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CHAPTER-5

DESIGN OF DIRECT TORQUE CONTROLLED

INDUCTION MOTOR DRIVE

5.1 INTRODUCTION

This chapter describes hardware design and implementation of

direct torque controlled induction motor drive with high-speed digital signal

processor. Implementation of different control algorithms necessarily requires

hardware design and implementation. In this research, prototype hardware is

developed with digital signal processor to verify the control strategies. In

order to validate the developed drive, the experimental results are obtained for

different direct torque control strategies including the proposed strategy.

5.2 EXPERIMENTAL DRIVE SYSTEM

The experimental drive system for a 3-phase squirrel cage induction

motor comprises of three subsystems.

1. Power circuit

2. Control circuit

3. Sensing circuitry and signal conditioning circuitry

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In this chapter, these three subsystems are explained with the block

diagram and hardware of experimental setup in detail. Block diagram of

experimental setup is shown in Figure 5.1.

5.2.1 Power circuit

Power circuit consists of an uncontrolled rectifier, filter and IGBT

based voltage source inverter. These are explained in the following sections in

detail.

5.2.1.1 Voltage Source Inverter

Voltage source inverter takes a DC bus voltage and uses six

switches arranged in three phase legs as shown in Figure 5.2. From the middle

of each phase leg comes from the line, which connects the stator of the motor.

The voltage on these lines must be a balanced three-phase sinusoidal

waveform in order to drive the induction motor. This is achieved by a

controlled switching to the gate of the IGBTs.

Drive Circuit

TMS320LF2407

Signal Conditioning

circuit

Induction

Motor

Voltage Source

Inverter

Bridge

Rectifier Filter

Voltage and Current Sensing Circuit

Figure 5.1 Block diagram of Experimental Set-up

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VDC

1MBH10D-060

1MBH10D-060 1MBH10D-060

1MBH10D-060

1MBH10D-060

1MBH10D-060

Phase A Phase C

Phase B

Figure 5.2 Power circuit diagram of Voltage source inverter

5.2.1.2 Selection of Power components Nowadays, Power Components used in industrial motor drives are

MOSFETs and IGBTs. IGBT switches are used in inverters for drives

applications. They are replacing MOSFETs in many high voltage, hard

switching applications since they have lower conduction and switching losses

for the same output power. They are lower cost devices and have smaller

input capacitance. Most IGBT modules are used in hard switching

applications of up to 20 kHz beyond which, switching losses become very

significant. Since MOSFETs have these drawbacks, in this experimental

work, IGBTs are preferred in inverter circuit.

Table 5.1 shows the switching performance and characteristics of

IGBT used in power inverter circuit. Turn-on time of IGBT used in inverter is

1.2 microseconds and rise time is 0.6 microseconds. The typical fall time is

200 nano seconds.

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Table 5.1 Characteristics of IGBT

Characteristics Values

Device IGBT

Type 1MBH10D-060

Current Rating 10A

Voltage Rating 600V

RON at Tj=25 C 0.23

RON at Tj=150 C 0.22

Fall Time (typical) 200 nsecs

Drive Type Voltage

Drive Power Minimum

Drive Complexity Simple

Current Density for a given voltage Drop High

Switching Losses Low

5.2.1.3 Snubber circuits

Snubbers are needed to protect the switches (IGBT) against over

voltage transients resulting from current changing due to the parasitic

inductance. In addition to providing protection from over voltage, snubbers

can be employed to:

° Limit dtdi (or) dt

dv

° Shape the load line to keep it within the safe operating area

(SOA)

° Transfer of power dissipation from the switch to a resistor

° Reduce total switching losses

° Reduce voltage and current ripples

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Figure 5.3 RCD snubber circuit

RCD snubbers are used in this research work to protect the IGBT

inverter as shown in Figure 5.3. Direct mount snubbers are used in which

hyper fast, soft recovery diode MUR3060 was prefered. Direct mount types

have lower inductance due to flat, radial lead geometry. They are installed by

using their own screws. Direct mount capacitors are rated for higher current

because heavy copper lugs are connected directly to the capacitor element.

SCD polypropylene double metalized capacitance is used in this snubber

circuit.

5.2.1.4 Selection of heat sink

The selection of a heat sink is constrained by many factors

including set space, actual operating power dissipation, heat-sink cost, flow

condition around a heat sink and assembly location.

Table 5.2 provides a comparison of the percentage transfer

efficiency of the different type of heat sinks for natural airflow conditions. It

is understood from the table that Ducted Pin Fin, Boded &Folded Fins have

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got higher percentage transfer efficiency. In this research, Ducted Pin Fin heat

sink was used.

Table 5.2 Heat Sink Type vs. Percentage Transfer Efficiency

Heat Sink Type %Transfer Efficiency

Stampings &Flat Plates 10-18

Fined Extrusions 15-22

Impingement Flow Fan Heat Sinks 25-32

Fully Ducted Extrusions 45-58

Ducted Pin Fin, Bonded &Folded Fins 78-90

All the above points are taken into consideration while designing

the power inverter

5.2.2 Control Circuits

The DTC control algorithm is performed by utilizing a DSP

controller board eZdspF2407. The optimal switching patterns, which are

selected based on the flux and torque status, are stored in a look-up table.

5.2.2.1 Digital signal processor (TMS320LF2407)

The eZdspF2407 board is available from Texas instruments as a

development tool, is shown in Figure 5.4. It is one of the processors that

execute most of the programs of the control algorithm. The DSP kit provides

a complete development environment, and includes the DSP board, power

supply, on-board JTAG compliant emulator and specific version of the Code

Composer Studio Integrated development Environment. The DSP board itself

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has nearly all peripheral signals available on the board headers, making it

easy to interface the board with other system hardware.

Figure 5.4 TMS320LF2407 eZdsp DSP Starter Kit Some of the hardware features are given below:

Clock frequency is 40 MHz and 30 MIPS

Communication interface for SCI (serial communication

interface) and CAN (control area network) controller

10 bit ADC with twin auto sequencer

Serial components connected to SPI (serial peripheral

interface) such as serial DAC, serial EEPROM and serial

LED driver

Serial communication interface module

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16 analog channel and two event manager for PWM

generation (both asymmetrical and symmetrical PWMs)

Voltage and Current sensors that interface to the capture and

quadrature encoded- pulse (QEP) decoding logic.

Capture pins to capture the logical signals.

External Memory interface

5.2.2.2 Gate driver and opto-isolator:

The gate drivers are used to get the signal pulses from the control

board and amplify them to the level required for switching the IGBTs and

opto isolators are used to isolate the power and control circuits.

5.2.3 Sensing and signal conditioning circuitry

Hall Effect sensors are used as voltage and current sensors. Two

voltage sensors and two current sensors are used. Apart from the sensing

circuitry, signal-conditioning circuits are also designed to meet the

requirements of DSP.

5.2.3.1 Voltage sensing circuit

Hall Effect voltage sensors are widely used to measure the phase

voltages (LV-25-P-29). The voltage obtained from the voltage sensor is

bipolar voltage. But, DSP can accept only unipolar voltage. In order to get the

unipolar voltage, a unipolar converter circuit is designed and implemented.

Once the processor receives the input, the received value is scaled in the

control algorithm.

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5.2.3.2 Current Sensing Circuit

Current sensors are used to obtain the proper information of current

from the phase windings of induction motor. In this research work, two closed

loop current sensors of PC board mount type LA-100P are used. The output of

the current sensor for the given input current is (0-3) V.

5.2.3.3 Isolation circuits

If the voltage and current sensor output are within the 0 to 3V input

range of the ADC, with no significant noise, and meets the source impedance

requirement of the ADC then a direct connection between the sensor output

and ADC input is possible. But in most cases, operational amplifier is used to

meet the input impedance requirement of the ADC and also used as isolation

between sensor and processor control unit.

5.3 EXPERIMENTAL PROCEDURES FOR DIFFERENT DTC

STRATEGIES

PC DSP

RAM

10-bit ADC

Device Firing

Data and address bus

Phase Voltages

Phase Currents

IM

+ -

Inverter

Figure 5.5 DSP based control system for DTC

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The overall DTC layout is implemented on the 40 MHz TI

TMS320LF2407 DSP based drive system as shown in Figure 5.5. This DSP

kit provides a complete development environment and includes the main DSP

board, power supply for the board, on – board JTAG compliant emulator and

an eZdsp specific version of the code composer studio. The algorithm was

programmed in C. An efficient C code optimizer is employed during

compilation. 150 kHz bandwidth Hall-effect sensors are used to measure the

phase currents. It is sufficient to measure two phase currents only. The eZdsp

captured the resultant signals with 10 bit flash analog to digital converters

(ADC) at the start of every interrupt. The ADCs are triggered in hardware by

the DSP internal interrupt clock. The phase voltages are also measured by

using Hall Effect voltage sensors. The DSP communicates with an input card

and fires the inverter devices.

(i) Control Update Period: The time interval between

successive calculations and generation of new voltage vector

demands. Control update period in this research work is

50μsec.

(ii) Inverter switching period: The minimum time interval

between changes in the output voltage vector state. A change

in the output voltage vector can result from any one signal

leg switching, so it is possible for the inverter switching

period to be less than the leg switching period. Inverter

switching period is also 50μsec.

(iii) Device switching period: The minimum time interval

during which an individual power device in the six device

bridge is allowed to switch ON and OFF. Because the bridge

consists of three legs, each containing two devices switched

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in opposition, the device switching period is equal to the leg

switching period and the value is 100μsec.

In this research work, the control update and inverter switching

periods are identical and equal to half the device switching period. The

various time periods are limited either by the switching devices or by the

processor.

Figure 5.6 shows the photographical view of complete experimental

set-up in which, power inverter, induction machine coupled with a d.c.

generator, DSP, oscilloscope and personal computer are shown. D.C.

generator with resistive load is also shown in this Figure.

The experimental control system is made and done in the following

modes:

(i) Torque and Flux loops without speed encoder (Speed

Estimation)

(ii) Torque, Flux and speed loops (Online speed measurement)

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Figure 5.6 Photographical view of Experimental Set-up

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5.3.1 Conventional DTC

The motor phase currents and voltages are measured and the

measured information is given to the motor model. Initial information of the

motor is given to the model based on the principle of auto tuning. Using speed

encoder the parameters of the motor model are determined. The output signals

from the motor model represent the torque and stator flux directly and the

speed is also estimated directly from the motor model. Power IGBTs are

controlled by the information from the torque and flux comparators. Actual

torque and flux values are compared with the respective reference values in

the comparators for every 100 micro seconds. Then the torque and flux error

signals are fed to the optimum switching state selector which is called as

pulse selector. DSP TMS320LF207 is within the state selector to determine

the switching state of the inverter. For every 100 microseconds, IGBTs are

supplied pulses for maintaining the torque and flux in the required levels. To

get the desired results torque and flux controller must be designed accurately.

The torque controller gain was chosen a high value to achieve fast torque

response. The control systems parameters chosen for this research are given

below:

Proportional Gain for flux comparator = KP = 100

Integral Gain for flux comparator = KI = 300

Proportional Gain for torque comparator = KPT = 2

Integral Gain for torque comparator = KIT = 200

The feedback rate of the speed loop = 1 kHz.

The speed loop bandwidth = [0, 32 Hz].

All graphics of the present chapter correspond to the experimental

results obtained from a three phase induction motor. In all cases, the reference

values are

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Torque reference value = 1.0Nm.

Torque hysteresis value = 0.27Nm.

Flux reference value = 0.8Wb.

Flux hysteresis value =0.035Wb.

Sample time = Ts = 100μs.

The DSP has been programmed using C language and assembler.

All the tasks, whose execution time is critical, have been programmed in

assembler. To implement the system, the first step taken for consideration is

the delay due to non-ideal behavior of the whole system. The most significant

delay is introduced by the ADC and the control algorithm. The control

algorithms implemented in this research have three routines. They are given

as follows:

(i) ADC processing with all the data

(ii) Estimation of torque and flux values

(iii) Implementation of DTC

The time taken to execute these routines is about 40 μs. The

sampling frequency of ADC is set to 10 kHz and therefore the sampling time

is 100 μs. Totally, there is a delay of 140 μs to send the new VSI state from

the sampled data. At every sampling time the voltage vector selection block

chooses the inverter switching state that reduces the instantaneous flux and

torque errors.

Figure 5.7 shows the torque developed at 1000 rpm, using

conventional DTC strategy. From this figure it is observed that the ripple

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content are more and it is matched with the simulation results as shown in

Chapter-3. As described in previous chapters the reference or command

torque value is taken as 1 Nm and the steady state torque is running around

the command torque in narrow band manner. Command torque is given from

the torque reference controller which includes speed control loop also. The

torque reference output is taken from this controller through Digital to Analog

converters and given to the torque comparator. Figure 5.8 shows the stator

flux linkage in stationary reference frame using conventional DTC strategy.

Figures 5.9 and 5.10 show the stator flux linkages in d-axis and q- axis at

stator with respect to stationary reference frames respectively. The flux

linkages and torque have been estimated within the sampling period of 100μs.

This is accomplished by sensing the stator current at the same sampling

period. Figure 5.11 shows the output voltage across the PWM inverter

terminals looking through attenuation probe. Figure 5.12 shows the position

of stator flux in angle. Figures 5.13 and Figure 5.14 show the direct and

quadrature currents and voltages respectively.

Figure 5.7 Torque developed in induction motor using conventional

DTC controller at 1000 r.p.m.

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Figure 5.8 Stator flux in conventional DTC scheme

Figure 5.9 Stator flux in stationary reference frame in DTC scheme

(d-axis)

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Figure 5.10 Stator flux in stationary reference frame in DTC scheme

(q-axis)

Figure 5.11 Output phase voltage across the inverter terminals through

attenuation probe

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Figure 5.12 Position of stator flux (angle) shown on computer screen

Figure 5.13 Direct and Quadrature axes currents

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Figure 5.14 Direct and Quadrature axes Voltages

5.3.2 Intelligent DTC

The following Intelligent control algorithms are implemented

experimentally:

(i) Fuzzy based Direct Torque control

(ii) Neural Network based Direct Torque control

(iii) Neuro Fuzzy based Direct Torque control

(iv) Genetic algorithm based Direct Torque Control

To implement intelligent control algorithms, the timing should be

as precise as possible. The sampling time taken to execute intelligent control

is also 100 μs. However, execution of intelligent direct torque control

algorithm differs from classical DTC algorithm due to the intelligent control

blocks presented such as Fuzzy, training of Neural Networks along with the

DTC algorithm. In this algorithm, once the active state is sent then only the

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Fuzzy logic or neural network algorithm starts being executed. Because, some

part of this controller is executed in personal computer itself and the processor

waits until execution is finished to obtain the duty cycle. Once the duty cycle

is obtained, the timer is programmed.

The time taken for this process is 100 μs. The duty cycle, which

needs to change the active state before this time is ignored, and it is not taken

for consideration. The total time taken to execute classical and intelligent

control algorithms is as given in Table 5.2. Figure 5.15 (a) shows the pie chart

for time taken for implementation of classical DTC, Figure 5.15 (b) shows the

pie chart for time taken for implementation of Intelligent controlled DTC. It is

noted that the time taken for all the intelligent control techniques is limited to

be nearing the same. Figure 5.15 (c) shows the pie chart for time taken for

implementation of proposed DTC strategy.

Table 5.2 Timing for Various DTC strategies

Sl.No. Control Algorithms

Sample Time (μs)

Delay Time for software

Execution (μs)

ADC interruption

Time (μs) 1. Classical DTC 100 40 200 2. Direct Torque

Fuzzy logic Control

100

80

200 3. Direct Torque

Neural Network Control

100 80 200

4. Direct Torque Neuro Fuzzy Control

100 80 200

5. Genetic algorithm based direct torque control

100 80 200

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100us

40us200us

Delay Time

Sample TimeADC interruption Time

Figure 5.15 (a) Time taken for classical DTC

200us

80us

100usSample Time

Delay TimeADC Interruption Time

Figure 5.15 (b) Time Taken for DTC using intelligent control techniques

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60us

200us

100us

Sampling Time

Delay time

ADC interruption Time

Figure 5.15 (c) Time Taken for proposed DTC strategy

Figure 5.16 shows the torque developed using neural network based

direct torque controller. Figure 5.17 shows the torque developed using Fuzzy

based direct torque controller. Figure 5.18 shows the torque developed using

adaptive neuro fuzzy based direct torque controller in the induction motor at

1000 rpm. Figure 5.19 shows the torque developed using genetic algorithm

based direct torque controller at 1000 rpm in which neural networks are

trained using genetic algorithm with binary coding representation.

Figure 5.20 shows the torque developed using genetic algorithm based direct

torque controller at 1000 rpm in which neural networks are trained using

genetic algorithm with floating point coding representation.

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Figure 5.16 Torque developed in induction motor using neural network

based direct torque control at 1000 r.p.m.

Figure 5.17 Torque developed in induction motor using Fuzzy logic

based direct torque control at 1000 r.p.m.

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Figure 5.18 Torque developed in induction motor using adaptive neuro

fuzzy based direct torque control at 1000 r.p.m.

Figure 5.19 Torque developed using Direct Torque Neuro controller

trained by genetic algorithm (Binary coding representation)

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Figure 5.20 Torque developed using Direct Torque Neuro controller

trained by genetic algorithm (Floating Point representation)

5.3.3 Proposed DTC

The proposed DTC strategy is implemented through the same

hardware setup, which has been used for other control strategies. As discussed

in chapter-4, in the experimental implementation also, the following steps are

involved:

(i) Calculation of torque and flux increments

(ii) Calculation of stator flux adjacent angle

(iii) Control of angle Δθ to define the position of new stator flux

vector

(iv) Calculation of stator flux vector increment

(v) Calculation of stator reference voltage

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The sampling frequency and time taken for the execution of

proposed strategy are 10 kHz and 40μsec respectively. The delay time taken

for execution of this control strategy is 60 μs, it includes the time taken for

calculation of stator flux increment, torque increment and stator flux reference

voltage.

Figure 5.21 shows the torque developed at the application of

proposed DTC strategy with the constant switching frequency and deadbeat

strategy. In this figure the developed torque is with reduced ripple at 1200

r.p.m. It is noticed that the torque ripple is minimized when compare to

classical DTC strategy and this Figure is followed by the torque developed

using proposed strategy at the speed of 1000 rpm as shown in Figure 5.22.

Figure 5.23 shows the torque developed at the speed of 600 rpm using

proposed constant frequency strategy In this figure it is observed that the

ripple content is almost same as high speed operation

Flux linkages are sinusoidal quantities in stationary reference frame

and d.c. quantities in synchronously rotating frame. For the proposed DTC

strategy, stator flux linkages are considered in synchronously rotating frame.

Figure 5.24, 5.25 and 5.26 show the stator flux magnitudes at the different

speeds such as 1200, 1000 and 600 rpm respectively. Figure 5.27 shows the

locus of stator flux with reduced ripple in DTC scheme using proposed

algorithm at 1000 r.p.m. It is noticed that the stator flux follows a smooth

circular path. Figure 5.28 shows the stator flux angular advancement at 1000

rpm. These values are given in radians. Figure 5.29 shows the three phase

stator currents of Induction motor at 1000 r.p.m at the application of constant

switching frequency and deadbeat DTC strategy. Actual speed response of the

motor is shown in Figure 5.30. Speed reference taken here is 600 rpm. For

750 rpm the speed response is shown in Figure 5.30 and this speed response is

captured on the computer screen.

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Figure 5.21 Torque developed at the application of proposed algorithm

at 1200 r.p.m

Figure 5.22 Torque developed at the application of proposed algorithm

at 1000 r.p.m

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Figure 5.23 Torque developed at the application of proposed algorithm

at 600 r.p.m

Figure 5.24 Stator flux plot at the application of proposed algorithm

of torque and flux ripple minimization at 1200 rpm

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Figure 5.25 Stator flux plot at the application of proposed algorithm

of torque and flux ripple minimization at 1000 rpm

Figure 5.26 Stator flux plot at the application of proposed algorithm

of torque and flux ripple minimization at 600 rpm

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Figure 5.27 Locus of stator flux with reduced ripple in DTC scheme

using proposed algorithm at 1000 r.p.m.

Figure 5.28 Stator flux vector angular advancement

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Figure 5.29 3- Phase stator currents of Induction motor at 600 r.p.m.

Figure 5.30 Speed Response at the application of proposed DTC strategy

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Figure 5.31 Speed response on computer screen 5.4 CONCLUSION

This chapter describes the design and implementation of direct

torque control of induction motor system with clear design procedure and

logic of different DTC strategies. A 1HP 3-phase squirrel cage induction

motor is used for experimental study and eZdsp TMS320LF2407 is used to

implement the control algorithms. The programs are written in ‘C’ language

and assembler language. The waveforms are captured by Code composer

studio software package. In this chapter, the experimental wave forms of

stator current, voltage, torque, speed, stator flux, position of stator flux for

different control strategies are shown clearly. In experimental platform, all of

the DTC strategies including the proposed DTC strategy were implemented

and waveforms were captured. Moreover, to establish the benefit of proposed

strategy, all the waveforms includes speed, torque and flux were captured.

From the waveforms, it is observed that the torque ripple produced by the

application of the proposed strategy is comparatively low. It is also observed

that, the experimental results match with the simulation results.