Indonesian J
ournal of Ele
c
trical Engin
eering and
Computer Sci
e
nce
Vol. 1, No. 3,
March 20
16, pp. 556 ~ 5
6
5
DOI: 10.115
9
1
/ijeecs.v1.i3.pp55
6-5
6
5
556
Re
cei
v
ed
De
cem
ber 9, 20
15; Re
vised
Febr
uary 19,
2016; Accept
ed Feb
r
ua
ry
29, 2016
Advanced Optimal for PV syst
em cou
p
led with PMSM
Salam Waley
Shneen
Schoo
l of Elect
r
ical & Electro
n
i
c Engi
neer
in
g, Huaz
h
o
n
g
Uni
v
ersit
y
of Sci
e
n
c
e and T
e
chno
log
y
/W
uh
an,
Chin
a
Electromec
han
ical En
gin
eeri
n
g Dep
a
rtment,
Univers
i
t
y
of
T
e
chn
o
lo
g
y
/Bag
hda
d,
Iraq
e-mail: salam_w
a
ley
73@y
a
hoo.com
A
b
st
r
a
ct
T
he
mai
n
adv
antag
es of PMSM are
h
i
gh
torque
de
nsit
y, hig
h
effici
ency
and
s
m
all s
i
z
e
.
Photovoltaic power generation syst
em
P
V
generation
tec
hnology is treated as the m
o
st promising
technology among renewabl
e energies. Photovoltaic
(PV)
power ge
neration system is
a prom
is
ing source
of ener
gy w
i
th great i
n
terest i
n
clea
n a
nd re
new
abl
e en
erg
y
sources. To
use differ
ent c
ontrol syste
m
s,
like
Classic
a
l PI C
ontrol
l
er, Expe
rt System F
u
zzy Logic
Co
ntrol
l
er an
d Opti
mi
zation PSO C
o
n
t
roller. It used t
o
control f
o
r power electronic (inverter) & PM
SM which
work
ed in the integr
ation system
to PV energy. Ther
e
are tw
o
parts
in th
is
pap
er,
first part a
d
va
nced
Opti
ma
l
PSO, F
u
zz
y
&
PI Co
ntrol
l
er
pow
er e
l
ectro
n
i
c
(inverter) with PV for using different control system
s
this part on the generator
side at
constant torque.
Secon
d
part in
the loa
d
sid
e
of variab
le torq
u
e
, by
using
different contro
l systems w
i
th PMSM to analy
z
e a
l
l
results after using the si
mu
lat
i
on
mod
e
l of p
r
opos
ed bas
ed
PV system. The PV system is couple
d
w
i
th
PMSM. A closed l
oop c
ontr
o
l syste
m
w
i
th a PI contro
l,
Fu
z
z
y
,
PSO in the s
pee
d l
oop w
i
th curr
e
n
t
control
l
ers. T
h
e
mai
n
obj
ecti
ve of th
e pr
es
ent w
o
rk
is
to
desi
gn
a PV s
ystem
by
usin
g Matl
ab S
i
mu
lin
k
progr
a
m
and
a
pply this syste
m
to adv
ance
Optima
l for PV system co
upl
e
d
w
i
th PMSM.
Ke
y
w
or
ds
:
PV, Inverter, PSO
, FL
C, PI Controller &PMSM
1. Introduc
tion
Incre
a
si
ng o
r
i
entation for the use of PV in
industry a
nd ele
c
trical applia
nce
s
b
e
ca
use
PV energy is
predi
ctabl
e to
play a bi
g rol
e
in future
sm
art g
r
ids a
s
di
stributio
n ren
e
wa
ble
sou
r
ce
[1]. PV system with PMS
M
drive i
s
in
vestigated.
T
he PV sy
ste
m
appli
c
ation
is p
r
o
s
pe
cte
d
, in
orde
r to
hig
h
l
i
ght the i
r
radi
ation effe
ct o
n
the PV
pan
el feedi
ng th
e
PMSM [2]. PV sou
r
ce to
a
n
AC voltage source by inve
rter ha
s the a
b
ility for
controlling a PMSM [3]. A way controlle
r (PI) i
n
addition to t
he controller integral rel
a
tive fo
rmulat
ed an
d impl
emented,
usi
ng spee
d co
ntrol
magnet
syn
c
hron
ou
s mot
o
r d
r
ive
syste
m
and
a
p
e
rmanent pilot pha
se. While
the
ne
w stra
tegy
prom
otes tra
d
itional PI co
ntrol pe
rform
ance to
a large extent, an
d prove
s
to
be a mod
e
l-f
r
ee
approa
ch
co
mpletely, it al
so
ke
ep
s the
stru
ctu
r
e
an
d features of
a sim
p
le PI
controlle
r [8].
The
use
con
s
ole
s
mode inste
ad of Fuzzy-PI contro
l to
improve the
perform
an
ce of engine
s o
f
PMSM. To control the
sp
eed of PMS
M
motor u
s
in
g fuzzy logic
(FL
)
app
ro
ach lead
s to a
spe
e
d
control to im
prove the dy
namic
behavi
o
r of t
he mot
o
r drive
syst
em and imm
une di
so
rde
r
s to
download
an
d para
m
eter
variation
s
[9]. In the driv
e system
s an
d gain
s
from th
e traditional
can’t
usu
a
lly be
se
t in prop
ortio
n
-integ
ral
(PI) cont
roll
er
speed l
a
rg
e e
noug
h be
ca
u
s
e of m
e
cha
n
ical
resona
nce. As a
re
sult, pe
rforma
nce d
e
g
rad
a
tion
an
d sp
eed
cont
rol. In ou
r
wo
rk
de
scribe
d
in
this pa
per, h
a
v
e been
ado
p
t
ed and fu
zzy logic
co
ntroll
er (FLC) fo
r u
s
e in
drive
system
s in o
r
de
r
to improve th
e perfo
rma
n
ce of the spe
e
d
cont
rol. Th
e pro
p
o
s
ed F
L
C ha
s be
en
comp
are
d
wi
th
traditional PI control with resp
ect to the spe
ed
of re
sp
onse and dyn
a
mic loa
d
torque. Simulati
on
and expe
rim
ental re
sults have
p
r
ov
ed
that FL
C
wa
s propo
se
d i
s
sup
e
rio
r
to
th
e tra
d
itional
PI.
This F
L
C
ca
n be a g
ood
solution fo
r
the high
-pe
r
forma
n
ce eng
ine lifts syst
ems. A mod
e
rn
approa
ch to
control the
sp
e
ed of PMSM
usin
g pa
rticle
swarm
optim
ization
(PSO) to improve th
e
algorith
m
p
a
r
amete
r
s o
b
s
erve
r PI-.
Simulate
the
syste
m
u
n
der differe
nt ope
rating
y
ear
con
d
ition
s
is prep
ared and
the experime
n
tal se
tup. Use PSO algorit
hm and optim
ization ma
ke
a
powerful en
g
i
ne, with fast
er re
sp
on
se
and hig
her
resol
u
tion dyn
a
mic a
nd se
nsitive to load
variation [10
-
11].
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 25
02-4
752
IJEECS
Vol.
1, No. 3, March 20
16 : 556 – 565
557
2. Model for a PMSM Dri
v
e
In Figure
1
. Block diag
ram
of a PMSM & Figur
e 2. Block diag
ram
of a PMSM Drive.
The co
mplete
nonline
a
r mo
del of a PMSM without da
mper
windi
ng
s is a
s
follows:
)
+
i
(L
+
i
pL
+
Ri
=
v
af
d
d
s
q
q
q
q
(1)
i
L
-
p
+
Ri
=
v
q
q
s
d
d
d
(2)
v
d
and v
q
are
the d,q axi
s
v
o
ltage
s, id a
n
d
iq a
r
e th
e d
,
q axis
stater
curre
n
ts, Ld
and L
q
a
r
e th
e
d,q axis ind
u
c
tan
c
e,
R a
n
d
s
are
the st
ater re
sista
n
ce and
inve
rter frequ
en
cy
respe
c
tively.
af
is the flux linkage du
e to the
rotor m
agn
ets linki
ng the
stator.
The elec
tric
torque:
)/2
i
)i
L
-
(L
i
3P(
T
q
d
q
d
q
af
e
(3)
The moto
r dynamics:
r
r
Jp
B
T
-
T
L
e
(4)
P is the num
ber of pol
e p
a
irs, TL i
s
the
load torq
ue, B is the dam
ping coefficie
n
t,
r
is the
roto
r
spe
ed an
d J the moment
of inertia. The inverte
r
freque
ncy is
related to the
rotor
spe
e
d
as
follows
:
r
s
p
(5)
The m
a
chine
model i
s
nonl
inear a
s
it
co
ntains produ
ct
terms such as sp
eed
wit
h
id and
iq. Note
that
r
, i
d
and i
q
are stat
e vari
able
s
. Duri
ng
vector contro
l, i
d
is normall
y forced to be
zero
q
t
q
e
i
K
/2
i
3P
T
af
(6)
T
L
-
-
r
T
L
-
-
i
q
i
q
r
ref
+
v
q
Figure 1. Block dia
g
ram of a PM
SM
Figure 2. Block diag
ram
of a PMSM Drive
3. Speed Control of PMSM Motor
(Figu
r
e
3. Bl
ock
Diag
ram
of Spe
ed
Control
of PM
SM) Th
e PM
SM is
usi
ng
control to
sup
p
re
ss ha
rmonic
noi
se t
o
a level. Th
en, noi
se to
a level belo
w
and vibration
transl
a
tes i
n
to a
more co
mfort
able ride
fo
r passe
nge
rs.I
GBT
SPWM
i
n
verters m
a
ke the ri
de m
o
re
smooth
e
r
with
pre
c
isely adj
usting
spee
d
control
with freque
ncy
an
d voltage reg
u
lation. It has the latest low-
noise power
units to make the ride ev
en quiet
e
r
.Elevator ha
s di
recte
d
high
-speed u
s
ed
(1
500
s
L
R
q
1
af
p
k
t
S
p
eed
C
ontr
o
l
C
u
rre
nt
C
ontr
o
l
J
s
B
1
af
p
s
L
R
q
1
k
t
Js
B
1
Evaluation Warning : The document was created with Spire.PDF for Python.
IJEECS
ISSN:
2502-4
752
A
d
van
c
e
d
Op
t
i
m
a
l f
o
r P
V
syst
em
co
uple
d
wit
h
P
M
S
M
(Sal
am
Wal
e
y
Sh
ne
en
)
558
rpm) PMSM.
Energy reform in the elev
ator ge
ar
ed for small ri
se
becau
se trav
el extremely
small
and fast.
Current
m
easure
m
ent
Current
com
m
and
Speed m
easurem
ent
Figure 3. Block
Diag
ram o
f
Speed Cont
rol of PMSM
3.1. PI Contr
o
ller Modeling
In the PI speed cont
rolle
r the machine speed is
comp
ared
with the referen
c
e sp
e
ed and
the spe
ed error is the nth
sampling inte
rval as
ω
e[n] =
ω
r*[n] –
ω
r[n] (7)
The
o
u
tput
of
the spe
ed controlle
r gives
th
e
refe
re
n
c
e to
rqu
e
. Hence the
out
put of the
spe
ed control
l
er at the nth sampli
ng inte
rval is
T[n] = T[n-1] + Kp(
ω
e[n] –
ω
e[n-1]) +
Ki.
ω
e[n] (8)
For con
s
tant air gap flux o
peratio
n refe
r
ence qua
drat
ure axis
cu
rre
nt is given as
iq* =
T[n]/Kt
(9)
The limite
r
i
s
u
s
ed
to li
mit the maxi
mum value
of output of
spe
ed
cont
ro
ller. The
maximum ma
chin
e rated
current and d
e
v
ice cu
rr
ent o
f
the converte
r dictate the li
mit.
Whe
r
e,
ω
e[n] is spe
e
d
error at nth
instant,
ω
r*[n]
is the referen
c
e spee
d at nth instant
ω
r[n] is the actual ma
chin
e spe
ed at nth instant,
ω
e[n-1] is the
sp
eed erro
r at (n-1
)th insta
n
t
T[n] is the referen
c
e to
rqu
e
at nth insta
n
t, T[n-
1] is the referen
c
e torqu
e
at (n-1)th instant
Kp is pro
porti
onal gai
n of the sp
eed
con
t
roller
Ki is integral
gain of the sp
eed controll
er is referen
c
e
quad
ratu
re a
x
is curre
n
t
Kt is torque consta
nt
3.2. Fuzzy
Logic Con
t
rol
l
er
Fuzzy logi
c
controlle
rs hav
e the follo
win
g
adv
anta
g
e
s
over th
e
con
v
entional
con
t
rollers
that they are
che
ape
r to d
e
velop, they cover
a
wi
de
rang
e of op
erating c
onditio
n
s, an
d they are
more
readily
cu
stomizable
in n
a
tural la
ngua
ge te
rm
s. In M
a
md
a
n
i type FIS t
he
cri
s
p
re
su
lt is
obtaine
d by
defuzzificatio
n
, in the
Ma
mdani
FIS
ca
n be
u
s
ed
for both
multiple
input
and
si
ngle
output and m
u
ltiple input
s multiple outp
u
ts syste
m
as sho
w
n in figu
re 4.
Load
Inverter
PMSM
Spee
d
cont
r
o
l
l
e
r
Current
cont
r
o
l
l
e
r
DC
S
p
eed
com
m
and
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 25
02-4
752
IJEECS
Vol.
1, No. 3, March 20
16 : 556 – 565
559
Figure 4. Arra
ngeme
n
t of fuzzy logi
c cont
rolle
r
The u
s
efuln
e
ss
of fuzzy logic
co
ntroll
er is ado
pte
d
espe
cially
in a compl
e
x and
nonlin
ear
system. The rules of conventional
FL
C are produ
ced dep
end
on the ope
rator'
s
experie
nce or gene
ral
kno
w
led
ge of the
system in
a
heuri
s
tic
way.
The thresh
ol
ds of the fu
zzy
lingui
stic vari
able
s
are usually cho
s
e
n
arbitrarily
in the de
sign
proce
s
s. An improp
er
cont
ro
ller
value lea
d
s to an
adve
r
se
co
nseque
nce, un
stabl
e mode, colla
p
s
e and se
paration.
Thi
s
work
prop
ose BBO to design a
n
Optimal Fuzzy Logi
c Co
n
t
roller O
F
LC,
the optimize
d
crite
r
ia is h
o
w
to minimizin
g
the tran
sient state.
3.3. Particle S
w
arm Opti
miz
a
tion
The big
g
e
s
t
cha
r
a
c
teri
stic of PSO is in
its
sim
p
le structu
r
e,
fa
st conve
r
ge
nce,
and
its
ability to prevent falling i
n
to a local optimum so
luti
on. At the same time, P
S
O is a
random
algorith
m
wit
h
a pa
rall
el structu
r
e. A u
n
iform di
stri
b
u
tion is
used
to ran
domly
cre
a
te a
parti
cle
swarm. Each
particle
rep
r
ese
n
ts a fea
s
ible sol
u
tion to the probl
e
m
, the particl
e swarm refe
rs to
the individ
ual
’s b
e
st
expe
rience, an
d th
e g
r
oup’
s
be
st expe
rien
ce
, and l
ogi
call
y cho
o
ses th
e
method it will
move it
self.
Afte
r continu
ous iteration
s
, the p
a
rtic
l
e
swarm
will
g
r
avitate towa
rds
the optimum
solutio
n
. For
the i
th
particl
e and n-dime
nsio
nal spa
c
e can b
e
rep
r
esented a
s
an
equatio
n (12
)
, the best pre
v
ious po
sition
of
its particle
is re
corded a
s
equ
ation (1
3):
x
x
,
,x
,
,
…
…..x
,
(10
)
P
P
,
,P
,
,……P
,
(11
)
The velo
city is an e
s
sentia
l part of ho
w PSO work
so
as modifie
d
velocity and
positio
n of ea
ch
particl
e ca
n b
e
cal
c
ulate
d
usin
g the
cu
rrent velocity and dista
n
ce from (
P
,
) to (
g
):
V
,
.
W
∗
V
,
.
c
1∗
r∗
P
,
x
,
.
c
2
∗r∗
g
x
,
.
(12
)
x
,
.
x
,
.
v
,
.
(13
)
Whe
r
e
n i
s
th
e num
be
r of
particl
es in
a
grou
p; m i
s
n
u
mbe
r
of
Dim
ensi
on m
= 1
,
2, …, ; It is
a
pointer of it
eration
s
(g
e
neratio
ns);
W i
s
a
n
in
e
r
tia weight f
a
ctor;
c1, c2
are
a
c
cele
ratio
n
con
s
tant
s we
re often
set t
o
be 1.2
acco
rding to
pa
st
experie
nces;
r is
ran
dom v
a
lue in th
e ra
nge
between [0,1];
V
,
.
: Veloc
i
ty of partic
l
e no. i at iteration It.,
x
,
.
: Curre
nt po
sition of pa
rticle i at
iteration It.
G
:
Global b
e
st p
a
rticle
amon
g
all the par
ti
cles in the p
o
pulation. In the above
pro
c
ed
ures, t
he pa
ram
e
ter determi
ned t
he re
sol
u
tion,
or fitness, with whi
c
h regi
ons
are to
b
e
sea
r
ched b
e
twee
n the prese
n
t positio
n and the ta
rget po
sitio
n
, the inerti
a weig
ht is set
according to the equ
ation (14).
.
(14
)
Whe
r
e: Iterm
ax is the maximum numb
e
r of iterati
ons
and Iter is the
current num
ber of iteratio
ns.
preprocessing
fuzzif
i
ca
tion
Defuzzif
i
c
a
tion
Post
processing
Rule
b
as
e
Inferenc
en
g
ine
Evaluation Warning : The document was created with Spire.PDF for Python.
IJEECS
ISSN:
2502-4
752
A
d
van
c
e
d
Op
t
i
m
a
l f
o
r P
V
syst
em
co
uple
d
wit
h
P
M
S
M
(Sal
am
Wal
e
y
Sh
ne
en
)
560
4. PMSM Connec
t
ed PV Sy
stem
PMSM–co
n
n
e
cted PV
system ha
s PV (Source),
DC-AC Inverte
r
a
nd PMSM (L
oad)
as
sho
w
in figure 5 belo
w
.
Figure 5. PMSM Conn
ecte
d PV System
The PV sy
stem ba
se
d o
n
the
single
-
diode PV, th
e Photovoltai
c
eq
uivalent
circuit
impleme
n
t all the param
eters
rep
r
e
s
ent
ed in Equatio
n (15
)
Figure 6. Photovoltaic equi
valent circuit
I
Iph
Io
e
xp
V
R
s
I
NsK
T
a
Iq
1
(15
)
Iph: is the ph
otocu
r
rent, Io: is the diode
satura
tio
n
cu
rre
nt, (NskT
)
/
q
is a therm
a
l voltage of the
array, Vt, Ns: number of cell
s in se
rie
s
, q=
1.6 x10
-
19 C
stand
s for the elect
r
on cha
r
ge, k:
mean
while i
s
the Boltzman
n’s con
s
tant, 1.38 x 10-
23
J/K, a: repre
s
ents the dio
d
e
ideal facto
r
.
Rs : se
rie
s
re
sista
n
ce, Rp
: parallel re
si
stan
ce
, The
photo
c
urre
nt influen
ced by
solar i
rra
dia
n
ce
and tempe
r
at
ure can be
ca
lculate
d
as in
Equation (16):
Iph
λ
1000
Iscr
γ
T
T
r
(16
)
Iscr: is the short–
circuit current at STC (25
°
C
a
nd
1000
W/m2),
Isc is the sh
ort–ci
rcuit cu
rrent
temperature coeffici
ent,
λ
: is the sol
a
r irradian
ce at 10
00 W/m 2
Io
I
rs
T
Tr
ex
p
qEg
a
K
1
Tr
1
T
(17
)
I
rs
: is the re
verse
satu
rat
i
on current, T
r
: the 298K
referen
c
e te
mperature, T
:
the operating
temperature i
n
K, Eg = 1.13 eV is the band en
er
gy gap of the se
micon
d
u
c
tor
use
d
in the cell,
Reverse
satu
ration current
I
rs
can be cal
c
ulate
d
as in
Equation (18):
Voc: the PV cell open
–ci
r
cuit voltage
Irs
I
e
xp
V
q
N
K
T
1
(18
)
5. Simulation Anal
y
s
is a
nd Results
The PV con
nect
with DC Lo
ad
(
Di
rect-cou
pled system
) o
r
AC Lo
ad (
S
yst
em
wit
h
inverter
) as
show in figu
re
s (7 & 8):
PV
DC-
A
C
In
verte
r
PMSM
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 25
02-4
752
IJEECS
Vol.
1, No. 3, March 20
16 : 556 – 565
561
Figure 7. Dire
ct-coupl
ed sy
stem
Figure 8. Sys
t
em with inverter
5.1. Simulation Res
u
lt of
PV Connec
t
w
i
th PMPM:
By using Sim
u
lation mo
del
of Gene
rato
r Side
by PV Sourc
e
with In
verter, Inverter (DC-
AC) is a fixed
powe
r
co
nve
r
ter PV syste
m
(D
C output
voltage) into AC output voltage.
The
simul
a
tio
n
mo
del
of P
V
system
co
upled
with
P
M
SM & Inverter a
s
sh
ow i
n
figu
re
9. T
h
e
Simulation re
sult as in figu
res
(10, 13
):
Figure 9. Simulation mod
e
l
of PV system coupl
ed wit
h
PMSM & In
verter
a. Simulation respon
se of i
n
verter in
put
voltage
b. Simulation respon
se of i
n
verter o
u
tpu
t
voltage
Figure 10. Simulation resp
onse of inverter at co
nst
ant
torque a. inverter in
put voltage b. inverter
output voltag
e
a. Simulation respon
se of i
n
verter in
put
voltage
b. Simulation respon
se of i
n
verter o
u
tpu
t
voltage
Figure 11. Simulation resp
onse of inverter at variabl
e torque
a. inverter inp
u
t voltage b. inverter o
u
tpu
t
voltage
Evaluation Warning : The document was created with Spire.PDF for Python.
IJEECS
ISSN:
2502-4
752
A
d
van
c
e
d
Op
t
i
m
a
l f
o
r P
V
syst
em
co
uple
d
wit
h
P
M
S
M
(Sal
am
Wal
e
y
Sh
ne
en
)
562
Figure 12. Simulation resp
onse of PMSM (AC
Load
) at con
s
tant torque
Figure 13. Simulation resp
onse of PMSM (AC
Load
) at varia
b
le torqu
e
{
T
o
rque
= [20 0
10 0
20 0 10 0]N.
m
5.2. Simulation Res
u
lt of
PV Connec
t
w
i
th PMDC:
To an
alyse
s
t
he 1
st
step
re
sult a
s
sho
w
n in the ta
ble
1 an
d sho
w
in figure 14.
At time
betwe
en (t0
-
t1) ha
s ove
r
shot, but at time betw
een
(t1-t2
) ha
s u
ndersh
ot and
at time between
(t2-t3
) ha
s small overshot
, the steady
state after that in (t3-tn).
Whe
r
e, t0=0, t1=0.07
5
, t2=0.015, t3=0.2
, tn=2se
c
Table 1. Simulation Respo
n
se of PV direct co
nne
ct with PMDC, co
nstant torque,
T=50
N.m
Time(sec)
Speed(rp
m)
Power(W
)
Voltage(V)
Curre
nt(A)
to-t1
375
1900
60
32
t1-t2
335
1550
58
26
t2-t3
340
1610
58.5
27.5
t3-t4&t4-t
n
339.1
1600
58.44
27.33
a
.
T
o
r
q
ue
b. Power
c. Cu
rre
nt
d. Voltage
e. Speed
Figure 14. Simulation Respon
se of PV dire
ct
c
o
nnect with PMDC, c
o
ns
tant torque,
T=50
N.m (a.
Torq
ue b. Power
c.
Cu
rre
n
t
d. Voltage e. Speed)
To analyses t
he 2
nd
step re
sult as
sho
w
n
in the table 2. and sho
w
in
figure 15.
Whe
n
, T
=
25
N.m: At tim
e
bet
wee
n
(t
0-t1) ha
s
overshot, but
at time bet
ween
(t1-t2
) h
a
s
unde
rshot an
d at time betwee
n
(t2-t3
) has
small ove
r
sh
ot, the ste
ady state after that in (t3-tn
1).
Whe
n
, T
=
50
N.m: At time
between
(tn
1
-tn2
) h
a
s o
v
ersh
ot, but
at time b
e
tween
(tn2
-tn3
) ha
s
unde
rshot an
d at time betwee
n
(tn3
-tn
4
) ha
s sma
ll
overshot, the steady st
ate
after that in
(t4-
tm).
Where, t0=0, t1=
0
.075, t2=0. 15,
t3=0.2, tn1=1
s
e
c
an
d tn2=1.0
75, tn3=1.1
5
, tn4=1.2, tm=2
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 25
02-4
752
IJEECS
Vol.
1, No. 3, March 20
16 : 556 – 565
563
Table 2. Simulation Respo
n
se of PV direct co
nne
ct with PMDC, co
nstant torque,
T=25&5
0
N.
m
Time(sec)
Speed(rp
m)
Power(W
)
Voltage(V)
Curre
nt(A)
Torque
(N.m
)
to-t1
300
750
51.5
14.5
25
t1-t2
290
660
50.8
13.5
25
t2-t3
293
710
51.3
14
25
t3-tn1
292
700
51
13.8
25
tn1-tn2
375
1900
60
32
50
tn2-tn3
335
1550
58
26
50
tn3-tn4
340
1610
58.5
27.5
50
t4-tm
339.1
1600
58.44
27.33
50
a. Torqu
e
b. Power
c. Cu
rre
nt
d. Voltage
e. Speed
Figure 19. Simulation Respon
se of PV dire
ct con
n
e
c
t with PMDC, variable to
rqu
e
,
T= (2
5 and 5
0
)N.m
(a. Torque b. Powe
r c. Current d. Voltage e. Speed)
5.3. Simulation Analy
s
is
and Re
su
lts Use Differen
t
Con
t
rol
Sy
stems
Final step,
u
s
e different contro
l sy
st
e
m
s,
Li
ke Cla
s
s
i
cal
P
I
Cont
r
o
ller,
E
x
pe
rt
S
y
st
em
Fuzzy Logi
c Controll
er a
nd Optimization PSO Co
ntrolle
r with PMSM to analysis all re
sult.
Simulation
model
s (Cla
ssi
cal PI Controlle
r,
Expert System
Fuzzy Logi
c Co
ntrolle
r and
O
p
timiz
a
tion
PSO
C
o
n
t
r
o
lle
r
w
i
th
PMSM)
o
f
th
is s
t
ep
as
s
h
ow
n in
fig
u
r
e
s
(2
0-2
2
)
& th
e tr
an
s
f
e
r
function of th
e PMSM can
be obtain
ed from its
state
model by usi
ng the followi
ng formul
a:
T(S) =
C [SI-A] B +
D
(19)
The sim
u
latio
n
model a
s
shown in figures (2
0-22)
a
n
d
simulatio
n
result
s as
sho
w
n in figure (23).
a) PI Control
b) Fu
zzy_PI
Control
0
5
10
15
20
25
0
0.
2
0.
4
0.
6
0.
8
1
1.
2
1.
4
S
peed
Ti
m
e
S
peed
0
5
10
15
20
25
0
0.
2
0.
4
0.
6
0.
8
1
1.
2
1.
4
Ti
m
e
Sp
eed
Evaluation Warning : The document was created with Spire.PDF for Python.
IJEECS
ISSN:
2502-4
752
A
d
van
c
e
d
Op
t
i
m
a
l f
o
r P
V
syst
em
co
uple
d
wit
h
P
M
S
M
(Sal
am
Wal
e
y
Sh
ne
en
)
564
c) PSO _PI Control
d) PI Control, Fuzz
y_PI Control &PSO_PI
Control
Figure 23. Simulation Respons
e
(pu) Of
PI
Control, Fuzz
y_ PI Control &PSO_PI Control
6. Conclusio
n
s
To use different cont
rol systems a
s
a
case
stu
d
ie
s. To a
c
hiev
e this obj
ecti
ve which
cha
r
a
c
teri
ze
s each p
a
rt of
a sy
stem
su
ch a
s
a PV
module,
co
ntrolle
r an
d PM
SM. After that to
investigate th
e desi
gn con
nectio
n
topol
ogie
s
for a
ll compon
ents of
a PV system in orde
r to study
the operation
of the system
for di
fferent environ
menta
l
conditio
n
s.
The simul
a
tion circuit
s
for PMSM, inverter
, speed
and cu
rrent controlle
rs in
clud
e all
reali
s
tic com
pone
nts of the drive
syste
m
. These re
sults also
conf
ir
med that the transi
ent torque
and cu
rre
nt never exce
e
d
the
m
a
ximum
pe
rmi
ssi
ble value. M
odelin
g, anal
ysis, te
sting
and
simulatio
n
a
PV array un
d
e
r
different
condition
s
usi
ng MAT
L
AB. Solar radiati
on i
s
a
mate
rial
sou
r
ce of ren
e
wa
ble ene
rg
y and is likely
for model a
main so
urce
of future ene
rgy.
The pe
rform
a
nce of the PV
system i
s
ob
ta
ined un
de
r wide
cha
nge
in PMSM spe
ed with
cha
nge
in
sol
a
r i
rra
diation.
PV syste
m
supplie
d
PMSM d
r
ive fo
r wa
te
r
pu
mp
in
g s
y
s
t
em. T
h
re
e
pha
se Invert
er is controll
ed to supply
PMSM under
variation i
n
sola
r irradi
ation to regu
late
discha
rge
of
water.
PMS
M
is sele
cte
d
to
be th
e
motor fo
r
a
drive EV
owi
ng to
its
different
inherent adv
antage
s. The
speed a
nd torqu
e
trackin
g
by the EV
in variou
s dri
v
ing mode
s are
pre
s
ente
d
.
Referen
ces
[1]
Kumar LS
C,
Padma K.
M
a
tlab/Si
muli
nk
Based
Mod
e
llin
g a
nd S
i
mulati
on
of Re
side
ntial Gri
d
Con
necte
d S
o
lar P
hotov
olt
a
ic Syste
m
. Internatio
na
l Journ
a
l of
E
ngi
neer
in
g
R
e
searc
h
a
n
d
T
e
chnolog
y. E
S
RSA Publ
icati
ons, 201
4, 3(3)
.
[2]
Ma
y
s
s
a
F
,
A
y
men F
,
Lass
a
â
d
S.
Influe
nce
of phot
ovolta
ic
DC b
u
s vo
ltag
e on
the
hig
h
spee
d PMS
M
drive
. IECON 2
012-
38th An
nu
al Co
nfer
enc
e
on IEEE Indust
r
ial El
ectroni
cs
Societ
y
.
IEEE,
201
2: 448
9-
449
4.
[3]
Moussa H, F
a
del M, Ka
naa
n H.
A sin
g
le-
s
tage D
C
-AC
boost top
o
l
ogy
and c
ontrol f
o
r solar P
V
system
s supplying a PMS
M
. Rene
w
a
ble Energies f
o
r Deve
loping Countries (
R
EDEC), 2012
Internatio
na
l C
onfere
n
ce o
n
. IEEE, 2012: 1-7
.
[4]
Kiran Bo
b
y
, Pr
of. Acy
M Kottalil, NP An
ant
hamo
o
rth
y
. Si
mulati
on of PMSM Vector Control S
y
ste
m
w
i
t
h
F
u
zz
y
S
e
lf-Adj
usting P
I
D Control
l
er
Using MAT
L
AB.
Internation
a
l
Journ
a
l of S
c
ientific a
n
d
Rese
arch Pub
l
i
c
ations
. 20
13;
3(3): 1-4.
[5]
Dave
ndra Y
a
d
a
v, Suni
l Ba
ns
al, Mun
e
n
d
ra
Kumar.
Des
i
gn
, Devel
opme
n
t
& Simul
a
tion
of F
u
zz
y
Lo
gic
Contro
ller
to C
ontrol
the
Spe
ed
of Perm
ane
nt
Mag
net S
y
n
c
hron
ous M
o
to
r Drive
S
y
stem
.
Jo
u
r
na
l
of
Electrical and Electron
ics
En
gin
eeri
ng
R
e
se
arch
. 201
1; 3(3
)
: 52-61.
[6]
MT
Benchoui
a, SE Z
ouz
ou, A
Golea,
and
A
Ghamri. Mod
e
l
i
ng
and
Simu
lat
i
on
of Var
i
ab
le
Spee
d Dr
iv
e
S
y
stem
w
i
th
Adaptiv
e F
u
zz
y
Contro
ller
A
pplic
atio
n to
PMSM.
IEEE International
Conference on
Industria
l T
e
ch
nol
ogy (ICIT
)
. 200
4:
683-
687.
[7]
Xi
ao
Xi, L
i
Yo
ngd
on
g, Li Mi
n. Performanc
e
control
of PMSM drives
u
s
ing a s
e
lf-tuni
ng PID.
IE
EE
Internatio
na
l C
onfere
n
ce
. 20
05: 105
3-1
057.
[8]
Francesco
Pa
rasiliti
an
d D
aqi
ng Zh
an
g. Rea
l
-T
ime Gain T
unin
g
of PI Contro
ll
ers for H
i
gh
-
Performanc
e PMSM Drives.
IEEE Transaction on In
dustry Applications
. 2
002; 38(
4): 101
8-10
26.
[9]
Madh
umita Ch
akrab
o
rt
y
.
Co
mparativ
e Anal
ysis
of Spe
ed
Contro
l of PMSM
using PI-C
ontrol
l
er an
d
Fuzz
y
Controller.
Internatio
na
l Journ
a
l of Sci
entific & Engi
n
eeri
ng Res
earc
h
. 2013; 4(
7):1
03-1
08.
[10]
W
e
ihu
a
Li, Z
i
yi
ng Ch
en, W
e
n
p
in
g Ca
o. Optimiza
tio
n
of Per
m
ane
nt Magn
e
t
S
y
nchr
on
ous
Motor Vector
Contro
l S
y
ste
m
Based
on
Particle S
w
a
r
m Optimizatio
n
.
Journ
a
l of
Information &
Co
mp
utation
a
l
Scienc
e
. 201
4: 4687
–4
696.
0
5
10
15
20
25
0
0.
2
0.
4
0.
6
0.
8
1
1.
2
1.
4
Ti
m
e
Sp
e
e
d
S
pee
d
0
5
10
15
20
25
0
0.
2
0.
4
0.
6
0.
8
1
1.
2
1.
4
Ti
m
e
SPEED
S
pee
d P
I
,
F
uz
z
y
_P
I
&
P
S
O
_P
I
C
o
n
t
r
o
l
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 25
02-4
752
IJEECS
Vol.
1, No. 3, March 20
16 : 556 – 565
565
[11]
B Jaga
nath
an,
R Brind
ha, P
a
llav
i
Murth
y
,
Nag
u
la
pati Kir
an,S
w
eth
a
S. ‘
An Onli
ne T
u
n
i
ng of a PMSM
for Improve
d
T
r
ansi
ent Resp
o
n
se Usin
g Z
i
e
g
ler-N
ich
o
l
’
s M
e
thod
’. Proc. of Int. Conf. on Advanc
es in
Electrical & El
e
c
tronics. 201
0: 189-
192.
[12]
Shne
en
SW
, Mao
C. Artifici
al Optim
a
l F
u
z
z
y
Co
ntro
l Str
a
teg
y
for
El
ev
ator Driv
e S
y
s
t
em b
y
Usin
g
Perman
ent Ma
gnet S
y
nc
hron
ous Motor.
T
E
LKOMNIKA Indon
esia
n Jour
nal
of El
ectrica
l
Engi
ne
erin
g
.
201
5; 14(3).
[13]
Atti
y
a
AJ. Co
mpare
d
w
i
th
PI, F
u
zzy_PI & PSO_PI Controll
ers of Ro
botic Grind
i
n
g
F
o
rce Serv
o
Sy
s
t
e
m
.
T
E
LK
OMNIKA Indon
esia
n Journ
a
l o
f
Electrical Eng
i
ne
erin
g
. 201
5; 16(1).
Evaluation Warning : The document was created with Spire.PDF for Python.