The document describes an adaptive flux search control technique to optimize the efficiency of a field oriented controlled six-phase induction motor drive. The technique adaptively changes the flux variation steps to decrease the convergence time of the search control algorithm. Simulation and experimental results show that when the proposed algorithm is run, it changes the stator flux amplitude to an optimal lower value than nominal to reduce input power without changing output power or torque, thereby improving efficiency. The adaptive flux search control is found to be faster than conventional search control techniques and effectively optimizes the efficiency of the six-phase induction motor drive.

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Advances in Electrical and Computer Engineering
Volume 11, Number 2, 2011
Energy Optimization of Field Oriented
Six-Phase Induction Motor Drive
Asgar TAHERI1, AbdolReza RAHMATI1, Shahriyar KABOLI2
1
Iran University Science and Technology, Electrical Engineering Department, Narmak, Tehran, Iran
2
Sharif University of Technology, Electrical Engineering Department, Azadi, Tehran, Iran
ataheri@iust.ac.ir
1
Abstract—This paper deals with the efficiency optimization
of Field Oriented Control (FOC) of a six–Phase Induction
Motor (6PIM) by adaptive flux search control. The six-phase
induction motor is supplied by Space Vector PWM (SVPWM)
and voltage source inverter. Adaptive flux search controller is
fast than ordinary search control technique and easy to
implement. Adaptive flux Search Control (SC) technique
decreases the convergence time by proper change of flux
variation steps and increases accuracy of the SC technique. A
proper loss model of 6PIM in conjunction with the proposed
method is used. The six-phase induction machine has large zero
sequence harmonic currents that can be reduced by SVPWM
technique. Simulation and experimental results are carried out
and they verify the effectiveness of the proposed approach.
Index Terms—efficiency optimization, field oriented control,
flux search control, motor control, six phase induction motor.
I. INTRODUCTION
Recently, multiphase machines have been received great
deal of attention. Space-harmonic in a multiphase machine
is less than three-phase machine. Also, the redundancy in a
multiphase machine is greater than three-phase one. When
one or more phases are lost, Multiphase machine continue to
run [1-4]. Applications of multiphase induction motor drives
lie in the field of high power and current systems with great
reliability such as marine propulsion, railway traction,
electrical vehicles, and aerospace applications [1], [4], [5].
One of the most interesting multiphase machines in past
applications is the six–phase induction. This machine is
known in the literatures as dual stator winding induction
machine (DSWIM), six-phase induction machine (6PIM),
and others. Until recent years, the literatures of six-phase
induction machine drives in the FOC and voltage source
inverter covered these issues: Field oriented control of this
machine in [5], [6], Indirect field oriented control [9],
suitable SVPWM techniques [7], [10]-[12], efficiency
optimization of six-phase induction machine [13]. Scalar
and Venturini strategy on six phase matrix converter fed
DSWIM is presented in [14]. The scalar strategy has a
balanced system, while Venturini strategy has an
unbalanced system with direct impact on the double star
induction motor.
Efficiency improvement of electro motor can be executed
by change in motor structure, inverter, or control system. To
obtain better performance and increase efficiency of motor,
a two-dimensional rotor pole shape optimization method for
1
This work was supported in part by the Power Electronic Lab of IUST
& Power Electronics and Drive Systems Lab of Sharif Univ.
a permanent magnet motor to reduce cogging torque in
Interior Permanent Magnet (IPM) motors is introduced in
[15]. A boost converter is used in [16] to improve the power
factor and individual current harmonics of SRM with
different load currents. A precise power control with
minimum distortion and harmonic noises in four-switch
three-phase power converters is presented in [17]. Also, flux
search control technique to efficiency improvement of
induction motor is presented in [18]-[21].
Efficiency improvement of six-phase induction machine
is important. If the six-phase induction motor works in a
speed or torque under its nominal point, the motor efficiency
becomes less than nominal. In FOC of the six-phase
machine, if the stator flux is set to the rated field flux in the
whole range of load, the efficiency in the light load is
decreased [13]. The flux Search Controller (SC) technique
to Efficiency optimization of FOC of a six–phase induction
motor is presented in [13]. Stator flux is reduced step by
step and measured input power is related to that. The long
time to reaching optimal input power is defect of proposed
algorithm in [13].
The proposed technique is based on flux controlling to
achieve maximum efficiency at light load or low speed.
Space vector modulation in induction machine is used to
generate harmonically optimum waves at the output. The
PWM switching frequency is quantitatively limited, since
the Space Vector Modulation (SVM) technique is complex
and computationally intensive [10]-[12]. For reaching to real
minimum input power, we use input power as objective
function. By choosing that as a minimum point, the machine
efficiency is maximized. To decrease the convergence time
of the algorithm, the flux reference is obtained
automatically. Minimum flux change must be chosen based
on the noise level coming from disturbance in the FOC loop.
Maximum flux step is limited by instability of the system.
The presented study consists of these sections. In the next
section, machine modeling and analysis in VSD (Vector
Space Decomposition) method, FOC, and SVPWM of sixphase induction machine is described. Then, in section III
proposed losses, input power, output power, and efficiency
modeling in 6PIM and adaptive flux search controller are
presented. Simulations and experimental results are shown
in section IV. These results show the preference of proposed
algorithm.
II. SIX-PHASE INDUCTION MOTOR MODELING AND FOC
DRIVE.
The Six-phase induction machine considered in this paper
Digital Object Identifier 10.4316/AECE.2011.02017
107
1582-7445 © 2011 AECE

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Advances in Electrical and Computer Engineering
Volume 11, Number 2, 2011
consists of a stator with two separate windings shifted by 30
electrical degrees and a double neutral point, having similar
pole numbers and parameters. The popular method in sixphase induction machine modeling is VSD (Vector Space
Decomposition) [5], [7]. In VSD method, the machine
modeling is achieved in three two-dimensional orthogonal
subspaces [9]. Fundamental harmonic of the machine and
the harmonics of the order 12n±1 (n=1, 2, 3 …) are mapped
to the (d  q) subspace. Harmonics of the order 6n±1 (n=1,
3, 5 …) are mapped to  z1 - z2  subspace and produces losses
and then they must be reduced to improve efficiency.
This (d  q) model can be described by the following set of
differential equations:
d d , qs

 je d , qs
vd , qs  Rs id , qs 

dt

 0  R i  d d , qr  j (   )
r d ,q r
e
r
d , qs

dt

(1)
m ,Ls  Lls  M , LR  LlR  M , M  3Lms
vz1, z 2  Rs iz1, z 2  Lls
2 Lr
Te  Tl 

diz1, z 2
dt
i   qr ids 
dr qs
J d r
P dt

F
r
P
 qr  0
2 Lr
(5)

Pcore  k  s
2
(12)
(13)
 dr iqs
1
1
Rs  I sz12  I sz 2 2   Rs  I rz12  I rz 2 2 
2
2
(14)
As see from (11), core loss is dependent on motor flux. If
motor has a load or speed under its nominal point and flux
reference is in nominal point, system efficiency is low. For
increasing 6PIM efficiency, motor flux is reduced until the
input power reduced and output power is not changed. In
this study, the motor output power is kept constant because
its output torque and speed is constant. Output power is
expressed as:
Pout  Te * rm
(15)
(6)
After calculating the input power the efficiency is as:
(7)
d dr Rr
RM
  dr  r ids
dt
Lr
Lr
108
2
(11)
1
1
Rs  I sd 2  I sq 2   Rr  I rd 2  I rq 2 
2
2
(4)
Thus
3P M
Pcore   s
Pz 
While F is the coefficient of friction, J is the motor inertia
constant, and P is number of pole pairs. To achieve the field
oriented control with the rotor flux orientation, the phase of
reference system such that the rotor flux is entirely in the qaxis, resulting in
Te 
2
2
 s   sd   sq
corresponding resistances and currents
Electromechanical energy conversion is as the following:
3P M
The main electrical losses in motor are consist of core and
copper losses. This kind of loss can be improved by suitable
optimization algorithm. The core losses consist of hysteresis
and eddy current losses. This loss is proportional to square
magnitude of flux as:
(3)
The control algorithm and supplying voltage must minimize
the current harmonics generated in the  z1 - z2  subspace.
Te 
III. MOTOR LOSSES DETERMINATION AND ADAPTIVE FLUX
SEARCH CONTROL.
Copper loss in  d  q  subspace are determined by the
Where:
2
(10)
Stator and rotor copper losses are given by:
(2)
 d , qr  Lr id , qr  Mid , qs
P
Rr M iqs
.
Lr  dr
Pcu  Pcus  Pcur 
 d , qs  Ls id , qs  Mid , qr
r 
s 
(8)
(9)

Pout
Pout

Pin
Pout  Ploss
(16)
The main advantage of search control is that it does not
depend on motor or converter parameters as other efficiency
control strategies do, and it leads to the true optimal
efficiency. An obvious disadvantage is its slow speed [19].
The Search control techniques iteratively adjusted the
machine flux reference. According to (12), the loss power is
relative to machine flux thus for a given load and speed, the
stator flux is varied to optimal point with fixed load and
speed. The search control algorithm converge time is large.
If the step changes of the fluxes are invalid toque pulsation
is produced. The search process cannot reach a steady state,
if the input power is little near the optimum operating point

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Advances in Electrical and Computer Engineering
Volume 11, Number 2, 2011
and flux step is large. Also, the minimum flux step has large
convergence time. The motor flux in each step can be
calculated by considering the flux of previous step as
following:
 s (  1)   s ()   t
(17)
For minimizing the convergence time, we begin with
maximum amount of flux change (less than instability
level),
and
check
the
value
of
objective
function ( f (  s )  Pin ) . To increase convergence time in
the search algorithm  is selected as:


1) if ( Pin (  1)  Pin ())  P then  : No change
1



 2) if [ P  ( Pin (  1)  Pin ())  P2 ] then  
1
m


   

3) if ( Pin (  1)  Pin ())  P2 then 

  s (  1)   s ()


(18)
By changing  s () , if flux approaches the optimal
value, objective function changes very slowly. If the
difference between two objective functions is greater

than P2 ,  is changed to  . This selection prevents
m
from efficiency oscillation and increases convergence time.
*
m
IV. SIMULATION AND EXPERIMENTAL RESULTS
In order to validate the efficiency of the proposed method,
a computer simulation model as fig. 1 is implemented using
Matlab. Simulation results of efficiency optimization of
FOC 6PIM is shown in fig. 2. In these simulations, motor
speed reference is 75 rad/sec. The proposed algorithm is run
at 10th second. After running optimized algorithm, input
power is changed to the optimal value. The efficiency, input
power, flux variations, Id, Iq, copper loss, and core loss
variation in proposed algorithm are shown in fig. 2 and
fig. 3. Flux variation steps are large in beginning and then
reduce by an adaptive algorithm. According to fig 2-b and 2c, when proposed algorithm is run, Id or stator flux
amplitude is changed to optimal value that is less than the
nominal value. The proposed technique for the efficiency
optimization has been developed and tested in the
experimental set up. Experimental results are shown in
fig. 4 and fig. 5. The test-bed is composed of a six-phase
induction motor. This machine is fed by a six-phase DC-AC
VSI. 6PIM is coupled to a DC machine that operates as a
generator. It acts as an adjustable load for the 6PIM. Two
three-phase inverters are designed and used to drive of the
motor. Experimental results of efficiency optimization of
FOC 6PIM are shown in fig. 5. Motor speed is fixed in 75
rad/sec. Then the proposed algorithm is run after 14 second.
Simulation and experimental results have almost similar
responses.
*
Ud
U*

I*
qs
m
Iqs
 ,
*
s
U
U*

*
q
I*
ds
s
e
s

e
I ds
m
Iqs
I ds
e
rm
I ds
Iqs
s
m
i
T()
i
Ia
Ib
Ix
Iy
s
I ds
Iqs
m
Figure 1. Diagram of adaptive flux SC of FOC of six-phase induction motor
109

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Advances in Electrical and Computer Engineering
(a)
Volume 11, Number 2, 2011
(b)
(c)
(e)
(d)
(f)
Figure 2. Simulation results in SC of FOC 6PIM a) mean of efficiency, b) stator flux amplitude, c) change of Id, d) change of Iq, e) input power, f)
averaging of core loss
110

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Advances in Electrical and Computer Engineering
Volume 11, Number 2, 2011
Figure 3. Simulation results of averaging of copper loss in SC FOC of 6PIM
(a)
(b)
Figure 4. Experimental results of a) efficiency, b) torque mean in adaptive SC rule of SVPWM FOC 6PIM
(a)
(b)
Figure 5. Experimental results in the adaptive SC of SVPWM FOC 6PIM a) Id, and b) Iq
111

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Advances in Electrical and Computer Engineering
Volume 11, Number 2, 2011
[3]
[4]
[5]
[6]
[7]
Figure 6. Experimental setup of inverters and DSP board
[8]
TABLE I. MOTOR PARAMETERS
Parameter
Value
Number of poles
4
[9]
[10]
Mutual inductance
196 mH
Stator resistance
15
Stator leakage inductance
15.1 mH
Rotor resistance
7.91
Rotor leakage inductance
16.3 mH
[11]
[12]
[13]
[14]
V. CONCLUSION
An efficiency optimization method for a six phase
induction motor which is robust against motor parameter
variations and does not require any extra hardware is
presented in this paper. This method is based on the adaptive
variation of flux in FOC of 6PIM to loss minimization.
Simulation and experimental results given in the paper
verify the effectiveness of the proposed method. Adaptive
flux SC technique decreases the convergence time and
increases accuracy of the algorithm. Flux step is large in
beginning and then changed to fewer amounts. If input
power approaches the optimal value, objective function
changes very slowly. Simulation and experimental results
have almost similar responses in adaptive flux Sc of 6PIM.
[15]
[16]
[17]
[18]
[19]
[20]
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