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113
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
115
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.
116
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
119
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
120
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.
121
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
122
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)
124
Figure 5.6 Photographical view of Experimental Set-up
125
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
126
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
127
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.
128
Figure 5.8 Stator flux in conventional DTC scheme
Figure 5.9 Stator flux in stationary reference frame in DTC scheme
(d-axis)
129
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
130
Figure 5.12 Position of stator flux (angle) shown on computer screen
Figure 5.13 Direct and Quadrature axes currents
131
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
132
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
134
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.
135
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.
136
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)
137
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
138
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.
139
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
140
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
141
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
142
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
143
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
144
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.