A Low Voltage Sensorless Switched Reluctance

8/16/2019 A Low Voltage Sensorless Switched Reluctance

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A Low Voltage Sensorless Switched ReluctanceMotor Drive Using Flux Linkage Method

Thomaq Koblara*, Member, IEEE, Ciprian Sorandaru**, Member, IEEE , Sorin Musuroi**, Member, IEEE ,and Marcus Svoboda**

*Polytechnic University of Tirana, Faculty of Electrical Engineering, TIRANA, ALBANIA**“Politehnica” University of Timisoara, Faculty of Electrical and Power Engineering, TIMISOARA, ROMANIA

E-mail: [email protected], [email protected], [email protected], [email protected]

Abstract -The inherent vulnerability to mechanical failures, extracost, and size associated with external position sensors such asoptical encoders and Hall sensors has motivated manyresearchers to develop sensorless control techniques for SRMdrives. In this paper a flux linkage method and dual layercontroller is developed to estimate rotor position and speed of a

low-voltage Switched Reluctance Motor (SRM) drive. The basicconcept of this application is that of a sensorless speed closedloop with an inner current loop using flux linkage positionestimation. The voltage drop on the power devices is moresignificant in case of low voltage then in case of the high voltagedrive. This voltage drop needs to be considered in the algorithm.Simulations and real-time experimental results given in thispaper shows that the proposed position estimation method canprovide accurate and continuous position information over awide range of speeds, even in low speed applications. To ensure asure operation, a start up algorithm is also included. Theproposed method was implemented and tested by using a digitalsignal processor 56F807EVM from Freescale SemiconductorCompany and an 8/6 switched reluctance motor coupled with abrushless DC motor as load. The software has been developed in

C language.

I. I NTRODUCTION

Switched reluctance motor (SRM) drives are beginning to penetrate the growing market of adjustable-speed motordrives. The SRM drives have been found to be suitable forautomotive applications, household goods, electric vehicles(EVs) and hybrid electric vehicles (HEVs), compressors, etc.Rotor position detection is an integral part of SRM control

because of the nature of reluctance torque production. In fact,excitations of the SRM phases need to be properly

synchronized with the rotor position for effective control ofspeed, torque and torque ripple. A shaft position sensor isusually used to provide the rotor position. By adding discrete

position sensors not only add complexity and cost to thesystem but also tend to reduce the reliability of the drivesystem. Also, there are certain applications, where theambient conditions do not allow the use of external positionsensors and, in these cases on apply a sensorless technique.Several sensorless control methods have been reported in theliterature over the past two decades [1]–[10]. The variousmethods of control without sensors can be classified asfollows: 1) intensive methods based on hardware that requiresexternal circuitry for further signal injection, 2) intensivemethods based on data, such as for example techniques basedon the integration stream, which require large tables for

storing the magnetic characteristics of SRMs, and 3) methods based on models such as for example a method based on stateobservers, the method based on signal strength measurementtechnique based inductance model, methods using neuralnetworks and fuzzy logic, etc., which require a very fast

processor, such as floating point digital signal processors.Ideally, it is desirable to have a sensorless control scheme,which uses only terminal measurements and does not requireadditional hardware or memory resources. Recent advances inthe development of very fast and cost-effective digital signal

processors have opened a new era in the sensorless control ofSRM motor drives.

Recently, there has been a significant interest in SRMdrives for low-voltage applications, such as in automotive,

battery-operated drives and appliances [12].The starting point is an algorithm developed by Freescale

Semiconductor for high-voltage (110-230V) switchedreluctance motor control. It is implemented also a dataacquisition program for flux, current and control signals.

In this paper an intermediate magnetization curve techniqueis used for rotor position estimation [3]-[7]. Experimental testsare carrying out using a Freescale Semiconductor DSP56F807EMV.

II. SENSORLESS TECHNIQUE

The method implemented in this application is based on thecomparison of the estimated flux linkage and the referenceflux linkage in order to define turn-off (commutation)

position. The block diagram of the control scheme is presented in Fig.1.

Fig. 1. Block diagram of the control scheme.

665978-1-4244-7020-4/10/$26.00 '2010 IEEE

2010, 12th International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2010


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When the estimated flux linkage reaches the desiredreference flux linkage it indicates that the commutation

position was reached, Fig.2. The active phase is turned offand the following phase is turned on. Reference flux linkageis obtained from the magnetization characteristic as a functionof phase current for the desired commutation position.

Then the reference flux linkage is obtained from the flux

linkage in the aligned position of the rotor. Flux linkage iscalculated and it is compared with the reference level fromthe reference magnetization curve. When the estimated fluxlinkage is higher than the reference flux linkage, it indicatesthat the switching position has been reached and thecommutation can be performed.

Fig. 2. Angle control method.

Practical implementation of the technique is divided in twoalgorithms, start up and real time running.

The starting of the sensorless controlled switchedreluctance motor is a challenging problem. The same problemmay be encountered when using optical sensors. In fact, asmooth and safe power can be achieved by accuratelyknowing rotor position. This has led researchers to developmethods for determining the initial position of the rotor.Voltage equations, neglecting the saturation and the inducedvoltage, can be expressed as follows:

( )dt

di L RiV j j j θ += (1)

So:

( ) ( ) jT

j j j idt RiV 1

0

−Ψ=−=Ψ ∫ θ (2)Before the motor can be started, rotor alignment and

initialization of the control algorithms must be performed.Initialization of the sensorless control algorithm includes themeasurement of the actual start-up phase resistance. Duringmotor operation, the variation of the resistance can exceed30% of the nominal value because the phase resistancedepends strongly on temperature [8].

R R R ∆+= (3)This variation generates an inaccurate estimation of the

flux linkage; hence it generates position estimation errors.Error of flux linkage is calculated in (4) when currentreaching the zero value.

∫ Ψ+Ψ=−−=Ψt

ton Error Phase Phase Phase Phase Phase dt i Ri Ru )(

∫ ∆−=Ψ=Ψ2

1

t

t F Error Phase dt i R (4)

First, the rotor needs to be aligned to a known position to be able to start the motor in the desired direction of rotation.The method of eliminating the starting hesitation presented inthis paper is based on the initial rotor position estimationrealized in the following steps.

1) Excite two phases for a very short moment (0.5 ms).2) Find the phase having the largest current.

3) Choose a phase next to the phase having the largestcurrent to be the optimal phase for the rotor positionestimation. In theory, the phase either right or left next to the

phase with the largest current can be chosen as the optimal phase. To avoid ambiguity, the right phase is always chosenas the optimal phase in this paper. For example, if phase Ahas the largest current, phase B is chosen to be the optimal

phase.4) Compute the flux linkage for the chosen optimal phase.5) Estimate the initial rotor position from a pre-stored

magnetizing characteristics table based on the current andflux linkage of the chosen optimal phase.

Variation of inductance and current during start up areshown in Fig.3 and Fig. 4.

Fig. 3. Flux variation during start up algorithm.

Fig. 4. Current variation during start up algorithm.

Both time of stabilization and the resistance measurementtake one sec. After this, the rotor is stable enough to reliablystart the motor in the desired direction of rotation. When the

phase resistance has been measured, the motor can be started by commutation of the phases in a desired direction. Signal ofcommutation is represented by duty circle and shown inFig.5.

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Fig. 5 Duty circle operation.

After start up process algorithm enter in a new calculationstage; on line controlling process. As in start up processresistance and flux linkage are measured and compared withreference value. Fig. 6, 7 and 8 shown flux linkage, currentand duty circle during running process.

In running algorithm, the controller proceeds to find theaccurate rotor position based on the flux linkage and currentof the selected optimal phase. The flux linkage integration isimplemented by software.

For accurate flux linkage calculation, it is better tosynchronize with the switching pulses (or the voltageexcitation pulses). For example, if the switching frequency is20 kHz, it is better to select 50 s as the updating rate.However, the rotor position can be still accurately estimateeven if the flux linkage calculation is not synchronized withthe switching pulses because of the robustness of the

proposed algorithms. In our testing, the updating rate is fixedto 100 s. The overall system has satisfactory performancewhen the switching frequency varies between 10–25 kHz.

When the phase is turned on θON , the phase current and the phase voltage are measured simultaneously at the center ofthe PWM pulses. The phase current, i PH is measured directlyusing the phase current sensing circuit with noise eliminationfilter, while phase voltage, u PH , is calculated according to themeasured DC-Bus voltage and the actual PWM duty cycle γ.

Fig.6. Flux variation measured on running process

Fig.7. Current variation during start up algorithm.

Fig.8. Duty circle operation during running process.

Fig.9. Discharge flux produced from discharge current.

When the phase is turned off, respective current starts todecrease -- the phase is discharged i discharge is monitored also.And the flux linkage Ψdischarge continues to be calculatedregularly at the rate of the sampling period (PWM frequency).Fig. 9 shows the discharge flux. As soon as the phase currentapproaches zero, the flux linkage error ΨError is captured. Thisvalue is used to eliminate problems when phase start tocommute again.

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III. SPEED AND CURRENT CONTROL TECHNIQUE

Speed and current are controlled by using a dual layer PIcontroller. Each layer of the controller contains one PIcontroller for speed and one PI controller for current.Structure of the system is shown in Fig. 10. Techniqueconsists in: one outer speed controlling loop and one innercurrent controlling loop.

The speed error is calculated as a difference between actualspeed and reference speed. Output of PI speed loop is thecurrent reference value. The current controller calculates thedifference between actual and desired phase current andcalculates the appropriate PWM duty cycle. The increasingactual phase current is regularly compared with desiredcurrent. As soon as the actual current exceeds the desiredvalue, the current controller is turned on and it controls theoutput of the duty cycle until the phase is turned off.

Fig.10. Speed and current controlling model for one layer.

Fig. 11. Programming diagram with speed condition bloc.

Moving from one layer to another is a function of referencespeed (Fig.11). One is used to control motor in case of lowspeed and the other in case of medium and high speed. Byusing these layers, a very good comportment of the motorduring dynamic regime is obtained. Reference speed is 400

rotations for minute. If reference speed is bigger than 400rpm is used second loop control.

Constants for speed and current PI regulators areimplemented in header part of program are presented below.

PI Contants for the second loop V>400 rpm

/* Current controller parameters */#define SPEED_CONTROLLER_P_SCALE 209

/* proportional scale */

#define SPEED_CONTROLLER_P_GAIN 1200

/*proportional gain */

#define SPEED_CONTROLLER_I_SCALE_12V 8

/* integration scale */

#define SPEED_CONTROLLER_I_GAIN 100

/*integration gain */

#define I_PHASE_MAX 28.5

/*high limit of ph. current */

#define I_PHASE_MIN 5.5

#define CURRENT_CONTROLLER_P_SCALE 26

/* proportional scale */

#define CURRENT_CONTROLLER_P_GAIN 236

/* proportional gain */

#define CURRENT_CONTROLLER_I_SCALE_12V 42

/*integration scale*/

#define CURRENT_CONTROLLER_I_GAIN 26

/*integration gain*/

#define DUTY_CYCLE_MAX 100.0

/* 100 high limit of

#define DUTY_CYCLE_MIN 8.0

/* 8 low limit of duty cycle*/

PI Contants for the second loop V


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IV. EXPERIMENTAL TESTS A ND SIMULATIONS

Experimental tests are carrying out in FreescaleSemiconductor DSP 56F807EMV platform. The block diagramof the experimental setup is presented in Fig. 12.

Fig. 12. The block diagram of the experimental setup.

A general view of the experimental test stand is presented inFig. 13.

Flux linkage SRM is calculated before implementingsensorless technique in Freescale Semiconductor DSP.Measurement tests are carried out using a USB-NI 6009 dataacquisition board and LabVIEW software from NationalInstruments. Implemented software is presented in Fig. 14.

Fig. 13. General view of the experimental test stand.

Specifications of SRM are shown in table 1.

TABLE ISRM SPECIFICATIONS

Nr Type SR40N1 Power ra ting 100 [w]2 Voltage 10 [v]3 Max. Current 28.5 [A]4 Speed 1200 [rpm]5 Number of poles 6/4

6 Phase resistance 0.03 [ohm]7 Inertia 0.82 [kgcm ]8 Duty cycle 15%

Resistance and flux linkage are obtained from thesemeasurements; part of program is shown in fig.16. Afterreceiving data, the building of software for inductancelinearization and implementing it in DSP is possible. Thislinearization is necessary to save memory and to build ageneral algorithm for both speeds. Linearization idea is

brought from flux linkage curves Fig.15, if the PI controllersare able to maintain current in values less than 20A so

preventing the SRM to enter in saturation zone thaninductance will be approximated with linear curves. In orderto avoid look-up tables, only one flux linkage curve and thisis for unaligned position has been considered. The rest ofcurves are performed by multiplying reference curve withangle value.

Algorithm is implemented in C++ language in FreescaleSemiconductor DSP. From the measurement of dc-busvoltage and phase currents, the rotor position is estimated bythe proposed estimation algorithm. The speed controllercompares the command and estimated rotor speeds togenerate the command current, turn-on, and turn-off angles.By combining both angle and current information, the pulse

modulator determines the gate signal for the inverter.

Fig.14. Implemented software for current and voltage data acquisition.

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Fig.15. Flux linkage obtained from calculations.

The algorithms for initial rotor position estimation atdifferent initial rotor positions are first verified and the resultsare given in Fig. 3. The duration of excitation is 0.5 ms.Computer simulation shows the phase which has the largestcurrent (Fig.4). According to the initial rotor positionestimation algorithm, phase C is selected as the optimal

phase.Fig. 6 also confirms two important assumptions of the

algorithm: first, the excitation current will increase linearly sothe flux linkage can be accurately calculated by the simplified(2) and second, the optimal phase has a sufficient diagnosticsignal. For example, the optimal phase current is 2A (Fig.4).

Fig.16. Calculation of the resistance error.

To verify the effectiveness of the estimation and controlalgorithms in a more comprehensive operating condition,following figures presents the operation of the SRM with the

proposed algorithm. By implementing the proposedsensorless technique, in the experimental SRM, it started

from standstill and, at an approximate speed of 350 rpm. TheSRM drive then accelerates to a steady state speed of 700rpm as shown in Fig. 18 and 20. In Fig.17, and 19 are shownthe results from the simulation in the same conditions. Testsare carried out in conditions of fully load.

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Fig. 17. Speed variation al 350rpm simulation results.

Fig. 18. Speed variation al 350rpm experimental results.

Fig.19. Speed variation al 700rpm simulation results.

Fig. 20. Speed variation al 700rpm experimental results.

Fig. 21. Speed variation using first control loop.

Fig. 22. Speed variation using second control loop.

System has represented a very good response duringdynamic load and speed modification. These results areshown in fig.21 and 22.

In fig.16 motor start running fully load and speed of300rpm after 1 s speeds is changed to 400rpm and in 1.3s in300rpm again in time 0.3s speed is up to 400rpm after 1.8sspeed is down to 300rpm and after 2s speed is down to200rpm, when (zone 1) load become zero and motor isrunning without any load. In zone 2 is applied fully load.Second test is carrying out for the second control layer.Speed variation is shown in Fig.22. In fig.22 motor startrunning without load and speed of 600rpm after 3s speeds ischanged to 700rpm and in 4s in 600rpm again in time 1.2sspeed is up to 700rpm after 1.5s speed is down to 500rpmand after 9s speed is up to 600rpm, when (zone 1) motor isfully load. In zone 2 is load becomes zero, and in zone 3motor is loaded again. In general the performance of thesensorless method has been satisfactory in terms of accuracyand a precision of 1.5 [mechanical] was maintained over theentire speed range. Due to dual layer controller, error limitedmeets at low-speeds and at higher speeds. Control of phasecurrent and speed is done in the software and no extracircuitry is used for this purpose. All of the routines aredeveloped in C++ language. The power electronic driver is a

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classic two-switch per phase driver. The speed reversal testwas also performed to verify the practicality of the proposedsystem.

Fig. 23. Speed variation using second control loop.

Fig. 23 shows the speed trajectory in both directions.During this test, SRM drive operating as a motor starts inclockwise direction and, after certain number of revolutions,it would stop for 0.2 ms and then start in the oppositedirection. This test has been performed at 350 rpm torepresent the performance of the system at low-speeds.Waiting time is necessary for the rotor to establish position.The speed information was collected using an external speedsensor on the DC motor load and NI6009 for dataacquisition. The SRM drive system is accommodating thisaction with high reliability and consistency.

V. CONCLUSIONS

In this paper is presented flux linkage method for positionestimation. Its necessary to be mentioned that technique isused for low voltage SRM and the dynamic of the drive isdifferent. Compensation of voltage is necessary during lowspeed operation also the maintaining of current in a limitedvalues is necesary in order to operate with linearised fluxlinkage curve. This method is tested in order to allowrealizing the sensorless speed control within entire speedrange. In our graphics are shown only one part of theseresults from 200rpm up to 800rpm by using two layers forspeed and current as a function of reference speed. Theproposed method demonstrates simplicity in computation

while providing high precision position information with noextra hardware or memory. The proposed algorithms have

been implemented on an experimental SRM test setup. Thesensorless control allows load jumps and variable speeds.

The contribution of this paper is the implementation of asensorless control algorithm on a low-voltage switchedreluctance motor. This research needed to focus mainly tothe current sensing since the proper current measurement isthe key for successful implementation of the sensorless

technique. The voltage drop on the power devices is moresignificant in case of low voltage then in case of the highvoltage drive. This voltage drop will need to be consideredthis in the algorithm.

This research is continuing in developing one algorithmfor dual application (for low and high voltage SRM).

R EFERENCES

[1] R. Krishnan, “ Switched Reluctance Motor Drives “ , CRC Press 1996[2] T.J.E. Miller, “ Switched Reluctance Motors and Their Control” ,

Magna Physics Publishing and Clarendon Press, Oxford 1993[3] L.Xu and J. Bu, “Position Transducerless Control of a Switched

Reluctance Motor Using Minimum Magnetizing Input”, Proc. of 32nd IEEE/IAS Ann. Meeting , 1997, pp. 533-539.

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S.R MacMinn, W.J Rzesos, P.M. Szczesny, and T.M.Jahns,“Application of sensor integration techniques to switched reluctancemotor drives”, Conf. Rec. IEEE Industry Applications Society Annual

Meeting , 1988, pp. 584-588.[5] R. Krishnan, “ Switched Reluctance Motor Drives: Modeling,

Simulation, Analysis, Design, and Applications ”, CRC Press (2001), pp. 351-384

[6] D.A.Torrey and J.H.Lang, “Modeling a nonlinear variable reluctancemotor drive”, IEE Proceedings. , Pt. B, Vo1.137, No.5, pp.314.326,Sept., 1990.

[7] A. Brosse, G. Henneberger, M. Schniederneyer, R. D. Lorenz, and N. Nagel, “Sensorless Control of a SRM at Low Speeds and StandstillBased on Signal Power Measurement,” Proceedings of IEEE-

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[9] C.S. Dragu and R. Belmans, “Sensorless control of switched reluctancemotor”, 15th International conference on electrical machines (ICEM) ,Brugge, Belgium, August 25-28, 2002

[10] Subrata Saha, Kyoe Ochiai, Takashi Kosaka, Nobuyuki Matsui andYoji Takeda “Developing a Sensorless Approach for SwitchedReluctance Motors from a New Analytical Model”, Proceedings ofThirty-Fourth IAS Annual Meeting, Conference Record of the 1999

IEEE , vol. 1, pp. 525-532.[11] R. Visinka “3-Phase Switched Reluctance (SR) Sensorless Motor

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[12] M. Ehsani, I. Husain, K. R. Ramani, and James H. Galloway, “Dual-Decay Converter for Switched Reluctance Motor Drives in Low-Voltage Applications”, IEEE Trans. On Power Electronics vol. 8, no.2, April 1993.

[13] Freescale, “ Apparatus and Method for Estimating the Coil Resistancein an Electric Motor" , Chalupa, L.,Visinka, R., US Patent, 6,366,865 ,2002-04-02

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A Low Voltage Sensorless Switched Reluctance