially
ha
v
e
an
y
communicati
on
infrastructure
with
the
po
wer
utility
.
Therefore,
local
methods
are
f
a
v
ourable
for
detecting
islanding
condition.
These
local
methods
can
be
cate
gorized
as
passi
v
e,
acti
v
e
and
h
ybrid
techniques
[8–10].
The
passi
v
e
islanding
detection
technique
monitors
t
he
system
parameters
such
as
v
oltage,
current,
frequenc
y
and
harmonic
distortion
at
the
point
of
common
coupling
(PCC)
with
the
utility
grid
for
detecting
e
v
ents
[3,
11–13].
In
the
acti
v
e
islanding
detection
technique,
disturbances
are
intentionally
injected
into
the
netw
ork
and
the
island
is
detected
based
on
the
system
responses
to
the
disturbances
[6,
14–16].
Meanwhile,
the
h
ybrid
technique
is
a
combination
of
the
acti
v
e
and
passi
v
e
techniques,
in
which
acti
v
e
technique
is
applied
only
if
islanding
is
not
detected
by
the
passi
v
e
technique
[3,
17–20].
Data
mining
is
widely
used
in
numerous
area
including
islanding
detection
[21–24].
F
or
instance,
an
intel-
ligent
islanding
detection
technique
w
as
de
v
eloped
in
[25]
using
decision
tree
(DT)
classifier
to
identify
and
classify
islanding
operations
at
specific
tar
get
locations.
Ho
we
v
er
,
the
DT
classifier
is
not
capable
in
capturing
all
possible
islanding
e
v
ents.
T
o
impro
v
e
the
accurac
y
of
the
DT
cl
assifier
,
fuzzy
rule-based
incorporated
wi
th
DT
w
as
utilized
in
detecting
the
islanding
e
v
ents
[26].
In
[13],
a
statistical
signal
processing
algorithm
is
applied
by
using
features
from
v
oltage
and
frequenc
y
w
a
v
eforms.
The
accurac
y
of
this
technique
is
acceptable,
b
ut
the
delay
in
statistical
pro-
cessing
mak
es
this
technique
slo
wer
than
other
islanding
detection
techniques.
Realizing
the
potential
of
dat
a
mining
techniques
for
islanding
detection,
ne
w
techniques
ha
v
e
been
de
v
eloped
by
combining
the
discrete
w
a
v
elet
transform
with
v
arious
classifiers,
namel
y
,
DT
,
probabilistic
neural-netw
ork
(PNN)
and
support
v
ector
machines
(SVM)
[27].
The
test
results
sho
wed
that
the
best
accurac
y
can
be
achie
v
ed
by
the
DT
classifier
model
[27].
In
[28]
a
pattern
recognition
approach
based
on
the
DT
classifier
w
as
emplo
yed
for
isl
anding
detection.
Ho
we
v
er
,
DT
classifier
ha
v
e
limitations,
suc
h
as
possibility
of
spurious
relationships,
possibility
of
duplication
with
the
same
sub-tree
on
dif
ferent
paths
and
limited
to
one
output
per
attrib
ute,
and
inability
to
represent
test
that
refer
to
tw
o
or
more
dif
ferent
objects,
which
requires
an
e
xploration
of
others
intelligent
technique.
On
the
basis
of
the
comprehe
n
s
i
v
e
literature
re
vie
w
,
the
data
mining
using
correlation
and
coef
ficient
analysis
had
rarely
been
reported.
Therefore,
the
main
objecti
v
e
of
this
study
is
to
propose
a
ne
w
islanding
scheme
using
the
correlation
and
coef
ficient
analysis
for
features
e
xtraction
and
data
mining
techniques.
Initially
,
features
are
e
xtracted
using
the
correlation
and
coef
ficient
analysis
in
which
se
v
en
parameter
indices
at
the
tar
get
DG
location
ha
v
e
been
identified
as
important
features
for
identifying
the
islanding
e
v
ents.
Then
fi
v
e
dif
ferent
data
mini
ng
techniques,
namely
,
DT
,
SVM,
neural
netw
ork
(NN),
bagging
and
random
for
-
est
(RF)
ha
v
e
been
de
v
eloped
as
classifiers
in
islanding
detection.
The
proposed
islanding
detection
scheme
is
tested
on
the
IEEE
34
b
us
system
with
in
v
erter
based
DGs.
2.
B
UILDING
THE
D
A
T
A
SET
2.1.
T
est
System
Fig.
1
sho
ws
the
single-line
diagram
of
the
IEEE
34-b
us
distrib
ution
system
model
in
MA
TLAB/SIMULINK
softw
are.
The
DG
and
the
load
are
connected
to
distrib
ution
system
by
a
100-kV
A
24.9-kV/480-V
transformer
.
Mean-
while,
the
PCC
is
connected
with
R
load
with
100-kW
.
The
DG
is
an
in
v
erter
-based
DG
with
current
controlled
interf
ace
using
the
same
control
units
in
the
pre
vious
study
[29].
Figure
1.
System
under
test:
IEEE
34-b
us
system.
2.2.
Database
Generation
V
arious
islanding
and
non-islanding
e
v
ents
should
be
generated
with
a
wide
range
of
dataset
for
training
the
classifier
.
The
possible
situations
that
may
create
islanding
and
non-islanding
conditions
are
gi
v
en
as
follo
ws:
i.
Load
and
capacitor
switching
at
dif
ferent
b
uses,
ii.
Se
v
eral
types
of
f
ault
at
dif
ferent
b
usses,
and
iii.
Ev
ent
that
can
trip
break
er
and
reclosers,
and
island
the
DG.
Compar
ative
Study
in
Determining
F
eatur
es
Extr
action
for
Islanding
Detection
Sc
heme
...
(Aziah
Khamis)
Evaluation Warning : The document was created with Spire.PDF for Python.
1114
ISSN:
2088-8708
The
abo
v
e
situations
are
simulated
under
possible
v
ariation
in
operating
condition
which
are
considered
as:
i.
Normal
DG
loading,
ii.
Dif
ferent
operating
points
that
cause
po
wer
mismatch
at
the
local
R
load
connected
at
b
us
848.
2.3.
F
eatur
es
Selection
The
main
idea
of
features
selection
is
to
choose
the
most
significant
input
v
ariables
by
eliminating
features
with
non/less-predicti
v
e
information.
The
use
of
significant
features
can
greatly
impro
v
e
the
classifier
model
perfor
-
mance
and
thus,
increase
the
prediction
accurac
y
as
well
as
the
computational
speed.
In
this
paper
,
the
combination
of
v
arious
features
parameters
has
been
chosen
from
pre
vious
islanding
detection
methods
focusing
on
in
v
erter
-based-
DG.
The
e
xtracted
features
include
X
a
frequenc
y
de
viation
(
f),
X
b
v
oltage
de
viation
(
V),
X
c
rate
of
change
of
v
oltage
magnitude
(
V)/(
t),
X
d
fundamental
current
total
harmonic
distortion
(
T
H
D
C
f
),
X
e
current
total
har
-
monic
distortion
(
T
H
D
C
),
X
f
fundamental
v
oltage
total
harmonic
distortion
(
T
H
D
V
f
)
and
X
g
v
oltage
total
har
-
monic
distortion
(
T
H
D
V
).
The
features
are
e
xtracted
by
per
phase
basis
in
order
to
identify
the
most
essential
feature
parameters
for
islanding
detection.
Figs.
2
and
3
sho
w
e
xamples
of
features
signals
obtained
from
islanding
e
v
ent
for
phase-A
at
DG
terminal
in
distrib
ution
system.
The
signals
in
Figs.
2a-c
and
d-f
represents
the
v
oltage
and
frequenc
y
of
phase-A
during
islanding
condition
case,
respecti
v
ely
.
The
signals
in
Figs.
2b
and
c
are
the
v
oltage
de
viation
(
V)
and
rate
of
change
of
v
oltage
magnitude
(
V)/(
t),
respecti
v
ely
,
obtained
from
the
v
oltage
signal
of
Fig.
2a.
The
frequenc
y
signals
of
Fig.
2d
are
e
v
aluated
to
get
the
frequenc
y
de
viation
(
f)
as
illustrated
in
Fig.
2f.
Meanwhile,
the
information
of
THD
for
v
oltage
and
current
are
selected
as
sho
wn
in
Figs.
3a
and
b
.
The
entire
features
information
is
then
utilized
as
the
input
for
the
classifier
.
The
features
are
then
rearranged
and
e
xpressed
as,
I
nput
=
2
6
6
6
4
X
a
1
X
b
1
X
c
1
X
d
1
X
e
1
X
f
1
X
g
1
X
a
2
X
b
2
X
c
2
X
d
2
X
e
2
X
f
2
X
g
2
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
X
a
y
X
b
y
X
c
y
X
d
y
X
e
y
X
f
y
X
g
y
3
7
7
7
5
(1)
where
X
a
is
referred
to
the
frequenc
y
de
viation
(
f),
X
b
is
referred
to
the
v
oltage
de
viation
(
V),
X
c
is
referred
to
rate
of
change
of
v
ol
tage
magnitude
(
V)/(
t),
X
d
is
referred
current
total
harmonic
distortion
(
T
H
D
C
),
X
e
is
referred
to
fundamental
current
total
harmonic
distortion
(
T
H
D
C
f
),
X
f
is
referred
v
oltage
t
otal
harmonic
distortion
(
T
H
D
V
),
X
g
is
referred
to
fundamental
v
oltage
total
harmoni
c
distortion
(
T
H
D
V
f
)
and
y
is
referred
to
the
number
of
points
tak
en
after
the
disturbance
detected.
IJECE
V
ol.
7,
No.
3,
June
2017:
1112
–
1124
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1115
0
0.5
1
1.5
2
2.5
3
(a)
×
10
4
-2
0
2
Voltage
(V)
0
1000
2000
3000
4000
5000
6000
7000
8000
(b)
0
0.5
1
Normalized
deltaV
(V)
0
1000
2000
3000
4000
5000
6000
7000
8000
(c)
0
0.5
1
Normalized
dV/dt
(V/s)
0
1
2
3
4
5
6
7
(d)
×
10
5
59.5
60
60.5
Frequency (Hz)
0
1
2
3
4
5
6
7
8
9
10
(e)
×
10
4
59.8
60
60.2
Frequency
(Hz)
0
1
2
3
4
5
6
7
8
9
10
Sample Number
(f)
×
10
4
0.4
0.5
0.6
Normalized
deltaF
(Hz)
Figure
2.
Example
features
e
xtraction
for
islanding
case:
(a)
Phase
A
v
oltage
signal,
(b)
V
oltage
de
viation
(
V),
(c)
Rate
of
change
of
v
oltage
(
V)/(
t),
(d)
Phase
A
frequenc
y
signal,
(e)
Zoom
in
frequenc
y
a
fter
disturbance,
(f)
Frequenc
y
de
viation
(
f).
Figure
3.
Example
features
e
xtraction
for
islanding
case:
(a)
V
oltage
total
harmonic
distortion
(
T
H
D
V
),
(b)
Current
total
harmonic
distortion
(
T
H
D
C
).
Compar
ative
Study
in
Determining
F
eatur
es
Extr
action
for
Islanding
Detection
Sc
heme
...
(Aziah
Khamis)
Evaluation Warning : The document was created with Spire.PDF for Python.
1116
ISSN:
2088-8708
3.
FEA
TURE
EXTRA
CTION
USING
CORRELA
TION
AND
COEFFICIENT
AN
AL
YSIS
The
inclusion
of
irrele
v
ant
and
redundant
features
e
xtraction
in
the
classifier
model
may
results
in
poor
performance
in
classification
accurac
y
and
increases
the
computation
time.
T
o
obtain
high
classification
accurac
y
,
high
quality
of
features
need
to
be
e
xtracted
in
describing
the
islanding
e
v
ents
using
the
correlation
and
coef
ficient
analysis.
Fig.
4
sho
ws
the
correlation
between
28
features
v
ariable.
The
colours
and
shape
element
in
the
figure
are
used
to
sho
w
the
de
gree
of
correlation
[30].
The
v
ariables
are
said
to
ha
v
e
perfect
correlation
with
itself,
which
is
in
the
diagonal
lines
on
the
diagonal
of
the
graphic
(see
Fig.
4).
The
blue
colours
sho
ws
the
positi
v
e
v
alue,
whereas
the
red
for
ne
g
ati
v
e
v
alue
that
used
to
encode
the
sign
of
correlation.
Meanwhile,
filled
circled
means
positi
v
e
v
alue,
while
anti-clockwise
is
for
ne
g
ati
v
e
v
alues.
In
this
analysis,
the
Pearson
correlation
coef
ficient
is
utilized
to
measure
the
strength
between
28
v
ariable
features.
Mathematically
,
the
coef
ficient
is
e
xpressed
as
follo
ws:
r
=
N
P
k
l
(
P
k
)(
P
l
)
p
[
N
P
k
2
(
P
k
)
2
][
N
P
l
2
(
P
l
)
2
]
(2)
where
N
is
referred
to
number
of
pairs
of
scores,
P
k
l
is
referred
to
sum
of
the
products
of
paired
scores,
P
k
is
referred
to
sum
of
k
scores,
P
l
is
referred
to
sum
of
l
scores,
P
k
2
is
referred
to
sum
of
squared
k
scores,
and
P
l
2
is
referred
to
sum
of
squared
l
scores.
Figure
4.
V
isual
summary
of
correlation
between
the
28
candidate
attrib
utes
for
phase
A.
F
or
instance,
Fig.
4
sho
ws
that
the
most
positi
v
e
correlation
v
ariable
is
X
g
,
where
most
of
the
relationship
between
the
v
ariables
are
in
positi
v
e
v
alue.
The
relation
correlation
between
X
b
1
and
X
c
1
,
X
b
1
and
X
c
4
,
and
X
b
3
and
X
c
1
are
e
v
aluated
as
-0.6746369,
-0.6300237
and
-0.3214842,
respecti
v
ely
.
Therefore,
the
circle
with
red
colours
in
Fig.
4,
sho
w
the
ne
g
ati
v
e
correlated
between
X
b
and
X
c
.
This
finding
pro
v
es
that
X
b
is
the
most
ne
g
ati
v
e
correlation
between
the
features
as
sho
wn
in
Fig.
4.
The
significant
of
the
v
ariables
is
ag
ain
highlighted
by
the
importance
analysis
report
from
the
RF
learning
as
illustrated
in
Fig.
5.
The
figure
sho
ws
that
the
top
four
v
ariable
are
listed
as
[
X
g
;
X
e;
X
b;
X
c
]
.
The
out-of
bag
accurac
y-based
ranking
results
in
approximately
same
with
the
top
four
,
e
v
en
though
the
X
b
should
be
substituted
to
the
lo
wer
correlated
with
X
g
.
Similar
to
the
islanding
detection
classifier
procedure
adopted
to
phase-A,
classifier
t
raining
and
testing
data
set
procedures
are
applied
on
the
other
tw
o
phases
namely
,
phase
B
and
C.
Figs.
6(a)
and
6(b)
sho
ws
the
correlation
between
28
features
v
ariable
for
phase-B
and
C,
respecti
v
ely
.
The
figure
sho
ws
the
correlation
relationship
with
the
28
features
v
ariable
by
depicting
the
pattern
of
relations
among
the
v
ariables.
Meanwhile,
Fig.
7
sho
ws
that
the
important
beha
viour
report
from
the
RF
model
classifier
for
phase-B
and
C.
Phase-B
and
C
sho
w
an
equal
important
v
ariable.
The
observ
ation
lik
e
wise
re
v
eals
that
the
top
four
v
ariable
are
listed
as
[
X
g
;
X
e;
X
b;
X
c
]
.
IJECE
V
ol.
7,
No.
3,
June
2017:
1112
–
1124
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1117
Figure
5.
T
op-do
wn
importance
of
v
ariable
according
to
accurac
y
loss
or
misclassification
rate
reduction
(gini)
for
phase
A.
Compar
ative
Study
in
Determining
F
eatur
es
Extr
action
for
Islanding
Detection
Sc
heme
...
(Aziah
Khamis)
Evaluation Warning : The document was created with Spire.PDF for Python.
1118
ISSN:
2088-8708
(a)
(b)
Figure
6.
V
isual
summary
of
correlation
between
the
28
candidate
attrib
utes:
(a)
phase
B,
(b)
phase
C.
IJECE
V
ol.
7,
No.
3,
June
2017:
1112
–
1124
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1119
(a)
(b)
Figure
7.
T
op-do
wn
importance
of
v
ariable
according
to
accurac
y
loss
or
misclassification
rate
reduction
(gini):
(a)
phase
B,
(b)
phase
C.
Compar
ative
Study
in
Determining
F
eatur
es
Extr
action
for
Islanding
Detection
Sc
heme
...
(Aziah
Khamis)
Evaluation Warning : The document was created with Spire.PDF for Python.
1120
ISSN:
2088-8708
4.
IMPLEMENT
A
TION
OF
DECISION
TREE
AND
RANDOM
FOREST
AS
CLASSIFIERS
Fig.
8
illustrates
the
DT
structure
for
the
islanding
classification
model
for
in
v
erter
-based
DG
consists
of
8
nodes.
At
the
top
of
the
tree,
the
v
alue
of
X
e
is
first
compared
with
the
threshold
v
alue
0.632898
and
it
will
split
into
tw
o
descendent
subsets.
This
subset
is
then
split
into
se
v
eral
leaf
called
nodes
which
are
designated
by
a
class
label.
There
are
tw
o
class
label
in
this
s
tudy
,
namely
,
islanding
and
non-islanding
cases.
From
the
figure,
all
the
cases
ha
ving
X
e
within
0.63
and
0.65
are
predicted
as
non-islanding
state.
Ho
we
v
er
,
for
cases
with
X
e
less
than
0.63,
the
classification
depends
on
the
v
alue
of
X
b
and
X
c
.
Figure
8.
DT
generated
for
phase
A
considering
optimal
node
of
in
v
erter
based
DG.
Fig.
9
sho
ws
the
multidimensi
onal
scaling
(MDS)
plot
for
islanding
and
non-islanding
e
v
ents
utilizing
the
RF
classifier
.
This
MDS
is
used
to
disco
v
er
the
underlying
structure
of
distance
measured
between
objects.
The
MDS
assign
the
observ
ations
to
specific
locations
in
a
conceptual
space
(commonly
2
or
3
dimensional
space
used),
thus
the
distance
between
points
in
space
match
the
gi
v
en
dissimilarities
as
closely
as
possible.
Figure
9.
Multidimensional
scaling
plot
of
proximity
matrix
from
random
forest.
5.
TEST
RESUL
TS
The
simulation
data
were
obtained
using
MAL
T
AB/SIMULIK
softw
are
and
the
data
were
randomly
di
vided
into
training
and
testi
n
g
data
set
as
summarized
in
T
able
1.
The
features
are
e
xtracted
from
the
information
gi
v
en
in
(1).
The
open-source
softw
are,
Rattle
is
used
to
implements
the
con
v
entional
DT
,
bagging
and
RF
classifier
.
F
or
easy
comparison,
all
the
classifier
use
the
same
training
and
testing
data
sets
which
gi
v
es
tw
o
predictors
of
class
label
called
as
is
landing
and
non-islanding
e
v
ents.
T
able
2
sho
ws
classification
results
for
testing
data
set
of
phase
A
with
three
dif
ferent
classifie
rs,
namely
DT
,
bagging
and
RF
classifiers.
This
result
re
v
eal
s
that
the
highest
accurac
y
can
be
achie
v
ed
with
the
RF
classifier
with
percentage
classification
of
98
:
9%
and
100%
for
the
non-islanding
and
islanding
IJECE
V
ol.
7,
No.
3,
June
2017:
1112
–
1124
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
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1121
cases,
respecti
v
ely
.
T
able
1.
N
U
M
B
E
R
O
F
S
A
M
P
L
E
Non-islanding
Islanding
T
otal
T
raining
data
set
95
91
186
T
esting
data
set
91
94
185
T
able
2.
C
L
A
S
S
I
FI
C
A
T
I
O
N
R
E
S
U
L
T
S
O
N
T
E
S
T
I
N
G
D
A
T
A
S
E
T
S
F
O
R
P
H
A
S
E
A
Classifier
Model
No
of
Cases
Actual
Class
Non-islanding
Islanding
Classification
Accuracy
(%)
D
ecisionT
r
ee
91
N
on
isl
anding
78
4
85
:
71
94
I
sl
anding
13
90
95
:
74
B
ag
g
ing
91
N
on
isl
anding
89
1
97
:
80
94
I
sl
anding
2
93
98
:
94
R
andomF
or
est
91
N
on
isl
anding
90
0
98.90
94
I
sl
anding
1
94
100
Further
comparison
is
then
made
for
islanding
detection
using
SVM,
NN,
DT
,
bagging,
and
RF
classifiers
considering
all
the
three
phases.
The
perform
ances
of
accurac
y
of
these
classifiers
is
e
v
aluated
as
sho
wn
in
Fig.
10
and
T
able
3.
T
able
3
sho
w
the
accurac
y
of
the
fi
v
e
classifiers
for
islanding
detection
at
each
phase,
i.e,
phase-A,
B
and
C.
F
or
all
the
phases,
the
RF
classifier
gi
v
es
the
highest
accurac
y
compared
to
the
other
classifiers
in
detecting
islanding
e
v
ents
as
indicated
in
bold.
This
result
pro
v
es
that
the
best
classifier
model
to
predict
the
islanding
condition
based
on
per
phase
feature
e
xtraction
can
be
obtained
using
the
RF
classifier
.
A
B
C
Phases
0.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
Accuracies
SVM
NN
DT
Bagging
RF
Figure
10.
Accuracies
of
v
arious
model
Compar
ative
Study
in
Determining
F
eatur
es
Extr
action
for
Islanding
Detection
Sc
heme
...
(Aziah
Khamis)
Evaluation Warning : The document was created with Spire.PDF for Python.