67.energy optimization of field oriented (1)

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 1Iran University Science and Technology, Electrical Engineering Department, Narmak, Tehran, Iran

2Sharif University of Technology, Electrical Engineering Department, Azadi, Tehran, Iran [email protected]

1Abstract—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

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].

1This work was supported in part by the Power Electronic Lab of IUST

& Power Electronics and Drive Systems Lab of Sharif Univ.

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 six-phase 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

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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 six-phase 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 subspace. Harmonics of the order 6n±1 (n=1,

3, 5 …) are mapped to subspace and produces losses

and then they must be reduced to improve efficiency. This model can be described by the following set of

differential equations:

(d q

( )d q

)

1 2-z z

,, , ,

,, ,

0 (

d qsd qs s d qs e d qs

d qrr d qr e r d qs

dv R i j

dtd

R i jdt

)

,

,

(1)

, ,

, ,

d qs s d qs d qr

d qr r d qr d qs

L i Mi

L i Mi

(2)

Where:

P , , , 3

2r m s ls R lR msL L M L L M M L (3)

1, 21, 2 1, 2 z z

z z s z z ls

div R i L

dt (4)

The control algorithm and supplying voltage must minimize

the current harmonics generated in the subspace.

Electromechanical energy conversion is as the following:

1 2-z z

3

2e dr qs qr ds

r

PT

Mi i

L (5)

lr

e

JT T

P

d F

dt P

r (6)

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 q-axis, resulting in

0qr (7)

Thus

3

2e dr qs

r

PT

Mi

L (8)

dr r rdr ds

r r

d R R Mi

dt L L

(9)

. qsrs

r d

iR M

L

r (10)

III. MOTOR LOSSES DETERMINATION AND ADAPTIVE FLUX

SEARCH CONTROL.

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:

2 2

s sd sq (11)

2

core s core sP P k 2 (12)

Stator and rotor copper losses are given by:

2 2 2 21 1 2 2

s rcu cu cu

s sd sq r rd rq

P P P

R I I R I I

(13)

Copper loss in d q subspace are determined by the

corresponding resistances and currents

2 2 21 2 1 2

1 1

2 2z s sz sz s rz rzP R I I R I I 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:

*out e rmP T (15)

After calculating the input power the efficiency is as:

out out

in out loss

P P

P P P

(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

108



109

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:

( 1) ( )s 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 ( ( ) )s inf P . To increase convergence time in

the search algorithm is selected as:

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 2-c, 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.

1

1 2

2

1) ( ( 1) ( )) then :No change

2) [ ( ( 1) ( )) ] then

3) ( ( 1) ( )) then( 1) ( )

in in

in in

in in

s s

if P P P

if P P P Pm

if P P P

(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 , 2P is changed tom . This selection prevents

from efficiency oscillation and increases convergence time.

,

)(T

e

m

*m

m qsI

*qsI

*dsI

dsIi

idsIqsI

qsI

dsI

rm

m

s

ss

*s

*U

*U

aI

bI xI

yIe e

qsI

dsI

sm

*dU

*qU

Figure 1. Diagram of adaptive flux SC of FOC of six-phase induction motor



(a) (b)

(c) (d)

(e) (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



111

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



Figure 6. Experimental setup of inverters and DSP board

TABLE I. MOTOR PARAMETERS

Parameter Value

Number of poles 4

Mutual inductance 196 mH

Stator resistance 15

Stator leakage inductance 15.1 mH

Rotor resistance 7.91

Rotor leakage inductance 16.3 mH

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.

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