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dec
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f
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power
q
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p
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m
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iza
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a
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o
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with
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rc
fu
rn
a
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s
(EAF
s).
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e
se
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u
strial
l
o
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d
s
a
re
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ll
-
k
n
o
w
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to
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a
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ted
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ti
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a
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m
II
(NSG
A
-
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lt
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k
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li
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d
ice
s:
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ic
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ist
o
rti
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(THD),
to
tal
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e
m
a
n
d
d
ist
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n
(TDD),
a
n
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v
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n
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e
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c
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n
a
d
d
it
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,
a
m
u
l
ti
-
c
rit
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ria
d
e
c
isio
n
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n
a
ly
sis
(M
CDA
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e
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a
p
p
li
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d
t
o
r
a
n
k
a
n
d
se
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th
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e
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n
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iffere
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t
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e
ra
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sc
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l
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it
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e
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o
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o
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h
a
rm
o
n
ic as
su
m
p
ti
o
n
s,
th
e
p
ro
p
o
se
d
a
p
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ro
a
c
h
a
c
c
o
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n
ts f
o
r
t
h
e
n
o
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li
n
e
a
r
a
n
d
ti
m
e
-
v
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ry
in
g
b
e
h
a
v
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o
r
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f
EAF
s,
e
n
su
rin
g
r
o
b
u
st
p
e
rfo
rm
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n
c
e
u
n
d
e
r
d
i
v
e
rse
o
p
e
ra
ti
n
g
c
o
n
d
i
ti
o
n
s.
A
c
o
m
p
re
h
e
n
siv
e
c
a
se
stu
d
y
b
a
se
d
o
n
a
B
o
l
iv
ian
ste
e
l
p
lan
t
il
lu
stra
tes
th
e
e
ffe
c
ti
v
e
n
e
ss
o
f
th
e
o
p
ti
m
iza
ti
o
n
stra
teg
y
.
Re
su
lt
s
in
d
ica
te
re
d
u
c
ti
o
n
s
o
f
4
7
.
6
%
in
THD,
3
3
.
5
%
i
n
TDD,
a
n
d
6
3
.
6
%
i
n
V
UF,
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rly
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t
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g
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o
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l
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p
p
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c
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s
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n
d
sig
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ifi
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a
n
tl
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imp
ro
v
i
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g
o
v
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ra
ll
p
o
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u
a
li
ty
.
Th
is
wo
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k
h
ig
h
li
g
h
ts
th
e
p
o
te
n
ti
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e
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ti
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a
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it
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m
s
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p
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c
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n
b
a
lan
c
e
d
p
o
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r
c
o
n
d
i
ti
o
n
s.
K
ey
w
o
r
d
s
:
E
lectr
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c
f
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ac
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Har
m
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to
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tio
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Mu
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b
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tim
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Pas
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f
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Steelm
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in
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T
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d
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CC B
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C
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p
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A
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:
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Yass
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Ma
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Yu
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a
I
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s
titu
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léctr
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Un
iv
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s
id
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Nac
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San
J
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an
(
UNSJ
)
–
C
ONI
C
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Av
.
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tad
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Gr
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San
Ma
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1
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5
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San
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Ar
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1.
I
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D
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teel
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tr
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f
ac
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t
ch
allen
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C
O₂
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f
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s
m
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in
r
esp
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tr
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en
t
en
v
ir
o
n
m
en
tal
r
e
g
u
latio
n
s
[
1
]
.
I
n
t
h
is
co
n
tex
t,
elec
tr
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c
f
u
r
n
ac
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(
E
AFs
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h
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lid
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as
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m
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ab
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r
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m
i
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t
tech
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teel
p
r
o
d
u
ctio
n
in
th
e
c
o
m
in
g
d
ec
ad
es
[
2
]
,
[
3
]
.
Ho
we
v
er
,
t
h
eir
o
p
e
r
atio
n
in
tr
o
d
u
ce
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ican
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tr
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g
r
id
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s
u
c
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as
h
ar
m
o
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is
to
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tio
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,
v
o
ltag
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u
n
b
alan
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,
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d
r
ap
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d
v
o
ltag
e
f
lu
ctu
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s
(
f
lick
er
)
.
T
h
ese
co
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d
itio
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s
m
ay
in
c
r
ea
s
e
en
er
g
y
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s
s
es
with
in
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e
p
la
n
t’
s
in
ter
n
al
n
etwo
r
k
,
i
m
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air
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h
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p
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o
r
m
a
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o
f
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eq
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ip
m
en
t,
a
n
d
h
in
d
er
co
m
p
lian
ce
with
p
o
wer
q
u
a
lity
s
tan
d
ar
d
s
[
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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E
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I
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N:
2252
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8
7
9
2
Mu
lti
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b
jective
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p
timiz
a
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a
n
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mu
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-
crit
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d
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a
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f p
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A
lva
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Ya
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if Ma
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1201
C
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en
tly
,
p
ass
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p
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ilte
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s
(
PP
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)
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a
wid
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u
s
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a
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d
co
s
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-
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itig
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ar
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d
u
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tr
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tio
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AF
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ased
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th
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PC
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.
Nev
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an
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wh
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ac
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h
ig
h
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ar
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a
n
d
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o
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lin
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o
f
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s
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ch
as E
AF
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wh
o
s
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a
r
m
o
n
ic
an
d
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ter
h
ar
m
o
n
ic
s
p
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tr
a
d
y
n
am
ically
f
lu
ctu
ate
th
r
o
u
g
h
o
u
t th
e
m
eltin
g
p
r
o
ce
s
s
[
5
]
,
[
6
]
.
T
o
o
v
er
co
m
e
th
ese
lim
itatio
n
s
,
s
e
v
er
al
o
p
tim
al
f
ilter
d
esig
n
m
eth
o
d
o
lo
g
ies
h
av
e
b
ee
n
p
r
o
p
o
s
ed
,
in
clu
d
in
g
class
ical
n
o
n
lin
ea
r
o
p
tim
izatio
n
ap
p
r
o
ac
h
es
[
7
]
,
[
8
]
,
m
etah
eu
r
is
tic
tech
n
iq
u
es
s
u
ch
as
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
[
9
]
-
[
1
1
]
,
an
d
m
o
r
e
r
e
ce
n
t
m
eth
o
d
s
s
u
c
h
as
s
im
u
ltan
eo
u
s
p
er
t
u
r
b
atio
n
s
to
ch
asti
c
ap
p
r
o
x
i
m
atio
n
(
SP
SA)
an
d
d
y
n
a
m
ic
p
a
r
ticle
s
war
m
o
p
tim
izatio
n
(
DPSO)
[
1
2
]
,
[
1
3
]
.
M
o
s
t
o
f
th
ese
s
tu
d
ies,
h
o
wev
er
,
ad
o
p
t
a
s
in
g
le
-
o
b
jectiv
e
p
er
s
p
ec
tiv
e,
f
o
c
u
s
in
g
ex
clu
s
iv
ely
o
n
r
ed
u
cin
g
h
ar
m
o
n
ic
in
d
ices
with
o
u
t
jo
in
tly
ad
d
r
ess
in
g
o
th
er
e
q
u
ally
cr
itical
in
d
icato
r
s
in
E
AF
o
p
e
r
atio
n
,
s
u
ch
as
v
o
ltag
e
u
n
b
ala
n
ce
.
Simu
ltan
eo
u
s
m
itig
atio
n
o
f
t
h
ese
in
d
ices
is
th
er
ef
o
r
e
cr
u
c
ial
in
in
d
u
s
tr
ial
n
etwo
r
k
s
wit
h
E
AFs
,
wh
ich
ar
e
am
o
n
g
t
h
e
m
o
s
t d
is
tu
r
b
in
g
lo
a
d
s
in
ter
m
s
o
f
p
o
wer
q
u
ality
[
1
4
]
.
I
n
th
is
co
n
tex
t,
th
e
p
r
esen
t
s
tu
d
y
p
r
o
p
o
s
es
a
m
eth
o
d
o
lo
g
y
f
o
r
th
e
o
p
tim
al
tu
n
in
g
o
f
a
PP
F
in
a
s
teel
p
lan
t
o
p
e
r
atin
g
with
an
E
AF.
T
h
e
ap
p
r
o
ac
h
co
m
b
in
es
th
e
non
-
d
o
m
in
ated
s
o
r
tin
g
g
en
eti
c
alg
o
r
ith
m
I
I
with
m
u
lti
-
cr
iter
ia
d
ec
is
io
n
an
al
y
s
is
.
NSGA
-
I
I
is
ad
o
p
ted
b
ec
au
s
e
o
f
its
p
r
o
v
en
ef
f
icien
cy
in
s
o
lv
i
n
g
n
o
n
lin
ea
r
m
u
lti
-
o
b
jectiv
e
p
r
o
b
lem
s
,
its
ab
ilit
y
to
ap
p
r
o
x
im
ate
th
e
Par
eto
f
r
o
n
t
with
g
o
o
d
co
n
v
er
g
en
ce
a
n
d
d
iv
er
s
ity
,
an
d
its
ex
ten
s
iv
e
u
s
e
in
p
o
wer
s
y
s
tem
s
o
p
tim
izatio
n
.
T
h
is
m
a
k
es it
p
ar
ticu
lar
ly
s
u
itab
le
f
o
r
a
d
d
r
es
s
in
g
th
e
co
n
f
lictin
g
o
b
jectiv
es
in
h
er
e
n
t
to
p
o
wer
q
u
ality
im
p
r
o
v
em
en
t
in
E
A
F
-
b
ased
n
etwo
r
k
s
.
T
h
e
o
p
ti
m
izatio
n
p
r
o
b
lem
is
f
o
r
m
u
lated
as
a
n
o
n
lin
ea
r
m
u
lti
-
o
b
jectiv
e
p
r
o
b
lem
,
s
im
u
ltan
eo
u
s
ly
m
i
n
im
izin
g
th
r
ee
p
o
wer
q
u
ality
in
d
ices:
to
tal
h
ar
m
o
n
ic
d
is
to
r
tio
n
(
T
H
D)
,
to
tal
d
em
an
d
d
is
to
r
tio
n
(
T
DD)
,
an
d
th
e
v
o
ltag
e
u
n
b
ala
n
c
e
f
ac
to
r
(
VUF)
.
T
h
e
g
o
al
is
to
id
en
tify
f
ilter
co
n
f
i
g
u
r
atio
n
s
th
at
ac
h
iev
e
a
n
ap
p
r
o
p
r
iate
tr
ad
e
-
o
f
f
a
m
o
n
g
th
ese
in
d
icato
r
s
,
wh
ile
en
s
u
r
in
g
r
o
b
u
s
t p
e
r
f
o
r
m
an
ce
u
n
d
er
d
i
v
er
s
e
o
p
e
r
atin
g
co
n
d
iti
o
n
s
o
f
th
e
E
AF m
eltin
g
p
r
o
ce
s
s
.
T
h
e
m
ain
co
n
t
r
ib
u
tio
n
o
f
th
is
wo
r
k
lies
in
th
e
s
im
u
ltan
eo
u
s
m
in
im
izatio
n
o
f
T
HD,
T
DD
,
an
d
VUF
u
s
in
g
NSGA
-
I
I
co
m
b
in
ed
with
m
u
lti
-
cr
iter
ia
d
ec
is
io
n
an
aly
s
is
(
MCD
A
)
,
wh
ich
,
to
t
h
e
b
es
t o
f
o
u
r
k
n
o
wled
g
e,
h
as
n
o
t
b
ee
n
p
r
ev
io
u
s
ly
r
ep
o
r
ted
f
o
r
E
AF
-
b
ased
s
teelm
ak
in
g
f
ac
ilit
ies.
T
h
is
p
ap
er
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
Sectio
n
2
an
aly
ze
s
th
e
o
p
er
ati
o
n
o
f
E
AFs
an
d
th
eir
im
p
ac
t
o
n
p
o
wer
q
u
ality
.
Sectio
n
3
p
r
e
s
en
ts
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
,
in
cl
u
d
in
g
t
h
e
h
y
p
er
b
o
lic
m
o
d
el
o
f
th
e
f
u
r
n
ac
e
,
th
e
im
p
lem
en
tatio
n
o
f
NSGA
-
I
I
,
an
d
th
e
a
d
o
p
te
d
MCDA
f
r
am
ewo
r
k
.
Sectio
n
4
d
is
cu
s
s
es
th
e
o
b
tain
ed
r
esu
lts
an
d
co
m
p
a
r
es
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
o
p
tim
ized
d
esig
n
ag
ain
s
t c
o
n
v
en
tio
n
al
a
p
p
r
o
ac
h
es.
Fin
ally
,
Sectio
n
5
s
u
m
m
ar
izes th
e
m
ain
c
o
n
clu
s
io
n
s
o
f
th
is
wo
r
k
.
2.
T
E
CH
N
I
CA
L
B
A
CK
G
RO
UND
2
.
1
.
M
elt
ing
pro
ce
s
s
in ele
ct
ric
s
t
ee
lm
a
k
ing
f
urna
ce
s
E
AFs
ar
e
f
u
n
d
am
en
tal
e
q
u
ip
m
en
t
in
m
o
d
e
r
n
s
teel
p
lan
ts
,
u
s
e
d
to
m
elt
s
cr
ap
s
teel
th
r
o
u
g
h
el
ec
tr
ic
ar
cs
ex
ce
ed
in
g
1
0
k
A
an
d
tem
p
er
at
u
r
es a
b
o
v
e
3
,
0
0
0
°C
.
T
h
is
p
r
o
ce
s
s
co
n
s
u
m
es lar
g
e
am
o
u
n
ts
o
f
elec
tr
ical
en
er
g
y
an
d
in
tr
o
d
u
ce
s
s
ig
n
if
ican
t
d
is
tu
r
b
an
ce
s
in
to
t
h
e
p
o
wer
s
u
p
p
ly
s
y
s
tem
[
1
5
]
.
T
h
e
m
eltin
g
p
r
o
ce
s
s
co
m
p
r
is
es
s
ev
er
al
s
tag
es,
ea
ch
with
s
p
ec
i
f
ic
o
p
er
atin
g
ch
ar
ac
te
r
is
tics
th
at
af
f
ec
t
p
o
wer
q
u
ality
in
d
if
f
e
r
en
t
way
s
an
d
with
v
ar
y
in
g
d
eg
r
ee
s
o
f
s
ev
er
ity
[
1
6
]
.
T
h
ese
s
tag
es
ca
n
b
e
d
iv
id
e
d
in
to
f
o
u
r
m
ain
p
h
ases
(
Fig
u
r
e
1
)
,
ea
ch
ass
o
ciate
d
with
d
is
tin
ct
en
er
g
y
c
o
n
s
u
m
p
t
io
n
p
atter
n
s
,
ar
c
d
y
n
am
ics,
an
d
im
p
ac
ts
o
n
th
e
g
r
id
.
Fig
u
r
e
1
.
Ma
in
o
p
er
atin
g
s
tag
es o
f
th
e
E
AF steelm
ak
in
g
p
r
o
ce
s
s
I
n
th
e
b
o
r
in
g
s
tag
e,
t
h
e
elec
tr
o
d
es
d
escen
d
to
estab
lis
h
th
e
f
ir
s
t
ar
cs,
p
ier
cin
g
t
h
e
u
p
p
er
s
cr
ap
lay
e
r
an
d
cr
ea
tin
g
a
p
it
th
at
en
ab
le
s
h
ea
t
p
en
et
r
atio
n
i
n
to
th
e
ch
ar
g
e.
T
h
is
s
tag
e
is
h
ig
h
ly
u
n
s
tab
le
an
d
g
en
e
r
ates
s
ev
er
e
f
lu
ctu
atio
n
s
.
Du
r
in
g
th
e
m
eltin
g
s
t
ag
e,
o
n
ce
th
e
p
it
i
s
f
o
r
m
e
d
,
t
h
e
elec
tr
o
d
es
ar
e
s
u
r
r
o
u
n
d
ed
b
y
s
cr
a
p
,
allo
win
g
an
in
cr
ea
s
e
in
ar
c
len
g
th
an
d
f
u
r
n
ac
e
p
o
wer
t
o
ac
ce
ler
ate
m
eltin
g
—
co
n
d
itio
n
s
th
at
in
ten
s
if
y
h
ar
m
o
n
ic
d
is
to
r
tio
n
an
d
r
ea
ctiv
e
p
o
wer
d
em
an
d
.
T
h
e
p
latin
g
s
tag
e
b
eg
in
s
wh
en
t
h
e
m
o
lte
n
b
ath
is
f
o
r
m
ed
an
d
t
h
e
r
em
ain
in
g
s
o
lid
m
ater
ial
is
m
elted
.
As
th
e
elec
tr
o
d
es
ap
p
r
o
ac
h
th
e
m
o
lten
b
ath
,
th
e
ar
c
s
tab
ilizes,
r
ed
u
cin
g
f
lu
ctu
atio
n
s
b
u
t
s
till
co
n
tr
ib
u
tin
g
to
d
is
to
r
tio
n
.
Fin
ally
,
in
th
e
r
ef
in
in
g
s
tag
e,
th
e
ch
ar
g
e
is
f
u
lly
m
o
lten
,
an
d
ch
em
ica
l
an
d
th
er
m
al
a
d
ju
s
tm
en
ts
ar
e
ca
r
r
ie
d
o
u
t,
with
a
p
r
o
g
r
ess
iv
e
r
ed
u
cti
o
n
in
ar
c
len
g
th
a
n
d
p
o
wer
.
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
.
3
,
Sep
tem
b
er
20
2
6
:
1
2
0
0
-
1
2
1
1
1202
Alth
o
u
g
h
t
h
is
s
tag
e
is
co
m
p
ar
ativ
ely
m
o
r
e
s
tab
le,
it
c
o
n
tin
u
es
to
af
f
ec
t
th
e
s
u
p
p
ly
s
y
s
tem
,
p
ar
ticu
lar
ly
th
r
o
u
g
h
u
n
b
alan
ce
a
n
d
r
esid
u
al
h
ar
m
o
n
ics.
2
.
2
.
P
o
wer
qu
a
lity
is
s
ues
a
s
s
o
cia
t
ed
wit
h E
AF
o
pera
t
io
n
T
h
e
f
lu
ctu
atin
g
an
d
n
o
n
lin
ea
r
b
eh
av
io
r
o
f
elec
tr
ic
ar
cs
m
ak
es
E
AFs
o
n
e
o
f
th
e
m
o
s
t
d
is
tu
r
b
in
g
in
d
u
s
tr
ial
lo
ad
s
f
o
r
elec
tr
ic
al
n
etwo
r
k
s
.
T
h
eir
o
p
er
atio
n
g
en
e
r
ates
a
wid
e
s
p
ec
tr
u
m
o
f
h
a
r
m
o
n
ics
,
in
ter
h
ar
m
o
n
ics
,
v
o
ltag
e
f
lick
e
r
,
an
d
p
h
ase
u
n
b
alan
ce
s
[
1
7
]
.
T
h
ese
d
is
tu
r
b
an
ce
s
ca
n
p
r
o
p
ag
ate
th
r
o
u
g
h
th
e
s
u
p
p
ly
s
y
s
tem
,
af
f
ec
tin
g
n
o
t o
n
ly
th
e
s
teel
p
lan
t
its
elf
b
u
t
also
o
th
e
r
u
s
er
s
co
n
n
ec
ted
to
t
h
e
s
am
e
g
r
id
.
Am
o
n
g
th
e
m
o
s
t im
p
o
r
ta
n
t p
o
wer
q
u
al
ity
in
d
ices in
f
lu
en
ce
d
b
y
E
AF o
p
er
atio
n
a
r
e:
i)
T
o
tal
h
ar
m
o
n
ic
d
is
to
r
tio
n
(
T
H
D)
:
r
atio
o
f
th
e
R
MS
v
alu
e
o
f
all
h
ar
m
o
n
ic
co
m
p
o
n
e
n
ts
to
th
e
R
MS
v
alu
e
o
f
th
e
f
u
n
d
am
en
tal
co
m
p
o
n
e
n
t o
f
v
o
ltag
e
o
r
cu
r
r
en
t (
1
)
,
p
r
o
v
i
d
in
g
a
m
ea
s
u
r
e
o
f
wav
e
f
o
r
m
d
is
to
r
tio
n
.
ii)
T
o
tal
d
em
an
d
d
is
to
r
tio
n
(
T
DD
)
:
r
atio
o
f
th
e
R
MS
v
alu
e
o
f
h
a
r
m
o
n
ic
cu
r
r
en
ts
to
th
e
m
ax
im
u
m
d
em
an
d
lo
a
d
cu
r
r
en
t (
2
)
,
p
r
o
v
id
in
g
a
n
o
r
m
a
lized
m
ea
s
u
r
e
o
f
c
u
r
r
e
n
t d
is
to
r
tio
n
.
iii)
Vo
ltag
e
u
n
b
alan
ce
f
ac
to
r
(
V
UF)
:
r
atio
o
f
th
e
n
eg
ativ
e
-
s
eq
u
en
ce
co
m
p
o
n
en
t
to
th
e
p
o
s
itiv
e
-
s
eq
u
en
ce
co
m
p
o
n
en
t o
f
th
e
f
u
n
d
am
e
n
tal
v
o
ltag
e
(
3
)
,
in
d
icatin
g
th
e
t
h
r
ee
-
p
h
ase
asy
m
m
etr
y
.
=
√
∑
(
)
2
=
2
1
∗
100
(
1
)
=
√
∑
(
)
2
=
2
∗
100
(
2
)
=
|
2
−
|
|
1
+
|
∗
100
(
3
)
W
h
er
e:
: h
ar
m
o
n
ic
o
r
d
e
r
(
=
2
,
…
,
)
.
: h
ig
h
est h
ar
m
o
n
ic
o
r
d
er
c
o
n
s
id
er
ed
.
:
r
m
s
v
alu
e
o
f
th
e
v
o
ltag
e
at
h
ar
m
o
n
ic
o
r
d
er
(
V)
.
1
: r
m
s
v
alu
e
o
f
th
e
f
u
n
d
am
e
n
tal
v
o
ltag
e
(
V)
.
: r
m
s
v
alu
e
o
f
th
e
cu
r
r
en
t a
t h
a
r
m
o
n
ic
o
r
d
er
(
A)
.
: r
m
s
v
alu
e
o
f
th
e
m
ax
im
u
m
d
em
an
d
lo
a
d
cu
r
r
en
t a
t PC
C
(
A)
.
2
−
: n
eg
ativ
e
-
s
eq
u
e
n
ce
v
o
ltag
e
c
o
m
p
o
n
en
t (
V)
.
1
+
: p
o
s
itiv
e
-
s
eq
u
en
ce
v
o
ltag
e
co
m
p
o
n
e
n
t (
V)
.
I
n
ter
n
atio
n
al
s
tan
d
a
r
d
s
s
u
ch
a
s
I
E
E
E
5
1
9
-
2
0
2
2
an
d
I
E
C
6
1
0
0
0
-
3
-
1
3
estab
lis
h
m
ax
im
u
m
ad
m
is
s
ib
le
lim
its
f
o
r
th
ese
in
d
ices,
wh
ich
m
u
s
t
b
e
ev
alu
ate
d
at
t
h
e
p
o
in
t
o
f
c
o
m
m
o
n
c
o
u
p
lin
g
(
PC
C
)
[
1
8
]
.
Ho
wev
er
,
in
en
v
ir
o
n
m
en
ts
with
lar
g
e
-
s
ca
le
E
AF
o
p
er
atio
n
,
th
ese
lim
it
s
ar
e
f
r
eq
u
en
tly
e
x
ce
ed
e
d
,
h
ig
h
lig
h
tin
g
th
e
im
p
o
r
tan
ce
o
f
ef
f
ec
tiv
e
m
itig
a
tio
n
s
tr
ateg
ies.
3.
P
RO
P
O
SE
D
M
E
T
H
O
D
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
lo
g
y
in
teg
r
ates
a
h
y
p
e
r
b
o
lic
m
o
d
el
o
f
th
e
E
AF
with
a
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
alg
o
r
ith
m
to
d
et
er
m
in
e
th
e
o
p
tim
al
tu
n
in
g
o
f
th
e
p
ass
iv
e
p
o
wer
f
ilter
(
PP
F)
p
ar
am
eter
s
.
T
h
is
ap
p
r
o
ac
h
aim
s
to
m
in
im
ize
th
e
m
o
s
t
cr
itical
p
o
wer
q
u
ality
i
n
d
ices
at
th
e
PC
C
o
f
th
e
s
teel
p
l
an
t,
en
s
u
r
in
g
r
o
b
u
s
t
f
ilter
p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
d
if
f
e
r
en
t
f
u
r
n
ac
e
o
p
e
r
atin
g
co
n
d
itio
n
s
.
T
h
e
s
eq
u
e
n
tial
s
tep
s
o
f
th
e
m
eth
o
d
o
lo
g
y
a
r
e
s
u
m
m
ar
ized
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
Pro
p
o
s
ed
m
et
h
o
d
o
lo
g
y
f
r
a
m
ewo
r
k
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
Mu
lti
-
o
b
jective
o
p
timiz
a
tio
n
a
n
d
mu
lti
-
crit
eria
d
ec
is
io
n
a
n
a
l
ysis
o
f p
a
s
s
ive
…
(
A
lva
r
o
Ya
s
s
if Ma
r
ca
Yu
cra
)
1203
3
.
1
.
H
y
perbo
lic
E
AF
m
o
del sens
it
iv
it
y
a
na
ly
s
is
T
h
e
an
aly
s
is
o
f
th
e
f
u
r
n
ac
e
i
m
p
ac
t
o
n
p
o
we
r
q
u
ality
in
d
ic
es
is
ca
r
r
ied
o
u
t
u
s
in
g
a
m
o
d
e
l
ca
p
ab
le
o
f
r
ep
r
o
d
u
cin
g
th
e
n
o
n
lin
ea
r
an
d
ch
a
o
tic
b
e
h
av
io
r
ch
a
r
ac
ter
is
tic
o
f
t
h
e
E
AF.
Fo
r
t
h
is
r
ea
s
o
n
,
in
th
is
s
tu
d
y
th
e
h
y
p
er
b
o
lic
m
o
d
el
o
f
th
e
E
AF
[
1
9
]
,
[
2
0
]
was
em
p
l
o
y
e
d
,
wh
i
ch
d
escr
ib
es
th
e
ar
c
v
o
ltag
e
–
c
u
r
r
en
t
r
elatio
n
s
h
ip
th
r
o
u
g
h
(
4
)
-
(
6
)
.
(
)
=
+
∗
(
4
)
(
)
=
1
2
(
−
)
(
(
)
)
c
os
(
)
+
(
5
)
(
)
=
(
)
+
(
)
+
|
(
)
|
(
6
)
W
h
er
e:
: A
r
c
v
o
ltag
e
th
r
esh
o
ld
(
V)
.
: A
r
c
len
g
th
(
c
m
)
.
: O
s
cillatin
g
ar
c
p
o
wer
(
W
)
.
: E
AF a
r
c
v
o
ltag
e
(
V)
.
: V
o
ltag
e
d
r
o
p
ac
r
o
s
s
elec
tr
o
d
es (
V)
.
: V
o
ltag
e
p
er
u
n
it len
g
th
o
f
ar
c
(
V/cm
)
.
: A
r
c
ig
n
itio
n
p
o
wer
(
W
)
.
: A
r
c
ex
tin
ctio
n
p
o
wer
(
W
)
.
: E
AF a
r
c
cu
r
r
en
t
(
A)
.
: A
v
er
ag
e
ar
c
tr
a
n
s
itio
n
cu
r
r
en
t (
A)
.
I
n
ad
d
itio
n
,
in
o
r
d
er
to
b
ette
r
r
ep
r
esen
t
th
e
o
p
er
atio
n
al
v
ar
iab
ilit
y
o
f
th
e
E
AF,
th
e
m
eth
o
d
o
lo
g
y
in
co
r
p
o
r
ates
a
s
en
s
itiv
ity
an
al
y
s
is
o
f
th
e
p
ar
am
eter
s
o
f
th
e
h
y
p
er
b
o
lic
m
o
d
el
d
escr
ib
e
d
in
(
4
)
-
(
6
)
.
I
n
p
ar
ticu
lar
,
th
e
ch
ar
ac
ter
is
tic
co
ef
f
icien
ts
,
,
an
d
th
e
a
r
c
len
g
th
o
f
ea
ch
p
h
ase
ar
e
m
o
d
if
ied
.
T
h
is
allo
ws
th
e
em
u
latio
n
o
f
m
o
r
e
r
ea
lis
tic
o
p
er
atin
g
c
o
n
d
itio
n
s
,
s
u
ch
as
u
n
eq
u
al
ar
c
len
g
th
s
am
o
n
g
p
h
ases
o
r
d
y
n
a
m
ic
ch
an
g
es
in
ar
c
b
eh
av
io
r
d
u
r
in
g
th
e
m
eltin
g
p
r
o
ce
s
s
.
C
o
n
tin
u
in
g
with
th
e
s
en
s
itiv
it
y
an
aly
s
is
p
er
f
o
r
m
ed
f
o
r
ea
ch
o
f
th
e
f
o
u
r
m
ai
n
s
tag
es
o
f
th
e
m
eltin
g
p
r
o
ce
s
s
s
h
o
wn
in
Fig
u
r
e
1
,
d
if
f
er
en
t
o
p
e
r
atin
g
co
n
d
itio
n
s
ar
e
s
im
u
lated
in
a
ty
p
ical
s
teel
p
la
n
t
n
etwo
r
k
.
Vo
ltag
e
an
d
cu
r
r
en
t
m
ea
s
u
r
em
en
ts
ar
e
tak
en
at
th
e
P
CC
.
T
h
e
m
ea
s
u
r
e
m
en
ts
ar
e
p
r
o
ce
s
s
ed
to
ca
lcu
lat
e
th
e
p
o
we
r
q
u
ality
in
d
ices u
n
d
er
ev
alu
atio
n
:
T
HD,
T
DD
,
an
d
VUF
f
o
r
ea
c
h
ca
s
e.
3
.
2
.
I
dentif
ica
t
io
n a
nd
s
elec
t
io
n o
f
cr
it
ica
l o
pera
t
ing
ca
s
e
s
Fro
m
th
e
d
atab
ase
o
f
t
h
e
s
en
s
itiv
ity
an
aly
s
is
,
th
e
in
d
ices
o
f
e
ac
h
s
ce
n
ar
io
a
r
e
n
o
r
m
alize
d
w
ith
r
esp
ec
t
to
th
e
m
ax
im
u
m
lim
it
estab
lis
h
ed
b
y
th
e
s
tan
d
ar
d
,
as
ex
p
r
ess
ed
in
(
7
)
.
Su
b
s
eq
u
en
tly
,
th
e
n
o
r
m
alize
d
v
alu
es
ar
e
weig
h
ted
an
d
s
u
m
m
ed
to
co
m
p
u
te
th
e
s
im
p
le
co
m
p
o
s
ite
in
d
ex
(
SC
I
)
,
as d
ef
in
ed
in
(
8
)
.
̃
,
=
,
(
7
)
=
∑
=
1
̃
,
(
=
1
,
…
,
)
(
8
)
W
h
er
e:
̃
,
: N
o
r
m
alize
d
v
alu
e
o
f
in
d
icato
r
f
o
r
ca
s
e
.
: Sim
p
le
co
m
p
o
s
ite
in
d
ex
o
f
c
ase
.
: T
o
tal
n
u
m
b
e
r
o
f
s
im
u
lated
c
ases
/s
ce
n
ar
io
s
.
,
: V
alu
e
o
f
in
d
icato
r
f
o
r
ca
s
e
.
: Reg
u
lato
r
y
lim
it o
f
i
n
d
icato
r
ac
co
r
d
in
g
to
th
e
c
o
r
r
esp
o
n
d
in
g
s
tan
d
ar
d
.
: Weig
h
t a
s
s
ig
n
ed
to
in
d
ex
(
w
ith
∑
=
1
=
1
).
T
h
e
SC
I
q
u
an
tifie
s
th
e
p
r
o
x
i
m
ity
o
f
ea
ch
s
ce
n
ar
io
to
th
e
r
eg
u
lato
r
y
lim
its
,
g
en
e
r
atin
g
a
r
an
k
in
g
o
f
cr
iticality
th
at
en
ab
les
th
e
id
e
n
tific
atio
n
o
f
th
e
m
o
s
t
u
n
f
av
o
r
ab
le
co
n
f
i
g
u
r
atio
n
s
.
I
n
th
is
w
ay
,
th
e
m
o
s
t
cr
itical
ca
s
es
in
ter
m
s
o
f
p
o
wer
q
u
ality
d
eg
r
a
d
ati
o
n
a
r
e
s
elec
ted
.
T
h
ese
s
ce
n
ar
io
s
ar
e
th
e
n
u
s
ed
as
th
e
b
asis
f
o
r
th
e
f
o
r
m
u
latio
n
o
f
t
h
e
o
p
tim
izatio
n
p
r
o
b
lem
.
3
.
3
.
F
o
rm
ula
t
i
o
n o
f
t
he
m
ulti
-
o
bje
ct
iv
e
o
ptim
iza
t
io
n pro
blem
T
h
e
o
p
tim
al
tu
n
i
n
g
o
f
th
e
p
r
o
p
o
s
ed
PP
F
to
im
p
r
o
v
e
p
o
wer
q
u
ality
in
d
ices
is
f
o
r
m
u
lated
as
a
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
p
r
o
b
le
m
.
T
h
e
o
b
jectiv
e
is
to
m
i
n
im
ize
th
r
ee
f
u
n
ctio
n
s
(
9
)
,
n
a
m
ely
:
th
e
m
in
im
izatio
n
o
f
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
.
3
,
Sep
tem
b
er
20
2
6
:
1
2
0
0
-
1
2
1
1
1204
v
o
ltag
e
h
ar
m
o
n
ic
d
is
to
r
tio
n
(
1
0
)
,
th
e
r
ed
u
ctio
n
o
f
to
tal
d
em
a
n
d
d
is
to
r
tio
n
(
1
1
)
,
a
n
d
th
e
d
ec
r
ea
s
e
o
f
th
e
v
o
ltag
e
u
n
b
alan
ce
f
ac
to
r
(
1
2
)
at
th
e
P
C
C
.
T
h
ese
o
b
jectiv
e
f
u
n
ctio
n
s
p
r
esen
t
in
h
e
r
en
t
tr
a
d
e
-
o
f
f
s
with
ea
ch
o
t
h
er
.
T
o
en
s
u
r
e
r
o
b
u
s
tn
ess
,
th
e
o
p
tim
i
za
tio
n
is
p
er
f
o
r
m
e
d
ac
r
o
s
s
d
i
f
f
er
en
t
cr
itical
o
p
e
r
atin
g
ca
s
e
s
d
er
iv
ed
f
r
o
m
th
e
s
en
s
itiv
ity
an
aly
s
is
o
f
th
e
E
AF m
o
d
el.
(
x
)
=
[
1
(
x
)
2
(
x
)
3
(
x
)
]
;
x
=
[
ℎ
,
,
,
,
,
]
(
9
)
1
(
x
)
=
1
∑
(
)
(
x
)
=
1
=
1
∑
(
√
∑
(
(
)
)
2
=
2
1
(
)
∗
100
)
=
1
(
1
0
)
2
(
x
)
=
1
∑
(
)
(
x
)
=
1
=
1
∑
(
√
∑
(
(
)
)
2
=
2
(
)
∗
100
)
=
1
(
1
1
)
3
(
x
)
=
1
∑
(
)
(
x
)
=
1
=
1
∑
(
|
2
−
(
)
|
|
1
+
(
)
|
∗
100
)
=
1
(
1
2
)
W
h
er
e:
: in
d
ex
o
f
th
e
cr
itical
o
p
er
atin
g
ca
s
e
(
=
1
,
…
,
)
.
: n
u
m
b
er
o
f
c
r
itical
o
p
er
atin
g
ca
s
es.
ℎ
: tu
n
in
g
h
ar
m
o
n
ic
o
r
d
er
.
: r
ea
ctiv
e
p
o
wer
o
f
th
e
f
ilter
(
Mv
ar
)
.
: f
ilter
ca
p
ac
itan
ce
(
F).
: f
ilter
in
d
u
ctan
ce
(
H)
.
: q
u
ality
f
ac
to
r
.
: f
ilter
r
esis
tan
ce
(
Ω
)
.
T
h
e
im
p
lem
e
n
tatio
n
i
n
co
r
p
o
r
a
tes
a
s
et
o
f
o
p
er
atio
n
al
co
n
s
tr
ain
ts
,
ex
p
r
ess
ed
in
(
1
3
)
-
(
1
8
)
,
wh
ich
d
ef
in
e
b
o
th
th
e
co
m
p
o
n
en
t
ca
p
ac
ity
li
m
its
an
d
th
e
ad
m
is
s
ib
le
r
an
g
es
o
f
th
e
f
ilter
p
ar
a
m
eter
s
.
T
h
ese
co
n
s
tr
ain
ts
en
s
u
r
e
th
at
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
y
ield
s
tech
n
ically
f
ea
s
ib
le
an
d
p
r
ac
tically
im
p
lem
en
tab
le
PP
F so
lu
tio
n
s
.
ℎ
≤
ℎ
≤
ℎ
(
1
3
)
≤
≤
(
1
4
)
≤
≤
(
1
5
)
≤
≤
(
1
6
)
≤
≤
(
1
7
)
≤
≤
(
1
8
)
T
h
e
o
p
tim
izatio
n
p
r
o
b
lem
is
m
u
lti
-
o
b
jectiv
e,
n
o
n
lin
ea
r
,
an
d
in
v
o
lv
es
co
n
f
lictin
g
g
o
als,
s
in
ce
n
o
t
all
p
o
wer
q
u
ality
in
d
ices
ca
n
b
e
m
in
im
ized
s
im
u
ltan
eo
u
s
ly
.
T
o
ad
d
r
ess
th
is
,
th
e
non
-
d
o
m
i
n
ated
s
o
r
tin
g
g
en
etic
alg
o
r
ith
m
I
I
(
NSGA
-
I
I
)
is
em
p
lo
y
ed
.
NSGA
-
I
I
was selec
ted
o
v
er
o
th
e
r
ev
o
lu
tio
n
ar
y
alg
o
r
i
th
m
s
b
ec
au
s
e
o
f
its
ab
ilit
y
to
ef
f
icien
tly
h
a
n
d
le
n
o
n
lin
ea
r
an
d
n
o
n
-
co
n
v
ex
s
ea
r
ch
s
p
ac
es,
to
m
ain
tain
a
well
-
d
is
tr
ib
u
ted
Par
eto
f
r
o
n
t,
an
d
to
p
r
e
v
en
t
p
r
em
atu
r
e
co
n
v
er
g
en
ce
th
r
o
u
g
h
its
cr
o
wd
i
n
g
d
is
tan
ce
m
ec
h
an
is
m
[
2
1
]
.
I
n
ad
d
itio
n
,
its
p
r
o
v
en
p
er
f
o
r
m
an
ce
i
n
p
o
wer
s
y
s
tem
ap
p
licatio
n
s
r
ep
o
r
ted
i
n
th
e
liter
atu
r
e
m
a
k
es
it
p
ar
ticu
lar
l
y
s
u
i
tab
le
f
o
r
ad
d
r
ess
in
g
th
e
co
n
f
lictin
g
o
b
jectiv
es
o
f
h
ar
m
o
n
ic
d
is
to
r
tio
n
an
d
v
o
ltag
e
u
n
b
alan
ce
in
E
AF
-
b
ased
n
etwo
r
k
s
.
NSGA
-
I
I
id
en
tifie
s
a
s
et
o
f
Par
eto
-
o
p
tim
al
s
o
lu
tio
n
s
b
y
c
o
m
b
in
in
g
th
r
ee
k
ey
m
ec
h
an
is
m
s
:
a)
Selectio
n
,
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
:
Fro
m
a
c
u
r
r
en
t
p
o
p
u
l
atio
n
(
ca
n
d
id
ate
s
o
lu
tio
n
s
)
,
an
o
f
f
s
p
r
in
g
p
o
p
u
latio
n
is
g
en
er
ated
.
Selectio
n
f
av
o
r
s
f
itter
in
d
iv
id
u
als,
cr
o
s
s
o
v
er
co
m
b
in
es
p
ar
en
ts
to
ex
p
lo
r
e
n
ew
r
eg
io
n
s
,
an
d
m
u
tatio
n
in
tr
o
d
u
c
es v
ar
iatio
n
s
to
p
r
eser
v
e
d
iv
er
s
ity
an
d
av
o
id
p
r
em
at
u
r
e
co
n
v
er
g
en
ce
.
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
Mu
lti
-
o
b
jective
o
p
timiz
a
tio
n
a
n
d
mu
lti
-
crit
eria
d
ec
is
io
n
a
n
a
l
ysis
o
f p
a
s
s
ive
…
(
A
lva
r
o
Ya
s
s
if Ma
r
ca
Yu
cra
)
1205
b)
No
n
-
d
o
m
in
ated
s
o
r
tin
g
:
T
h
e
c
o
m
b
in
ed
p
o
p
u
latio
n
=
∪
is
class
if
ied
in
to
s
u
cc
ess
iv
e
Par
eto
f
r
o
n
ts
(
1
,
2
,
.
.
.
)
ac
co
r
d
in
g
to
d
o
m
in
an
ce
.
T
h
e
f
ir
s
t
f
r
o
n
t
1
co
n
tain
s
all
n
o
n
-
d
o
m
i
n
ated
s
o
lu
tio
n
s
,
wh
ile
s
u
b
s
eq
u
en
t f
r
o
n
ts
in
clu
d
e
s
o
lu
tio
n
s
d
o
m
in
ated
b
y
th
o
s
e
in
th
e
p
r
ev
io
u
s
o
n
es,
estab
lis
h
in
g
a
h
ier
ar
ch
y
.
c)
C
r
o
wd
in
g
d
is
tan
ce
:
T
o
b
u
ild
th
e
n
e
x
t
g
e
n
er
atio
n
+
1
,
en
tire
f
r
o
n
ts
ar
e
ad
d
ed
s
eq
u
en
tially
u
n
til
th
e
p
o
p
u
latio
n
s
ize
is
m
et.
I
f
th
e
i
n
clu
s
io
n
o
f
a
f
r
o
n
t
e
x
ce
ed
s
th
e
lim
it,
NSGA
-
I
I
ap
p
lies
cr
o
w
d
in
g
d
is
tan
ce
to
s
elec
t th
e
m
o
s
t d
iv
er
s
e
in
d
iv
id
u
als,
en
s
u
r
in
g
a
well
-
s
p
r
ea
d
d
i
s
tr
ib
u
tio
n
ac
r
o
s
s
th
e
o
b
jectiv
e
s
p
ac
e
.
T
h
e
o
v
er
all
s
elec
tio
n
p
r
o
ce
s
s
is
s
u
m
m
ar
ized
in
Fig
u
r
e
3
,
wh
ich
illu
s
tr
ates
h
o
w
p
o
p
u
latio
n
s
ar
e
co
m
b
in
ed
,
s
o
r
ted
in
to
Par
eto
f
r
o
n
ts
,
a
n
d
f
ilter
ed
to
co
n
s
tr
u
ct
+
1
.
Fig
u
r
e
3
.
NSGA
-
I
I
p
o
p
u
latio
n
u
p
d
ate
p
r
o
ce
s
s
[
2
1
]
3
.
4
.
M
ulti
-
cr
it
er
ia
decisi
o
n a
na
ly
s
is
a
nd
f
ilte
r
ro
bu
s
t
ne
s
s
a
s
s
es
s
m
ent
On
ce
th
e
s
et
o
f
o
p
tim
al
s
o
lu
tio
n
s
(
n
o
n
-
d
o
m
i
n
ated
f
r
o
n
t)
h
as
b
ee
n
o
b
tain
ed
,
a
m
u
lti
-
cr
iter
i
a
d
ec
is
io
n
an
aly
s
is
(
MCDA)
tech
n
iq
u
e
i
s
ap
p
lied
to
s
elec
t
th
e
m
o
s
t
b
alan
ce
d
an
d
r
o
b
u
s
t
s
o
lu
tio
n
.
T
h
e
MCDA
m
eth
o
d
u
s
ed
in
th
is
s
tu
d
y
is
tech
n
iq
u
e
f
o
r
o
r
d
er
p
r
e
f
er
en
ce
b
y
s
im
ilar
ity
to
id
e
al
s
o
lu
tio
n
(
T
OP
SIS
)
,
wh
ich
r
an
k
s
alter
n
ativ
es
ac
co
r
d
in
g
to
th
eir
r
elativ
e
d
is
tan
ce
f
r
o
m
an
id
ea
l
s
o
lu
tio
n
.
T
h
is
m
eth
o
d
b
e
g
in
s
b
y
n
o
r
m
alizin
g
an
d
weig
h
tin
g
th
e
i
n
d
ices
(
T
HD,
T
DD,
VUF)
o
f
ea
ch
ca
n
d
id
at
e
s
o
lu
tio
n
.
T
h
en
,
th
e
p
o
s
itiv
e
id
ea
l
an
d
n
eg
ativ
e
id
ea
l
s
o
lu
tio
n
s
ar
e
d
eter
m
in
e
d
u
s
in
g
(
1
9
)
,
(
2
0
)
,
an
d
th
e
E
u
clid
ea
n
d
is
tan
ce
s
o
f
ea
ch
alt
er
n
ativ
e
to
t
h
e
id
ea
l
s
o
lu
tio
n
s
ar
e
ca
lcu
lated
u
s
in
g
(
2
1
)
,
(
2
2
)
.
Af
ter
war
d
,
th
e
r
elativ
e
clo
s
en
ess
co
ef
f
icien
t
(
C
C
i
)
f
o
r
ea
ch
alter
n
ativ
e
is
o
b
tain
ed
u
s
in
g
(
2
3
)
.
T
h
is
in
d
ex
in
d
icate
s
th
e
p
r
o
x
im
ity
to
th
e
p
o
s
itiv
e
id
ea
l
s
o
lu
ti
o
n
a
n
d
th
e
d
is
tan
ce
f
r
o
m
th
e
n
e
g
a
tiv
e
id
ea
l
s
o
lu
tio
n
.
T
h
u
s
,
th
e
alter
n
ativ
e
with
t
h
e
h
ig
h
est
is
co
n
s
id
er
ed
t
h
e
b
est
co
m
p
r
o
m
is
e
s
o
lu
tio
n
o
n
th
e
P
ar
eto
f
r
o
n
t.
+
=
{
min
(
)
}
(
1
9
)
−
=
{
ma
x
(
)
}
(
2
0
)
+
=
√
∑
(
−
+
)
2
=
1
(
2
1
)
−
=
√
∑
(
−
−
)
2
=
1
(
2
2
)
=
−
+
+
−
(
2
3
)
W
h
er
e:
: n
o
r
m
alize
d
a
n
d
weig
h
te
d
v
al
u
e
o
f
c
r
iter
io
n
f
o
r
alter
n
ativ
e
.
+
: p
o
s
itiv
e
id
ea
l so
lu
tio
n
,
c
o
m
p
o
s
ed
o
f
th
e
m
in
im
u
m
v
alu
es o
f
ea
ch
cr
iter
io
n
.
−
: n
eg
ativ
e
id
ea
l so
lu
tio
n
,
co
m
p
o
s
ed
o
f
t
h
e
m
ax
im
u
m
v
al
u
es o
f
ea
ch
c
r
iter
io
n
.
+
:
E
u
clid
ea
n
d
is
tan
ce
o
f
alter
n
a
tiv
e
to
th
e
p
o
s
itiv
e
id
ea
l so
lu
tio
n
.
−
:
E
u
clid
ea
n
d
is
tan
ce
o
f
alter
n
a
tiv
e
to
th
e
n
e
g
ativ
e
id
ea
l so
lu
t
io
n
.
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
.
3
,
Sep
tem
b
er
20
2
6
:
1
2
0
0
-
1
2
1
1
1206
On
ce
th
e
o
p
tim
al
co
n
f
ig
u
r
atio
n
o
f
th
e
PP
F
h
as
b
ee
n
s
elec
ted
,
its
r
o
b
u
s
tn
ess
is
ev
alu
ated
u
n
d
er
d
if
f
er
en
t
o
p
e
r
atin
g
c
o
n
d
itio
n
s
.
T
h
e
PP
F
is
co
n
f
ig
u
r
ed
ac
co
r
d
i
n
g
to
th
e
d
ec
is
io
n
v
ar
iab
les
o
f
th
e
s
o
lu
tio
n
ch
o
s
en
b
y
th
e
MCDA
m
eth
o
d
,
an
d
its
ef
f
ec
tiv
en
ess
is
v
er
if
ied
in
th
e
s
ce
n
ar
io
s
d
ef
in
ed
f
o
r
th
e
f
o
u
r
o
p
er
atin
g
s
tag
es
o
f
th
e
f
u
r
n
ac
e.
New
r
esu
lts
o
f
p
o
wer
q
u
ality
in
d
ices
ar
e
th
e
n
d
eter
m
in
ed
a
n
d
c
o
m
p
ar
e
d
with
th
e
b
ase
ca
s
e,
allo
win
g
th
e
ass
ess
m
en
t
o
f
th
e
g
lo
b
al
ef
f
ec
tiv
en
ess
o
f
th
e
d
esig
n
b
y
e
v
alu
atin
g
v
ar
iatio
n
s
in
th
e
q
u
ality
in
d
ices
o
b
tain
ed
in
th
e
d
atab
ase.
I
n
th
is
way
,
it
is
co
n
f
ir
m
ed
wh
eth
er
th
e
s
elec
ted
d
esig
n
is
r
o
b
u
s
t
ag
ain
s
t
th
e
o
p
er
atio
n
al
v
ar
iab
ilit
y
o
f
th
e
E
AF.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
ap
p
licatio
n
o
f
t
h
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
f
o
llo
ws
th
e
p
r
o
ce
d
u
r
e
s
u
m
m
ar
ized
in
Fig
u
r
e
2
,
wh
ich
in
teg
r
ates
th
e
s
en
s
itiv
ity
an
al
y
s
is
o
f
th
e
E
AF,
t
h
e
f
o
r
m
u
la
tio
n
an
d
r
eso
lu
tio
n
o
f
th
e
o
p
tim
izatio
n
p
r
o
b
lem
th
r
o
u
g
h
NSGA
-
I
I
,
an
d
th
e
a
p
p
licatio
n
o
f
MCDA
f
o
r
th
e
s
ele
ctio
n
o
f
th
e
m
o
s
t r
o
b
u
s
t f
ilter
co
n
f
ig
u
r
atio
n
.
4
.
1
.
Ca
s
e
s
t
ud
y
des
cr
iptio
n
T
o
v
alid
ate
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
,
a
ca
s
e
s
tu
d
y
was
co
n
s
id
er
ed
b
ased
o
n
a
s
teel
p
lan
t
co
n
n
ec
ted
to
th
e
Natio
n
al
I
n
ter
co
n
n
ec
ted
Sy
s
tem
o
f
B
o
liv
ia,
f
r
o
m
wh
ich
th
e
d
ata
wer
e
o
b
tain
ed
.
T
h
e
s
in
g
le
-
lin
e
d
iag
r
am
o
f
th
e
p
la
n
t
u
n
d
er
an
aly
s
is
is
s
h
o
wn
in
Fig
u
r
e
4
,
wh
er
e
th
e
PC
C
with
th
e
ex
te
r
n
al
s
u
p
p
ly
n
etwo
r
k
ca
n
b
e
o
b
s
er
v
ed
.
T
h
e
in
ter
n
al
n
etwo
r
k
co
n
s
is
ts
o
f
a
m
ain
tr
a
n
s
f
o
r
m
er
T
1
r
ated
at
2
3
0
/
1
1
k
V
a
n
d
a
tr
an
s
f
o
r
m
er
T
2
r
ated
at
1
1
/0
.
6
3
5
k
V
s
u
p
p
ly
in
g
th
e
E
AF.
T
h
e
elec
tr
ical
p
ar
am
eter
s
o
f
th
e
f
ee
d
er
lin
es
o
f
t
h
e
c
i
r
cu
it
ar
e
p
r
esen
ted
in
T
ab
le
1
.
T
h
e
m
ain
lo
ad
o
f
th
e
p
lan
t
co
r
r
esp
o
n
d
s
to
a
1
9
MW
E
AF,
wh
o
s
e
n
o
n
lin
ea
r
c
h
ar
ac
t
er
is
tic
wa
s
r
ep
r
esen
ted
th
r
o
u
g
h
th
e
h
y
p
er
b
o
lic
m
o
d
el
d
escr
ib
e
d
in
s
ec
tio
n
3
.
1
.
T
o
r
e
p
r
o
d
u
ce
th
e
v
ar
ia
b
ilit
y
o
f
th
e
f
u
r
n
ac
e
d
u
r
in
g
th
e
m
eltin
g
p
r
o
ce
s
s
,
wh
ich
co
m
p
r
is
es
th
e
s
tag
es
o
f
b
o
r
in
g
,
m
eltin
g
,
p
latin
g
,
an
d
r
e
f
in
in
g
,
a
s
en
s
itiv
ity
an
aly
s
is
o
f
th
e
m
o
d
el
p
ar
am
et
er
s
d
o
cu
m
en
ted
in
th
e
liter
atu
r
e
[
2
2
]
-
[
2
4
]
was
ca
r
r
ied
o
u
t.
T
h
e
r
an
g
es
co
n
s
id
er
ed
in
ea
ch
s
tag
e
f
o
r
th
is
an
aly
s
is
ar
e
p
r
esen
ted
in
T
ab
le
2
.
Fig
u
r
e
4
.
E
lectr
ical
n
etwo
r
k
o
f
th
e
ca
s
e
s
tu
d
y
T
ab
le
1
.
E
lectr
ical
p
a
r
am
eter
s
o
f
th
e
f
ee
d
er
co
n
d
u
ct
o
r
s
in
ca
s
e
-
s
tu
d
y
s
teel
p
lan
t
n
etwo
r
k
[
1
9
]
F
e
e
d
e
r
(
k
V
)
(
k
A
)
Le
n
g
t
h
(
k
m)
R
’
(
2
0
°
C
)
(
Ω
/
k
m)
X
’
(
Ω
/
k
m)
ZL1
2
3
0
0
.
3
7
6
5
1
0
.
7
0
.
0
8
4
0
0
.
4
4
7
0
ZL2
15
0
.
5
2
4
0
0
.
0
2
5
0
.
0
7
8
7
0
.
1
5
1
1
ZC
1
15
0
.
4
7
0
.
0
3
0
.
0
9
7
8
0
.
1
5
3
8
ZC
2
15
0
.
4
7
0
.
2
6
0
.
0
9
7
8
0
.
1
5
3
8
T
ab
le
2
.
R
an
g
es o
f
v
ar
iatio
n
f
o
r
p
ar
a
m
eter
s
o
f
th
e
h
y
p
e
r
b
o
li
c
E
AF m
o
d
el
P
r
o
c
e
ss
(
c
m)
(
c
m)
(
c
m)
B
o
r
i
n
g
[
2
5
-
30
-
35]
[
2
5
-
30
-
35]
[
2
5
-
30
-
35]
[
1
5
0
0
0
0
-
2
5
0
0
0
0
]
[
3
0
0
0
0
-
5
0
0
0
0
]
[
3
0
0
0
-
6
5
0
0
]
M
e
l
t
i
n
g
[
1
9
-
23
-
27]
[
1
9
-
23
-
27]
[
1
9
-
23
-
27]
[
1
2
0
0
0
0
-
2
0
0
0
0
0
]
[
2
4
0
0
0
-
4
0
0
0
0
]
[
3
5
0
0
-
6
0
0
0
]
P
l
a
t
i
n
g
[
1
0
-
14
-
18]
[
1
0
-
14
-
18]
[
1
0
-
14
-
18]
[
9
0
0
0
0
-
1
5
0
0
0
0
]
[
1
8
0
0
0
-
3
0
0
0
0
]
[
4
0
0
0
-
5
5
0
0
]
R
e
f
i
n
i
n
g
[3
-
6
-
9]
[3
-
6
-
9]
[3
-
6
-
9]
[
6
0
0
0
0
-
1
0
0
0
0
0
]
[
1
2
0
0
0
-
2
0
0
0
0
]
[
4
5
0
0
-
5
0
0
0
]
Fo
r
ea
ch
s
tag
e,
a
s
en
s
itiv
ity
a
n
aly
s
is
o
f
th
e
h
y
p
e
r
b
o
lic
m
o
d
el
p
ar
am
eter
s
was
p
er
f
o
r
m
e
d
,
g
en
er
atin
g
a
to
tal
o
f
7
2
s
im
u
lated
s
ce
n
ar
i
o
s
p
er
s
tag
e.
Vo
lta
g
es
an
d
c
u
r
r
en
ts
wer
e
m
ea
s
u
r
ed
at
th
e
PC
C
in
ea
ch
s
im
u
latio
n
to
ca
lcu
late
th
e
p
o
wer
q
u
ality
in
d
ices
u
n
d
er
s
tu
d
y
(
T
HD,
T
DD,
an
d
VUF)
.
T
h
e
r
esu
lts
in
d
icate
th
at
th
e
m
o
s
t
cr
itical
s
tag
es
ar
e
b
o
r
in
g
an
d
m
eltin
g
,
d
u
e
to
t
h
eir
h
i
g
h
er
d
is
to
r
tio
n
an
d
u
n
b
alan
ce
lev
els.
Fig
u
r
e
5
p
r
esen
ts
th
e
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
Mu
lti
-
o
b
jective
o
p
timiz
a
tio
n
a
n
d
mu
lti
-
crit
eria
d
ec
is
io
n
a
n
a
l
ysis
o
f p
a
s
s
ive
…
(
A
lva
r
o
Ya
s
s
if Ma
r
ca
Yu
cra
)
1207
p
o
wer
q
u
ality
in
d
ices
o
b
tain
e
d
in
th
e
b
ase
ca
s
e
f
o
r
th
e
b
o
r
in
g
s
tag
e,
h
ig
h
lig
h
tin
g
th
at
s
ev
er
al
o
f
th
em
ex
ce
ed
th
e
lim
its
r
ec
o
m
m
en
d
e
d
b
y
in
t
er
n
atio
n
al
s
tan
d
ar
d
s
.
Fin
ally
,
in
o
r
d
er
to
s
elec
t
th
e
c
r
itical
ca
s
es
th
at
will
b
e
u
s
ed
in
th
e
o
p
tim
izatio
n
s
tag
e
(
s
ee
s
ec
tio
n
3
.
3
)
,
th
e
p
o
wer
q
u
ality
in
d
ices
o
b
t
ain
ed
f
o
r
all
s
im
u
lated
s
ce
n
a
r
io
s
wer
e
f
ir
s
t
n
o
r
m
alize
d
wi
th
r
esp
ec
t
to
th
eir
r
eg
u
lato
r
y
lim
its
,
as d
e
f
in
ed
in
(
7
)
.
T
h
ese
n
o
r
m
alize
d
i
n
d
ices w
er
e
th
e
n
weig
h
ted
a
n
d
ag
g
r
e
g
ated
ac
co
r
d
in
g
to
(
8
)
to
ca
lcu
late
th
e
SC
I
f
o
r
ea
ch
ca
s
e.
T
h
is
p
r
o
ce
d
u
r
e
m
ak
e
s
it
p
o
s
s
ib
le
to
id
en
tify
an
d
r
a
n
k
th
e
m
o
s
t
cr
itical
f
u
r
n
ac
e
o
p
er
atin
g
co
n
d
itio
n
s
,
wh
ich
ar
e
later
u
s
ed
as th
e
in
p
u
t set f
o
r
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
.
(
a)
(
b
)
(
c)
Fig
u
r
e
5
.
Po
wer
q
u
ality
in
d
ice
s
o
b
tain
ed
f
o
r
th
e
b
ase
ca
s
e
in
th
e
b
o
r
in
g
s
tag
e:
(
a)
t
o
tal
h
ar
m
o
n
ic
d
is
to
r
tio
n
,
(
b
)
to
tal
d
e
m
an
d
d
is
to
r
tio
n
,
a
n
d
(
c)
v
o
ltag
e
u
n
b
alan
ce
f
ac
to
r
4
.
2
.
M
ulti
-
o
bje
ct
iv
e
t
un
ing
o
f
t
he
po
wer
f
ilte
r
B
ased
o
n
th
e
d
ata
b
ase
co
n
s
tr
u
cted
in
s
ec
tio
n
4
.
1
,
t
h
e
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
p
r
o
b
lem
f
o
r
m
u
lated
was
s
o
lv
ed
u
s
in
g
NSGA
-
I
I
,
im
p
lem
en
ted
in
MA
T
L
AB
9
.
1
3
(
R
2
0
2
2
b
)
.
T
h
e
o
p
tim
iz
atio
n
p
r
o
b
lem
was
f
o
r
m
u
lated
with
t
h
e
th
r
ee
o
b
je
ctiv
e
f
u
n
ctio
n
s
in
(
1
0
)
–
(
1
2
)
,
wh
ile
th
e
d
ec
is
io
n
v
ar
iab
le
li
m
its
ar
e
s
u
m
m
ar
ize
d
in
T
ab
le
3
.
T
h
e
NSGA
-
I
I
alg
o
r
ith
m
was
co
n
f
ig
u
r
ed
with
a
p
o
p
u
latio
n
o
f
5
0
in
d
iv
id
u
als,
a
cr
o
s
s
o
v
er
p
r
o
b
ab
ilit
y
o
f
0
.
8
,
a
m
u
tatio
n
p
r
o
b
a
b
ilit
y
o
f
0
.
3
3
3
,
an
d
1
0
0
g
en
e
r
atio
n
s
.
T
h
e
o
p
tim
izatio
n
p
r
o
d
u
ce
d
a
Par
eto
f
r
o
n
t
o
f
n
o
n
-
d
o
m
in
ated
s
o
lu
tio
n
s
,
illu
s
tr
atin
g
th
e
tr
ad
e
-
o
f
f
s
am
o
n
g
th
e
o
b
jectiv
e
f
u
n
ctio
n
s
,
as
s
h
o
wn
in
Fig
u
r
e
6
(
a
)
.
T
o
s
elec
t th
e
m
o
s
t b
alan
ce
d
alter
n
ativ
e,
th
e
MCDA
ap
p
r
o
ac
h
wa
s
ap
p
lied
u
s
in
g
(
1
9
)
–
(
2
3
)
to
c
o
m
p
u
te
t
h
e
C
C
i
f
o
r
ea
ch
s
o
lu
tio
n
,
wh
o
s
e
v
al
u
es
ar
e
p
r
esen
ted
in
Fig
u
r
e
6
(
b
)
.
T
h
e
alter
n
ativ
e
with
th
e
h
ig
h
est
C
C
i
v
alu
e
(
0
.
3
0
9
2
)
was
id
en
tifie
d
as
th
e
b
est
co
m
p
r
o
m
is
e
s
o
lu
tio
n
ac
r
o
s
s
th
e
m
u
lt
ip
le
s
ce
n
ar
io
s
o
f
th
e
ca
s
e
s
tu
d
y
.
I
ts
co
r
r
esp
o
n
d
in
g
f
ilter
p
ar
am
eter
s
a
r
e
s
u
m
m
a
r
ized
in
T
a
b
le
4
,
to
g
eth
er
with
th
o
s
e
o
b
tain
ed
u
s
in
g
t
h
e
co
n
v
en
ti
o
n
al
tu
n
i
n
g
m
eth
o
d
d
escr
ib
ed
in
[
2
5
]
,
[
2
6
]
f
o
r
co
m
p
ar
is
o
n
.
T
ab
le
3
.
Dec
is
io
n
v
a
r
iab
le
lim
its
V
a
r
i
a
b
l
e
Lo
w
e
r
b
o
u
n
d
U
p
p
e
r
b
o
u
n
d
ℎ
3
11
10
1
0
0
(
p
F
)
5
.
4
7
1
0
0
(H)
3
.
0
6
5
6
.
1
3
(
Ω
)
1
0
5
.
8
5
2
9
0
(
M
v
a
r
)
1
5
T
ab
le
4
.
Fil
ter
p
ar
am
eter
s
o
f
t
h
e
co
n
v
en
tio
n
al
a
n
d
p
r
o
p
o
s
ed
m
eth
o
d
V
a
r
i
a
b
l
e
C
o
n
v
e
n
t
i
o
n
a
l
m
e
t
h
o
d
P
r
o
p
o
se
d
m
e
t
h
o
d
ℎ
5
4
.
9
1
8
4
16
8
6
.
2
8
3
9
(
p
F
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3
6
.
7
0
5
4
.
5
6
1
(H)
1
1
.
0
4
7
.
6
7
6
6
(
Ω
)
3
4
6
.
8
8
1
3
7
.
4
7
2
0
(
M
v
a
r
)
3
.
0
5
3
.
4
5
9
8
4
.
3
.
Va
lid
a
t
i
o
n a
nd
co
m
pa
ri
s
o
n o
f
re
s
ults
On
ce
th
e
o
p
tim
al
f
ilter
co
n
f
i
g
u
r
atio
n
was
id
en
tifie
d
,
its
r
o
b
u
s
tn
ess
was
a
s
s
e
s
s
ed
b
y
ex
ten
d
in
g
th
e
s
im
u
latio
n
s
to
all
o
p
er
atin
g
s
ce
n
ar
io
s
d
ef
in
e
d
in
s
ec
tio
n
4
.
1
.
A
co
m
p
ar
is
o
n
o
f
th
e
p
o
wer
q
u
ality
in
d
ices
at
th
e
PC
C
,
o
b
tain
ed
u
s
in
g
th
e
p
r
o
p
o
s
ed
an
d
c
o
n
v
e
n
tio
n
al
m
et
h
o
d
s
d
u
r
in
g
th
e
b
o
r
in
g
s
tag
e,
is
s
h
o
wn
in
Fig
u
r
e
7
.
T
h
e
r
esu
lts
s
h
o
w
th
at
th
e
p
r
o
p
o
s
ed
o
p
tim
izatio
n
ac
h
iev
es
lo
wer
h
ar
m
o
n
ic
d
is
to
r
tio
n
in
d
i
ce
s
(
T
HD,
T
DD)
in
all
ev
alu
ated
ca
s
es,
wh
er
ea
s
th
e
co
n
v
en
tio
n
al
d
esig
n
co
n
s
is
ten
tly
y
ield
s
h
ig
h
er
v
al
u
es.
T
h
e
o
v
er
all
p
e
r
f
o
r
m
an
ce
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
.
3
,
Sep
tem
b
er
20
2
6
:
1
2
0
0
-
1
2
1
1
1208
o
f
b
o
t
h
co
n
f
ig
u
r
atio
n
s
with
r
e
s
p
ec
t
to
th
e
b
ase
ca
s
e
(
with
o
u
t
co
m
p
en
s
atio
n
)
is
s
u
m
m
ar
ize
d
in
T
ab
le
5
,
wh
ic
h
r
ep
o
r
ts
th
e
av
e
r
ag
e
v
alu
es o
f
t
h
e
p
o
wer
q
u
ality
in
d
ices a
cr
o
s
s
th
e
en
tire
f
u
r
n
ac
e
p
r
o
ce
s
s
.
T
ab
le
5
s
h
o
ws
th
at
th
e
p
r
o
p
o
s
ed
o
p
tim
izatio
n
-
b
ased
f
ilter
d
e
s
ig
n
ac
h
iev
es
a
4
7
.
6
%
r
ed
u
cti
o
n
in
T
HD
an
d
a
3
3
.
5
%
r
ed
u
ctio
n
in
T
DD
with
r
esp
ec
t
to
th
e
b
ase
ca
s
e,
wh
ile
also
p
r
o
v
i
d
in
g
a
d
d
itio
n
a
l
d
ec
r
ea
s
es
o
f
4
.
8
%
in
T
HD
an
d
2
.
1
%
in
T
DD
r
elativ
e
to
th
e
co
n
v
en
ti
o
n
al
m
e
th
o
d
.
I
n
co
n
tr
ast,
alth
o
u
g
h
th
e
o
p
tim
ized
tu
n
in
g
s
lig
h
tly
in
cr
ea
s
es
VUF
co
m
p
ar
ed
with
th
e
co
n
v
en
tio
n
al
f
ilter
,
it
s
till
d
eliv
er
s
a
6
3
.
6
%
r
ed
u
ctio
n
in
VUF
with
r
esp
ec
t
to
th
e
b
ase
ca
s
e
an
d
r
em
ain
s
well
b
elo
w
th
e
m
ax
i
m
u
m
p
er
m
is
s
ib
le
v
alu
e
im
p
o
s
ed
b
y
t
h
e
s
tan
d
ar
d
s
.
Ov
er
all,
th
ese
r
esu
lts
in
d
icate
t
h
at
th
e
p
r
o
p
o
s
ed
m
eth
o
d
y
ield
s
s
u
p
er
io
r
h
ar
m
o
n
ic
p
e
r
f
o
r
m
an
ce
,
with
th
e
lar
g
est
r
elativ
e
im
p
r
o
v
em
en
ts
o
b
s
er
v
ed
in
T
HD
an
d
T
DD.
(
a)
(
b
)
Fig
u
r
e
6
.
Op
tim
al
s
o
lu
tio
n
s
elec
tio
n
v
ia
in
teg
r
ated
r
an
k
in
g
:
(
a)
Par
eto
f
r
o
n
t o
f
th
e
ca
s
e
s
tu
d
y
an
d
(
b
)
C
C
i
r
an
k
in
g
o
f
th
e
n
o
n
-
d
o
m
in
ated
s
o
lu
tio
n
s
(
a)
(
b
)
(
c)
Fig
u
r
e
7
.
C
o
m
p
a
r
is
o
n
o
f
p
o
we
r
q
u
ality
in
d
ices b
etwe
en
th
e
p
r
o
p
o
s
ed
a
n
d
co
n
v
en
ti
o
n
al
m
et
h
o
d
s
:
(
a)
to
tal
h
ar
m
o
n
ic
d
is
to
r
tio
n
,
(
b
)
to
tal
d
em
a
n
d
d
is
to
r
tio
n
,
an
d
(
c)
v
o
ltag
e
u
n
b
alan
ce
f
ac
to
r
T
ab
le
5
.
C
o
m
p
a
r
is
o
n
o
f
av
er
a
g
e
p
o
wer
q
u
ality
in
d
ices a
t
th
e
PC
C
P
o
w
e
r
q
u
a
l
i
t
y
i
n
d
e
x
B
a
se
c
a
se
C
o
n
v
e
n
t
i
o
n
a
l
m
e
t
h
o
d
P
r
o
p
o
se
d
m
e
t
h
o
d
TH
D
(
%)
1
.
3
1
4
7
0
.
7
5
2
1
(
-
4
2
.
8
%)
0
.
6
8
9
5
(
-
4
7
.
6
%)
TD
D
(
%)
6
.
4
9
3
7
4
.
4
5
4
7
(
-
3
1
.
4
%)
4
.
3
1
7
1
(
-
3
3
.
5
%)
VUF
1
.
0
6
3
7
0
.
3
2
7
2
(
-
6
9
.
2
%)
0
.
3
8
6
9
(
-
6
3
.
6
%)
4
.
4
.
Dis
cus
s
io
n
T
h
e
r
esu
lts
in
T
ab
le
5
in
d
icate
th
at
th
e
o
p
tim
ized
f
ilter
clea
r
ly
o
u
tp
e
r
f
o
r
m
s
b
o
th
th
e
b
ase
ca
s
e
an
d
th
e
co
n
v
en
tio
n
al
PP
F.
T
h
e
NSGA
-
II
–
b
ased
tu
n
i
n
g
ac
h
iev
es
m
a
r
k
ed
ly
l
o
wer
T
HD
an
d
T
DD
o
v
er
th
e
wh
o
le
f
u
r
n
ac
e
p
r
o
ce
s
s
,
co
n
f
ir
m
in
g
its
ef
f
ec
tiv
en
ess
in
m
itig
ati
n
g
cu
r
r
en
t
h
ar
m
o
n
ics
u
n
d
er
h
ig
h
ly
v
ar
ia
b
le
E
AF
o
p
er
atin
g
co
n
d
itio
n
s
.
I
n
co
n
tr
ast,
th
e
av
er
ag
e
VUF
o
b
tain
ed
with
th
e
o
p
tim
ized
d
esig
n
is
s
lig
h
tly
h
ig
h
er
th
a
n
with
th
e
co
n
v
en
tio
n
al
f
ilter
,
b
u
t
th
is
in
cr
ea
s
e
is
m
o
d
est
an
d
VUF
r
em
ain
s
well
b
elo
w
th
e
m
ax
im
u
m
p
e
r
m
is
s
ib
le
lim
it
im
p
o
s
ed
b
y
th
e
s
tan
d
ar
d
s
.
T
h
ese
tr
en
d
s
s
u
g
g
est
t
h
at
th
e
o
p
t
im
izatio
n
p
r
im
a
r
ily
f
a
v
o
r
s
t
h
e
r
ed
u
ctio
n
o
f
T
DD
an
d
T
HD
o
v
e
r
f
u
r
th
er
im
p
r
o
v
em
en
ts
in
VUF,
a
tr
ad
e
-
o
f
f
th
a
t is ex
am
in
ed
in
m
o
r
e
d
etail
u
s
in
g
th
e
n
o
r
m
alize
d
in
d
ices in
Fig
u
r
e
8
.
Fig
u
r
e
8
p
r
o
v
id
es
f
u
r
t
h
er
in
s
ig
h
t
in
to
th
is
tr
ad
e
-
o
f
f
b
y
s
h
o
win
g
T
HD,
T
DD,
an
d
VUF
n
o
r
m
a
lized
with
r
esp
ec
t
to
th
eir
co
r
r
esp
o
n
d
in
g
r
eg
u
lato
r
y
lim
its
.
A
n
o
r
m
ali
ze
d
v
alu
e
g
r
ea
ter
th
an
1
in
d
i
ca
tes
th
at
th
e
in
d
ex
ex
ce
ed
s
th
e
ad
m
is
s
ib
le
r
an
g
e,
wh
er
ea
s
v
alu
es
b
el
o
w
1
d
en
o
te
f
u
ll
co
m
p
lian
ce
with
th
e
s
tan
d
ar
d
s
.
I
t
ca
n
b
e
o
b
s
er
v
ed
th
at,
in
th
e
b
ase
ca
s
e,
T
DD
is
th
e
m
o
s
t
cr
itical
in
d
icato
r
,
s
in
ce
it
clea
r
ly
s
u
r
p
as
s
es
th
e
l
im
it,
wh
ile
VUF
alr
ea
d
y
lies
with
in
th
e
ac
ce
p
tab
le
r
an
g
e
.
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
Mu
lti
-
o
b
jective
o
p
timiz
a
tio
n
a
n
d
mu
lti
-
crit
eria
d
ec
is
io
n
a
n
a
l
ysis
o
f p
a
s
s
ive
…
(
A
lva
r
o
Ya
s
s
if Ma
r
ca
Yu
cra
)
1209
I
n
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
,
th
e
Par
eto
-
o
p
tim
al
s
et
g
en
er
a
ted
b
y
NSGA
-
I
I
is
p
o
s
t
-
p
r
o
ce
s
s
ed
u
s
in
g
th
e
MCDA
tech
n
iq
u
e
T
OPSIS.
T
h
e
d
ec
is
io
n
m
atr
ix
is
co
n
s
tr
u
cted
f
r
o
m
t
h
e
n
o
r
m
alize
d
in
d
ices
an
d
th
e
clo
s
en
ess
to
th
e
id
ea
l
s
o
lu
tio
n
i
s
co
m
p
u
ted
,
s
o
th
at
s
o
lu
tio
n
s
with
lo
wer
n
o
r
m
alize
d
T
HD
an
d
T
DD
(
p
ar
ticu
lar
l
y
th
o
s
e
ab
o
v
e
th
e
lim
it)
ar
e
r
an
k
ed
m
o
r
e
f
av
o
r
ab
l
y
.
As
a
r
es
u
lt,
th
e
s
elec
ted
o
p
er
atin
g
p
o
i
n
t
co
r
r
esp
o
n
d
s
to
a
d
esig
n
th
at
s
u
b
s
tan
tially
r
ed
u
ce
s
T
DD
an
d
T
HD,
ev
en
if
t
h
is
im
p
lies
a
s
m
all
in
cr
ea
s
e
in
VUF.
I
m
p
o
r
tan
tly
,
VUF
r
em
ain
s
wel
l b
elo
w
it
s
m
ax
im
u
m
p
er
m
is
s
ib
le
v
alu
e,
s
o
th
is
tr
ad
e
-
o
f
f
is
ac
ce
p
tab
le
f
r
o
m
b
o
th
a
tech
n
ical
an
d
r
eg
u
lato
r
y
p
er
s
p
ec
tiv
e.
Ov
er
all,
th
e
co
m
b
in
ed
NSGA
-
I
I
an
d
T
OPSIS
f
r
am
ewo
r
k
e
x
p
licitly
s
teer
s
th
e
o
p
tim
izatio
n
to
war
d
s
m
itig
atin
g
th
e
m
o
s
t
s
ev
er
e
n
o
n
-
co
m
p
lian
ce
s
(
m
ai
n
ly
T
D
D)
,
r
ed
u
cin
g
th
e
m
ag
n
itu
d
e
o
f
th
e
v
io
latio
n
an
d
y
ield
in
g
a
m
o
r
e
b
alan
ce
d
co
m
p
r
o
m
is
e
am
o
n
g
T
HD,
T
DD,
an
d
VUF.
T
h
is
r
eg
u
latio
n
-
o
r
i
en
ted
tu
n
i
n
g
ca
n
b
e
r
eg
ar
d
e
d
as
a
f
ir
s
t
s
tep
th
at
m
ay
b
e
co
m
p
lem
en
te
d
with
ad
d
itio
n
al
m
itig
atio
n
m
ea
s
u
r
es
if
s
tr
ict
f
u
lf
illme
n
t
o
f
all
lim
its
is
r
eq
u
ir
ed
.
Fig
u
r
e
8
.
No
r
m
alize
d
p
o
wer
q
u
ality
in
d
ices a
t th
e
PC
C
r
elati
v
e
to
th
e
b
ase
ca
s
e
5.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
p
r
esen
ted
a
m
eth
o
d
o
lo
g
y
f
o
r
o
p
tim
al
tu
n
in
g
o
f
s
h
u
n
t
p
ass
iv
e
f
ilter
s
in
E
AF
-
b
ased
s
y
s
tem
s
u
s
in
g
th
e
NSGA
-
I
I
m
u
lti
-
o
b
je
ctiv
e
o
p
tim
izatio
n
alg
o
r
ith
m
,
s
im
u
ltan
eo
u
s
ly
m
in
im
izin
g
T
H
D,
T
DD,
an
d
VUF.
T
h
e
ap
p
r
o
ac
h
co
n
s
id
er
ed
th
e
n
o
n
lin
ea
r
an
d
h
ig
h
ly
v
ar
i
ab
le
b
e
h
av
io
r
o
f
th
e
f
u
r
n
ac
e,
e
n
s
u
r
in
g
f
ilter
p
e
r
f
o
r
m
an
ce
u
n
d
er
c
r
itical
an
d
u
n
b
alan
ce
d
o
p
er
ati
n
g
s
ce
n
ar
io
s
.
T
h
e
r
esu
lts
s
h
o
wed
th
at
th
e
p
r
o
p
o
s
ed
d
esig
n
clea
r
ly
o
u
tp
er
f
o
r
m
s
co
n
v
en
tio
n
al
tu
n
i
n
g
,
ac
h
ie
v
in
g
m
o
r
e
e
f
f
ec
tiv
e
r
ed
u
ctio
n
s
in
p
o
wer
q
u
ality
in
d
ices,
esp
ec
ially
in
s
ce
n
ar
io
s
wh
er
e
r
eg
u
lato
r
y
l
im
its
ar
e
ex
ce
ed
ed
.
Mo
r
eo
v
er
,
v
alid
atio
n
ac
r
o
s
s
d
if
f
er
e
n
t
o
p
er
atin
g
s
tag
es
co
n
f
ir
m
e
d
th
e
r
o
b
u
s
tn
ess
o
f
t
h
e
o
p
tim
ize
d
f
ilter
u
n
d
er
d
y
n
am
ic
co
n
d
itio
n
s
.
Ov
er
all,
co
m
b
in
in
g
ev
o
lu
tio
n
ar
y
o
p
tim
izatio
n
with
d
ec
is
io
n
-
m
ak
in
g
m
eth
o
d
s
p
r
o
v
id
es
a
r
esi
lien
t
s
tr
ateg
y
t
o
a
d
d
r
ess
p
o
wer
q
u
ality
ch
allen
g
es
in
s
teelm
ak
in
g
f
ac
ilit
ies.
As f
u
tu
r
e
wo
r
k
,
th
e
e
x
ten
s
io
n
o
f
th
i
s
m
eth
o
d
o
lo
g
y
to
ac
tiv
e
o
r
h
y
b
r
id
ac
tiv
e
–
p
ass
iv
e
f
ilt
er
s
is
s
u
g
g
ested
,
as
well
as
its
ap
p
licatio
n
to
o
t
h
er
in
d
u
s
tr
ial
f
ac
ilit
ies
with
h
ig
h
ly
n
o
n
l
in
ea
r
lo
ad
s
b
e
y
o
n
d
s
teelm
ak
in
g
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
wo
r
k
was
s
u
p
p
o
r
ted
b
y
th
e
Deu
ts
ch
er
Ak
a
d
em
is
ch
er
Au
s
tau
s
ch
d
ien
s
t
(
DAAD
)
u
n
d
e
r
a
p
o
s
tg
r
ad
u
ate
s
ch
o
lar
s
h
ip
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Alv
ar
o
Yass
if
Ma
r
ca
Yu
cr
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Gastó
n
Or
lan
d
o
Su
v
i
r
e
✓
✓
✓
✓
✓
✓
✓
✓
J
o
h
n
Ar
m
an
d
o
Mo
r
ales
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
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