TELKOM
NIKA Indonesia
n
Journal of
Electrical En
gineering
Vol. 14, No. 1, April 2015, pp. 55 ~ 61
DOI: 10.115
9
1
/telkomni
ka.
v
14i1.762
9
55
Re
cei
v
ed
Jan
uary 8, 2015;
Re
vised Ma
rc
h 9, 2015; Accepte
d
March
23, 2015
A Modified Bat Algorithm for Power Loss Reduction in
Electrical Distribution System
Djosso
u Ade
y
em
i Am
on
Schoo
l of Elect
r
ic Engi
neer
in
g
,
Beijin
g Jiaot
o
ng Un
iversit
y
,
No.3 Sha
n
g
y
u
ancu
n
Hai
d
i
an
District, Beijin
g
,
China, 00
86-
10-5
168
453
5 / 0086-
10-
516
8 525
8
E-mail: djossamon@outlook.com
A
b
st
r
a
ct
Losses
on E
l
e
c
tric Distributi
o
n Syste
m
li
nes
repr
es
ent a
major c
hal
le
nge
for electric
dist
ributi
o
n
compa
n
ies si
n
c
e those loss
e
s
refer to the amo
unt of el
ectr
icity inject
ed in
to the
distributi
on gri
d
s that ar
e
not pai
d by us
ers. Netw
ork
Optimi
z
a
t
i
o
n
b
y
system
reco
nfigur
ation is o
ne of the solut
i
ons a
m
ong
man
y
others us
ed to
solve th
is pro
b
l
e
m. In th
is p
a
p
e
r a
mo
difie
d
v
e
rsio
n of n
e
w
Meta He
uristic
alg
o
rith
m b
a
se
d
on Bat beh
avi
o
r is propos
ed
to find the best system c
onfigur
ation w
i
th a low
loss rate, w
e
present tw
o
different
ap
pro
a
ches: r
e
d
u
cti
on
of se
arch
s
pace
a
nd
intr
od
u
c
ti
on
o
f
si
gm
oi
d fu
n
c
ti
on
to
fi
t the
al
go
ri
th
m to
the prob
le
m. T
he ma
in a
d
v
antag
es of the
propos
ed
me
thodo
lo
gy ar
e:
easy imple
m
entatio
n an
d l
e
ss
computati
o
n
a
l
efforts to find
an o
p
ti
mal s
o
l
u
tion. T
o
d
e
m
onstrate its effi
ciency th
e pro
pose
d
sche
m
e
is
tested on 3
3
Bus distrib
u
tion
system a
nd the
results show
loss reducti
on r
a
te of 33%.
Ke
y
w
ords
:
bat
algor
ith
m
, sig
m
o
i
d functi
on, pow
er losses,
opti
m
a
l
reconfi
gurati
on, opti
m
al pow
er flow
Copy
right
©
2015 In
stitu
t
e o
f
Ad
van
ced
En
g
i
n
eerin
g and
Scien
ce. All
rig
h
t
s reser
ve
d
.
1. Introduc
tion
Gene
rally Di
stribution Syst
em (DS
)
ope
rate in
ra
dial
config
uratio
n with many switch
es
locate
d al
on
g the
net
wo
rk at
strate
g
i
c p
o
ints [1]. We
h
a
ve t
w
o
main
switche
s
o
n
DS:
Sectionali
z
e
d
Switch
es (S
S) that a
r
e n
o
rmally
cl
o
s
e
d
and
Tie S
w
itche
s
(TS) t
hat are no
rm
ally
open
ed. Re
config
uratio
n
is a p
r
o
c
e
s
s to c
hang
e
topology of system by ch
angin
g
the st
ate
(clo
se/o
pen
)
of switche
s
. A normally o
pene
d TS is
clo
s
ed to tra
n
s
fer a lo
ad from one fee
d
e
r to
anothe
r while
an ap
pro
p
ri
a
t
e clo
s
ed SS
is op
ene
d to
resto
r
e th
e ra
dial structu
r
e
[2]. Its prese
n
t
many advant
age
s in two
ca
se
s: in ca
se of faul
t o
c
curs, it allo
ws i
s
olatin
g
a fault are
a
and
resto
r
in
g loa
d
to non-fault
area [3] and
in case
of normal p
r
o
c
e
s
s, it enhan
ce
s voltage pro
f
ile
,
load bal
an
cin
g
[4], reliabili
ty [5] and re
duce network lo
ss [6
-8]. In this pap
er we a
r
e mo
st
con
c
e
r
ne
d ab
out redu
cin
g
power lo
ss.
The effect of
losses
ca
n
be co
mpa
r
ed
to a pipe th
at is bein
g
constri
c
ted
as load an
d
ambient
air tempe
r
ature i
n
crea
se
s, thu
s
limiting
the
amo
unt of
p
o
we
r
and
en
ergy
availabl
e at
the end-use. The red
u
ctio
n of powe
r
loss
can im
p
r
o
v
e efficiency while redu
cin
g
overall po
wer
costs, improving voltage levels, and potentially
redu
cin
g
c
o
st
ly
inv
e
st
m
ent
s in
sy
st
em
improvem
ent
s. Redu
cin
g
power l
o
ss
b
y
cha
ngi
n
g
t
he state
of swit
che
s
ca
n
be define
d
as
optimizatio
n
probl
em, b
e
cause
we a
r
e
trying to
get
a b
e
st
co
nfiguratio
n a
m
o
ng ma
ny oth
e
rs
whi
c
h give
s u
s
a lo
we
r po
wer lo
ss
whil
e resp
ectin
g
the
con
s
trai
nts.
The optimi
z
at
ion in thi
s
case
is not
con
s
id
ered
a
s
a
si
mple on
e be
cau
s
e i
n
DS
we h
a
ve m
any co
nst
r
ai
nts an
d a lot
of
swit
che
s
which make the p
r
oblem combi
natorial
com
p
lex and a non
linear o
p
timization one [9].
Previou
s
re
se
arche
r
s have been
l
o
o
k
ing at
red
u
cin
g
lo
sses
by re
co
nfigurin
g the
syste
m
and
several
method
s hav
e bee
n devel
oped in
pa
ral
l
el with the t
e
ch
nolo
g
y’s i
m
provem
ent
and
innovation
s
i
n
compute
r
sci
en
ce and
mathemat
i
cs.
Thu
s
bra
n
ches exchan
g
e
meth
ods h
a
ve
been i
n
tro
d
u
c
ed
by Civa
nlar
and Al.
sin
c
e 1
988
a
nd
several p
ublication
s
h
a
ve mad
e
so
me
comm
ents o
n
the limitation of this meth
od whi
c
h
h
a
sn’t able to find the optimu
m
solutio
n
[10
]
.
Merlin
an
d B
a
ck al
so
introdu
ced
Math
ematical
opti
m
ization
mo
del b
a
sed
o
n
b
r
an
che
s
and
boun
d te
chni
cs that a
r
e
great
but
co
mputation ti
me is very l
ong [1
1]. D.
Shirmo
ham
madi
introdu
ce
d o
p
timal flow p
a
ttern al
go
rithm that
wa
s a h
euri
s
tic
method. T
h
is method
takes
minimizi
ng po
wer lo
ss as o
b
jective fun
c
tion [12].
Recently m
any re
search papers
used ar
tifici
al
intelligence technics t
o
solve
combi
nato
r
ial
nonline
a
r o
p
t
imization p
r
o
b
lems to o
b
tain an o
p
timal solutio
n
of global mi
nim
u
m.
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 14, No. 1, April 2015 : 55 – 61
56
These tech
ni
cs a
r
e called
Meta heuri
s
tics a
nd have
obtained the
good re
sult
s. Typical meta-
heuri
s
tic
met
hod
s incl
ude
Simulated An
nealin
g (SA),
Geneti
c
Algo
rithm [1, 7, 13], Tabu Sea
r
ch
[14, 15] Ant
Colo
ny Opti
mization
[16,
17], and
Particle Swarm
Optimizati
on
[18]. In reference
[19] a Selective Particle
Swarm 0
p
tim
i
zation
(SPS
O) is devel
o
ped ba
sed i
n
sea
r
ch sp
ace
method.
Since 2
010
a ne
w bio i
n
spired al
go
rithm ba
sed
o
n
bat e
c
hol
o
c
ation
beh
avior
was
prop
osed to
engin
eers to
solve ma
ny optimizat
io
n
probl
em
s. It has
bee
n d
e
mon
s
trate
d
that
unlike
othe
r optimizatio
n method
s
the diversity
of
th
e sol
u
tion
s in
the pop
ulati
on propo
se
d
b
y
BAT Algorith
m
can b
e
increa
sed [20]. In the field
of electri
c
di
stri
bution net
work loss redu
cti
on
this algo
rithm
is com
m
onl
y asso
ciate
d
with po
wer
disp
atch [21,
22], Optimal powe
r
flow[
23],
cap
a
cito
r pla
c
eme
n
t [24], locatio
n
and
size of DG u
n
its [25].
This p
ape
r try to associ
ate BAT Algorit
hm to
network re
co
nfigu
r
a
t
ion by modif
y
ing the
origin
al one t
o
find the be
st configu
r
atio
n of distri
b
u
tion network th
at exhibits th
e low rate of loss
by ch
oo
sing
the
state of
switch
es in
net
work. Init
ially
Bat Algorithm
is
sp
ace
sea
r
ch
alg
o
rithm,
to
adapt it in ou
r situatio
n we
input the sig
m
oid fun
c
tion
that chan
ge
s any fun
c
tio
n
in bina
ry one,
so th
en
we
can ea
sily d
e
ci
de the
switch
that sh
oul
d
b
e
ope
n a
nd
cl
ose. T
h
is alg
o
rithm i
s
te
sted
on IEEE 33 nodes and i
s
compared
with other method.
2. Recon
f
igu
r
ation Mod
e
l of Distribu
ti
on Sy
stem
As we me
ntion above the
purpo
se of
DS re
config
u
r
ation in
clud
e
decrea
s
ing t
he loss,
improvin
g vol
t
age qu
ality, power
sup
p
ly and
so
on.
No
wad
a
ys m
any re
se
arch
pape
rs are
more
intere
sted a
b
out minimum
netwo
rk l
o
sse
s
and l
oad b
a
l
ance. The m
a
in obje
c
tive in this re
se
arch
pape
r i
s
redu
cing
po
we
r l
o
ss. To
sim
p
lify the st
udy
we
su
ppo
se
that the lo
a
d
alon
g a
fe
eder
se
ction a
s
co
nstant P, Q loads
pl
aced at
the end of the lines an
d
e
v
ery switch is associ
ated with
a line in the sys
tem as
desc
ribe in [2].
2.1. Objectiv
e Functio
n
The obj
ectiv
e
functio
n
re
pre
s
ent
s the
tota
l power loss on th
e
system th
at we
can
expre
ss with the
bra
n
ch
resi
stan
ce
i
R
active and re
active powe
r
(,
)
ii
PQ
and bran
ch vo
ltage
i
V
as
:
22
2
1
(1
)
nb
ii
Lo
s
s
i
i
i
i
PQ
fP
k
R
V
The pa
ramet
e
rs
nb
rep
r
e
s
ent
the numbe
r
of bran
ch in
Distri
bution
Networks, vari
able
i
k
is
the switch
state
pl
aced on
b
r
an
ch
i
, this va
riabl
e can ta
ke o
n
ly
2 value
s
d
e
p
endin
g
on
th
e
state of switch, when
swit
ch is ope
n the
n
0
k
and reversi
b
ly when
clo
s
e
1
k
. The functi
ons
var
y
w
i
th
V
and
k
. This fu
nctio
n
re
presents
also
ou
r fitne
s
s fun
c
tion.
The o
b
je
ctive here i
s
to
minimize
f
un
der certai
n co
nstrai
nts.
2.2. Constrai
nts
The
distri
buti
on n
e
two
r
k p
e
rform
a
n
c
e
s
depe
nd
s o
n
certain
con
s
tra
i
nts. In thi
s
article, we
cho
s
e to do
our re
sea
r
ch with three
con
s
traint
s that are essential for a minimum sy
stem
operation:
a) Voltage p
r
ofile of the system
The net
wo
rk
reconfigu
r
atio
n is o
p
timize
d su
ch th
at the no
de voltage ma
gnitud
e
i
V
isn’
t
out of the voltage limit:
,m
i
n
,
m
a
x
(2
)
ii
i
VV
V
b) Cu
rrent Ca
pacity of the feede
r
The
curre
n
t g
oes t
r
ou
gh th
e bran
ch
i
I
sho
u
ld not
be hi
g
her th
en the
maximum all
o
wa
ble
c
u
rrent.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
A Modified Bat Algorithm
for Power Lo
ss Re
du
ction i
n
Electri
c
al…
(Djo
ssou Ad
eyem
i Am
on)
57
,m
a
x
(3
)
ii
II
c)
Radial
stru
cture of the n
e
twork
In order to m
a
intain the ra
dially of the sy
stem, the number of clo
s
e
d
lines in ea
ch loop
need
s to be less than the total numbe
r o
f
lines maki
ng
the loop as p
r
oscribe
d
, in other words n
o
loop are allo
wed in the
system where
i
k
is Switch stat
e 0 or 1 and
s
N
is Num
b
er of swit
ch in DS:
1
1(
4
)
s
N
is
i
kN
3. BAT
Algor
ithm
Bat Algorithm
(BA) is a
na
ture in
spired
me
taheu
ri
stic algorithm
de
veloped by X
i
n-She
Yang in 201
0
.
BA is based
on echol
ocation that is
an importa
nt feature of
bat be
havior. Bats a
r
e
a fasci
nating
grou
p of ma
mmals that rely on echolo
c
ation to d
e
tect ob
stacl
e
s in flight, finding
their
way int
o
ro
ost
s
a
n
d
forag
e
for fo
od [26]. Th
e
prin
ciple
of o
peratio
n of th
is
system i
s
as
follow: bats
emit a very loud soun
d p
u
lse a
nd
li
sten for the e
c
ho th
at bou
nce
s
ba
ck from
surro
undi
ng
obje
c
ts, thus
it can dete
r
mi
ne the di
sta
n
c
e bet
wee
n
them and
also
can di
stingui
sh
obsta
cle
s
an
d preys [27].
Based o
n
tha
t
behavior of bats: the abili
ty
to compute the distan
ce betwe
en th
em and
obje
c
t, echol
ocatio
n ca
n be use in su
ch a way t
hat it can be asso
ciated
with the object
i
ve
function to be
optimized [2
1]. To model this alg
o
rithm
Yang [28] ha
s set some ru
les a
s
follows:
1) All b
a
ts u
s
e e
c
h
o
lo
cati
on to
sen
s
e
distan
ce, a
n
d
they al
so
gue
ss th
e dif
f
eren
ce
betwe
en food
/prey and ba
ckgroun
d ba
rri
ers in
som
e
magical way.
2) Bats fly ra
ndomly with
velocity
i
v
at position
i
x
with
a fixed frequ
ency
mi
n
f
var
y
ing
wavele
ngth
λ
and
lo
udne
ss
A
0
to
se
arch
for prey. The
y
can
a
u
toma
tically adj
ust
the
wavele
ngt
h
(or freq
uen
cy
) of thei
r emit
ted pul
se
s a
n
d
adju
s
t the
rate of pul
se
e
m
issi
on
r
[0
, 1], depen
din
g
on the
p
r
oxi
m
ity of their target. Altho
u
gh the
lou
d
ness
c
a
n var
y
in many
ways
, we ass
u
me that
the loudn
ess
varies from a
large
(po
s
itive)
A
0
to a minimum co
nsta
nt value
A
mi
n
.
3) Although t
he loudn
ess
can vary in
many wa
ys,
we a
s
sume that the loudn
ess varie
s
from a larg
e (positive)
0
A
to a minimum co
nstant value
mi
n
A
.
For ea
ch b
a
t
i
b
, the position
i
x
i
, the veloc
i
ty
i
v
and the freq
u
ency
i
f
are initialize. For
each time ste
p
t, the maximum numb
e
r of iterations
, the movemen
t
of the virtual bats is given
by
updatin
g their velocity and positio
n usi
n
g
Equation (3
), (4) an
d (5
) a
s
follows:
mi
n
m
a
x
m
i
n
1
()
(
5
)
()
(
6
)
i
tt
t
ii
i
i
ff
f
f
vv
x
x
f
Whe
r
e
is a
random
num
b
e
r b
e
twe
en [
0
, 1],
i
f
is
use
d
to control
th
e pa
ce
and
range
of the bat’s
m
o
vement,
x
is a
cu
rre
nt be
st locatio
n
. Then
the ne
w solu
tion or
po
sition for the
bat
can b
e
gen
erated by the equation give
n
below:
1
(7
)
tt
t
ii
i
xx
v
One
solution
is sele
cted a
m
ong the
cu
rrent be
st solu
tions a
nd the
n
the ra
ndom
wal
k
is
use
d
to obtai
n a new
soluti
on:
(8
)
ne
w
o
l
d
t
i
xx
A
is the averag
e loudne
ss of all Bats, a random num
be
r betwe
en [0,1]. The local sea
r
ch
is laun
ch
ed d
epen
ding on t
he pul
se rate
i
r
.
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 14, No. 1, April 2015 : 55 – 61
58
It shoul
d be
n
o
ted that
whe
n
Bat find
pre
y
the rate
of
pulse emi
s
sio
n
i
r
i
n
c
r
ea
se
and
th
e
loudn
ess
i
A
decrea
s
e[29].Fo
r
ea
ch ite
r
atio
n the lo
udn
ess
i
A
and th
e e
m
issi
on
pul
se
rate
i
r
are
update
d
, as follows:
1
0
(9
)
1e
x
p
(
)
(
1
0
)
tt
ii
t
ii
AA
rr
t
Whe
r
e
and
are con
s
tants.
At the first step of the alg
o
r
ithm the valu
es of the
s
e t
w
o
para
m
eters a
r
e ch
osen ran
domly, gene
rally
(0
)
1
,
2
i
A
and
(0
)
0
,
1
i
r
.
Based o
n
the above app
roximation
s and ideali
z
ati
on, the pseu
do-cod
e
of the Bat
Algorithm (B
A) can b
e
su
mmari
zed b
e
l
o
w:
Step1
: Initialize the bat po
pulation o
r
their po
sition
i
r
and their velo
cities
i
v
.
Define
pulse
freq
ue
nc
y
i
f
at
i
x
. Initiali
ze pulse rates
r
and th
e loudne
ss
A
.
Step2:
Gen
e
rate new
soluti
ons by adj
usti
ng frequ
en
cy, and upd
ating
velocities a
n
d
Location
s
/sol
utions.
Step3
: if (
r
and
r
) Select a solutio
n
among the
best solution
s. Generate a local
solutionthe selected best solution.
Step4
: Else g
enerate a ne
w sol
u
tion by flying rando
m
l
y.
Step5
: If (
i
ra
n
d
A
) a
nd
()
(
)
i
fx
f
x
Accept the
new solution
s, increa
se r
and re
du
ce
A
Step6
: Rank
the bats an
d find the cu
rren
t best
x
Step7
:
whil
e
(iteration
< Max n
u
mb
er
of iterations) Po
st
p
r
ocess re
sul
t
s an
d
visuali
z
ation.
The algo
rithm
stops
with the total-be
st solution.
4. Proposed
Metho
dolog
y
w
i
th Modifi
ed BAT Algo
rithm
To better
sol
v
e our p
r
obl
e
m
and fa
cilita
t
e its implem
entation, we cha
nge th
e initial Bat
Algorithm
on
one h
and
a
nd on th
e ot
her
hand
mo
dify the initial desi
gn of t
he di
stributio
n
netwo
rk,
whi
c
h allo
ws o
u
r
algorith
m
to
be effe
ct
ive a
nd fa
st. The
DNR’
s resolu
tion process t
hat
we offer
can
be divided int
o
three
steps
descri
bed b
e
l
o
w:
a) Specific
a
tion of the nu
mber of dimensions
In this ste
p
radial de
sig
n
of the distri
b
u
tion net
work is tran
sfo
r
m
ed into de
sig
n
loop by
clo
s
ures Tie
swit
ch
(Norm
a
lly Open
). T
he nu
mb
e
r
of
loop
s
co
rre
spondi
ng to th
e dime
nsi
o
n
s
of
numbe
rs. Fig
1
sh
ow th
e d
i
stributio
n net
work
with 33
node
s. Blue l
i
nes
rep
r
e
s
e
n
t se
ctionali
z
ed
swit
ch an
d re
d lines tie swi
t
ch. By closin
g tie switch the system cha
nge to multilo
op circuit with
5
loop
s so 5 di
mensi
o
n
s
.
b)
Find sear
ch area
Each
dimen
s
i
on rep
r
esent
s o
ne
sea
r
ch
are
a
. And thi
s
sea
r
ch i
s
th
e set of all
branche
s
forming the lo
op. The bra
n
c
h is not pa
rt of one
loop a
r
e exclu
ded from the sea
r
ch area, an
d the
bran
ch
es bel
ongin
g
to
se
veral lo
op m
u
st ne
ce
ssa
r
i
l
y belong
to
one a
nd
only
one l
oop, thi
s
i
s
done rand
oml
y
.
c) Find opti
m
um solution
The i
dea
he
re is to fin
d
o
r
determi
ne th
e
statu
s
of the
Switch,
whi
c
h
allo
ws u
s
to
have a
low lo
ss
rate
s. In the
ori
g
inal alg
o
rith
m the p
o
sitio
n
of be
ats i
s
rep
r
e
s
ent
ed
by continuo
us-
valued po
sitions, which is not very adequate for ou
r purpo
se. Sin
c
e the switch
has two stat
es
clo
s
ed o
r
op
en, the ideal
in this contex
t would b
e
to
alter the po
sition of the b
a
t to a seri
es of
binary
value.
Thi
s
i
s
p
o
ssi
b
le by th
e
si
gmoid
fun
c
tio
n
that
re
stri
cts the
n
e
w po
sition
of the
bat
has o
n
ly bina
ry values. Thi
s
metho
d
is
widely explai
ned in[29, 3
0
]. Thus the foll
owin
g functio
n
is
use
d
:
1
()
(
1
1
)
1
t
i
t
i
v
Sv
e
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
A Modified Bat Algorithm
for Power Lo
ss Re
du
ction i
n
Electri
c
al…
(Djo
ssou Ad
eyem
i Am
on)
59
And the Equa
tion (6)
can b
e
repla
c
e
d
by:
1(
),
(
12)
0
t
t
i
i
if
S
v
x
other
w
i
s
e
With
(0
,
1
)
U
.
To sum
m
ari
z
e our meth
o
d
in a simpl
e
way, each
switch located on ea
ch
bran
ch i
s
rep
r
e
s
ente
d
by a bat in o
u
r alg
o
rithm.
The ne
w p
o
sition of the la
tter determi
n
e
s o
r
not if this
swit
ch is
ope
n or cl
osed p
o
sition a
nd the value we
assign to the
param
eter
k
of our obje
c
t
i
ve
function. F
o
r
each iteratio
n
power flo
w
i
s
pe
rfor
med,
the co
nst
r
aint
s a
r
e
che
c
ke
d. This i
s
d
o
n
e
until the small
e
st po
ssi
ble value of the po
wer lo
ss is fo
und.
Figure 1. Flow ch
art of pro
posed metho
d
5. Simulation, Results a
nd Analy
s
is
The p
r
op
ose
d
modificatio
n
of BAT Algorithm i
s
pro
g
ramm
ed in
MATLAB environm
ent
and has been tested
on I
EEE 33 busses of
Distri
bution Net
w
ork.
Figur
e 2 bellow
shows the
initial con
d
itio
n of the syst
em t
hat co
nsists of 37
switche
s
wh
ereb
y 5 of them are tie switch
es
and the
rem
a
ining 3
2
a
r
e
se
ctionali
z
ing
swit
che
s
if
we co
nsi
der th
at on ea
ch
branch the
r
e i
s
a
swit
ch. Th
e
norm
a
lly ope
ned
swit
che
s
are
(9
-15
)
,
(3-18),
(21
-
2
8
), (1
2-22),
(25-2
9
). F
o
r t
h
is
ca
se, the initial real po
we
r
loss is 20
2.68
kW.
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 14, No. 1, April 2015 : 55 – 61
60
Figure 2. 33 Nod
e
s Te
st System
Table 1. Modi
fied Bat Algorithm Main
Parameters
n
A
0
r
0
f
min
f
max
40 0.5
0.5
0
2
Table 2. Re
sults obtain
ed
and compa
r
e
d
S
y
stem
Loss
Reduction%
Tie Lines
Before
Reconfiguration
0
8-21 / 9
-
15
12-22 / 18
-33
25-29
After
Reconfiguration
using Modified
BAT
33
(5-6 / 8
-
21
23-24 / 25
-29
27-26
After
reconfiguration
using SPSO[19]
31
7-9 / 9-10
14-15 / 25
-29
32-33
The re
sult o
b
t
ained after p
e
rform
ed the
algorith
m
is resum
e
in tabl
e 2. Cal
c
ulati
on he
re
indicates that
a percenta
g
e
red
u
ctio
n in real
p
o
wer loss i
s
33%.
The re
du
ctio
n of the sea
r
ch
spa
c
e
ha
s m
u
ch
ma
ke
s t
h
ing
s
e
a
sie
r
i
n
t
he
se
ns
e t
h
at the
algo
ri
thm ha
s n
o
l
onge
r
to
se
arch
throug
h the whol
e syste
m
but just a
part of t
he system. The numbe
r
s of tie lines in the ne
w
config
uratio
n doe
sn’t ch
an
ges.
6. Conclusio
n
This pa
pe
r propo
sed a mo
dification of
Bat-inspire
d
algorith
m
for
redu
ce p
o
we
r loss in
Distri
bution S
y
stem. Introd
uction of
sig
m
oid fun
c
tion
and re
du
ctio
n of sea
r
ch space are ne
ws
and playe
d
a key role to
the implem
entation of t
h
e algo
rithm.
The result demon
strates the
effectivene
ss of this alg
o
rit
h
m
by high
redu
cing
real
power lo
ss. T
h
is mo
del o
p
timization
ca
n
be
applie
d to se
veral cases i
n
engi
nee
ring
. The challen
ge to come i
s
to che
c
k its
effectivene
s
s on
a large
r
sy
ste
m
with integrati
on of Distri
buted Ge
nera
t
ion and re
du
ction of switching op
eratio
n.
Referen
ces
[1]
Nag
y
S, Ahme
d M. N
e
t
w
ork
reconfi
gurati
o
n
for l
o
ss
re
duc
tion in electric
al distrib
u
tion
s
y
stem usi
n
g
G
enetic al
gorit
hm
.
Arab Jour
nal of Nuc
l
e
a
r Scienc
e an
d Applic
atio
ns
. 20
13: 78-8
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
A Modified Bat Algorithm
for Power Lo
ss Re
du
ction i
n
Electri
c
al…
(Djo
ssou Ad
eyem
i Am
on)
61
[2]
Z
ehra E MM, Hashim M, Kashem M. Net
w
o
r
k re
co
nfigu
r
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SAT
for loss reducti
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n
Distributi
on s
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Iraqi Jou
r
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ic Engi
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. 2010; 6(1): 6
2
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Hata
w
a
y GWT
,
Stephens
C.
Impl
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m
ent
atio
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gh-s
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d dist
ri
buti
on n
e
tw
ork reconfig
urati
o
n
Schem
e
. Inter
natio
nal
Po
w
e
r S
y
stems
Confer
ence:
Advanc
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e
tering, Pr
otection, C
ontro
l
,
Commun
i
cati
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n
, and Distri
but
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[4]
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g Jin J
Z
, Ying Sun, Keju
n Li, Boqi
n Z
hang.
Dist
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ibuti
on netw
o
rk reconfig
urat
ion for lo
ad
Bala
ncin
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i
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a
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ar
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p
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at
io
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. Internatio
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nferenc
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w
e
r S
y
ste
m
T
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chnolog
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h
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n
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n L. Relia
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n
net
w
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gurati
on.
Intern
ation
a
l Jo
urna
l
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w
e
r & Energy Systems
. 20
12
; 34(1):138-
14
4
[6]
Cher
agh
i M, R
a
meze
npo
ur P
.
An efficie
n
t-fast meth
od for
deter
mi
nin
g
mi
ni
mu
m
loss c
o
nfigur
ation
i
n
radi
al distri
buti
on netw
o
rks
b
a
sed o
n
se
nsi
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w
e
r
Engi
neer
in
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d Optimizatio
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ideri
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a
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hang, W
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a
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Appl
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uati
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strib
u
ti
on netw
o
rk rec
onfig
uratio
n
.
Internatio
na
l Confer
ence
o
n
Adva
nces i
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g
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netw
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co
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h
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onfer
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ectric
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ntrol
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CECE)
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a
lcao
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dist
ributi
on syste
m
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. Proceedi
ngs
of the 2002
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g
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v
oluti
onar
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mputatio
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o
lul
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i
HMJ.
Distrib
utio
n
s
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optimiz
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bas
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o
n
a
li
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p
o
w
e
r-flo
w
f
o
rm
ulati
on.
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n
s
a
c
ti
on
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we
r D
e
l
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w
a
nad
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BPR.
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la
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a
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unb
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a
n
c
ed d
i
stri
bu
ti
on
syste
m
s fo
r l
o
ss
minimis
a
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.
IET
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T
r
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le
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x
PF, H
a
mam Y.
Distr
ibuti
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o
r
k
reco
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urati
on us
in
g
ge
ne
tic al
gorith
m
a
nd
loa
d
fl
ow
.
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na
l C
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n
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n
Po
w
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erg
y
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ej
un
Xu, C
W
Xi
aol
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.
A tab
u
se
arch
appr
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stributio
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e
tw
ork reco
nfig
ura
t
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base
d
o
n
GIs
.
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o
rksh
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ig
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