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10thInternational Symposium
Topical Problems in the Field of Electrical and Power Engineering
Prnu, Estonia, January 10-15, 2011
Simulation Study of Inverter-Fed Motor Drives
Mikhail Egorov
Tallinn University of Technology
Abstract
The paper presents models developed to study
modulation systems of inverters that supply the
induction motor drives. A modelling technique and
simulation results are proposed. The benefits of the
discontinuous space vector modulation algorithms
for the six-step, pulse-width, and continuous space
vector control methods are described.
Keywords
Power electronics, electric drive, modelling,
simulation, Simulink, modulation
1. IntroductionAs the complexity of the motor drive circuits and
systems increases, the role of simulation as an
analysis and verification tool has expanded
significantly. It has become a cost-efficient way to
design many complex circuits in this way. Both
large- and small-signal simulations are performed onthe drive motors, power converters, and control
circuits to better predict and verify projects [1].
Several simulation toolkits are propagated such as
Simulink, Multisim, Spiceand PSim[2], [3]. Among
them, Matlab is in high demand in the motor drive
study [4]. This high-level 4th generation
programming language and interactive environment
enables users to perform intensive calculation-based
tasks very fast. The toolbox allows matrix
manipulation, functions and data plotting, algorithm
implementation, creation of the user interfaces, and
interfacing with programs in other languages. It hasbeen widely adopted for over 25 years in the
academic community, industry and research centers.
The toolkit provides the users with a large collection
of toolboxes and modules for a variety of
applications in many fields of interest. Its interactive
graphical superstructure Simulink [5] was added to
Matlab to make the modeling and simulation of
various systems as easy as connecting predefined
and designed building blocks. Simulink contains
many block sets that are used in different
applications, such as the communication block,
signal-processing block, etc [6], [7].
The objective of this study was to develop and study
Simulink models of the control blocks of inverters
that supply and adjust induction motors of
asynchronous motor drives. This problem is
significant because the control method governs the
voltage and current harmonics, torque ripple,
acoustic noise emitted from the motor as well as
electromagnetic interference. To this end, the new
models of the motor drive inverter control systems
developed are described in this paper.
Several techniques are known in the drive controlpractice: low commutation simple six-stepmodulation, high commutation pulse-widthmodulation (PWM) and progressive space vectormodulation (SVM).
The six-step modulation algorithm generatessignificant harmonic distortion as its alternatingcurrent (ac) waveforms are the low frequencyrectangles. Here, the reference sinusoidal voltage isapproximated by the six discrete voltage levelsavailable at direct current (dc) supply.Implementation of this type of modulation isrelatively simple and does not require high switching
speed, which makes it suitable for converters.Though the six-step inverters have found occasionaluse in motor drives, simulation of this technique isimportant for understanding open-loop voltage-frequency control [8].
Intensive switching PWM techniques are veryvaluable for drive performance. Simulation of thiscontrol method is beneficial to study voltage andcurrent harmonics, torque ripple, acoustic noiseemitted from an induction motor, and alsoelectromagnetic interference [9]-[11].
An SVM method is the most advanced computation-intensive control approach for generating afundamental sine wave providing a higher voltage tothe motor and lower switching losses than both thesix-step and PWM methods. This is known today asthe best among all the power converter controltechniques in the field of variable drive applications.Several SVM algorithms have been reported in[12]-[15].
The models proposed in this paper help to analyze
and compare different modulation techniques from
the ripple and switching loss point of view.
Advantages and disadvantages of different
modulation methods are illustrated by the simulation
results of the inverter performance in ac driveapplications. The developed models can be
effectively applied in the development and study of
open-loop and closed loop motor drives.
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2. Modelling and ExperimentationArrangements
To perform numerical simulation, a set of models
was developed where a controlled induction motor
drive was investigated in the Matlab/Simulink
toolbox. The models designed in the simulation
environment were accomplished with an inductionmotor, loading device, and independent bridge-
connected IGBT simulators (Fig. 1). The control
circuits which are focused in the paper are enveloped
by the bold contours in Fig. 1. Simulation was
executed for the voltage/frequency control mode of
an induction motor drive using the six-step, PWM
and SVM continuous and discontinuous techniques.
Power converter M Load
Gate circuits
ControllerInputs
References
Supply
Controls
Feedbacksfrom
sensors
Fig. 1. Generalized block diagram of a motor drive
A model of a squirrel cage induction motor
M33AA 132S of ABB series was used for
simulation. To measure its torque, currents and
speed of rotation, the Simulink machine
measurement block was used.
To develop the control algorithms, the three-phasebridge inverter shown in Fig. 2 consisting of the
three-leg IGBTs VT1and VT4, VT2and VT5, and
VT3 and VT6 with the freewheeling diodes was
studied.
VT4
VD1 VD3
VD4 VD5 VD6
VT1 VT3
VT5 VT6
UdM
C
C
VD2VT2
N
L1
L2L3
+
Fig. 2. Three-phase bridge inverter
To this aim, the Simulinkuniversal bridge block was
used built on the Semikron SKM 145 GB 124 DN
transistor simulators. The switches were examined in
eight different combinations designated by the binary
variables 100, 110, 010, 011, 001, 101, 111, and
000, which indicate whether the switch is under the
positive (1) or negative (0) supply, thus defining all
possible switching states. During the modulating
period 1 / = 1 / (2fm), a phase voltage sequentially
changed its values depending on which of theswitches were on-state. Here, is an angular motor
speed, fmis the modulating frequency (max 50 Hz),
and Ud is the dc supply voltage. To simulate a
dc link, the Simulinkdc voltage source was used. For
voltage and current measuring and tracing, the
Simulink voltage and current measurement blocks
and scopes were connected. The Simulink series
RLC branches were used to reach the dc link and
load neutral points.
To obtain sufficient resolution, the modulating
period was presented by the six sectors, each dividedinto 20 sampling periodsTc. Therefore, the sampling
frequency was 120fm (max 6 kHz). All the schemes
assume digital implementation therefore calculations
were performed at the beginning of each sampling
period, based on the value of the reference voltage
and speed at that instant. In this way, the reference
was updated at every sampling intervalTc.
To test the proposed models, an experimental
workplace was organized at the Department of
Electrical Drives and Power Electronics of Tallinn
University of Technology. It unites two electric
drives ACS800 series, the testing drive and theloading one. Each drive has the same structure,
consisting of an induction motor, power converter,
remote console, as well as the cabinet, housing,
measuring, and cabling equipment. The motor shafts
of the drives were mechanically coupled to provide
their joint rotation. Both power converters include
the line-side active rectifier and the motor-side
inverter connected via the dc link. The tested motor
M33AA 132S has the following characteristics: rated
power 5.5 kW, voltage 400 V, current 11 A, speed
1460 r/min, torque 36 Nm, and the moment of inertia
0.038 kgm2. The loading motor M33AA 160L has
the rated power 15 kW, voltage 400 V, current 29 A,
speed 1460 r/min, torque 98 Nm, and the moment of
inertia 0.102 kgm2. The power converters ACS800
enable two modes of operation: voltage / frequency
(U/f) control and direct torque control (DTC) with
direct and indirect measuring of the motor speed,
torque, and current. Their technical data are as
follows: input voltage 400 V, output voltage 0 to
415 V, output frequency from 8 to 300 Hz, and
output power 15 kW with the speed and torque
scalar and vector control, flux and mechanical
braking, acceleration and deceleration ramps.
During the verification stage of research the
differences of the transient and steady-state
characteristics between the tested and the simulated
drive were studied. Both the half-speed, half-rated
running (18 Nm) and the rated speed nominal
loading (36 Nm) operations were compared. In this
way, the model correctness and adequacy were
confirmed.
3.Six-Step Control ModelTo generate the six-step control signals, six Simulink
pulse generators were connected directly to the
IGBT gates (Fig. 3). Thus, switching of the threeinverter legs supplied by the dc voltage is phase-
shifted by 120 and each phase L1, L2, L3 is kept
under the current during half a modulating period
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and open in another half-period. In this way, specific
phase is alternately switched from the positive pole
to the negative one being sequentially in series with
the remaining two parallel-connected phases. The
traces of the gate signals, the phase-to-ground,
phase-to-neutral and line-to-line voltages and the
phase current are shown in Fig. 4 for the full-loaded
drive running with nominal speed.
Fig. 3. Six-step control system
Fig. 4. Simulation results of six-step control
4.PWM Control ModelA model developed to simulate the PWM controlleris presented in Fig. 5. It includes three sine-wavegenerators accompanied by speed-amplitude andmodulating index reference blocks, a carriergenerator, three comparators and gate drivers. In thisway, the symmetrical triangle double-sided wave ofthe carrier frequency is compared with themodulating wave thereby generating the controlpulses. The control output represents the chain ofconstant magnitude pulses, the duration of which ismodulated to obtain the sinusoidal waveform. The
traces of the gate signals, the phase-to-ground,phase-to-neutral and line-to-line voltages and thephase current are shown in Fig. 6 for the full-loadeddrive running with nominal speed.
Fig. 5. PWM control system
Fig. 6. Simulation results of PWM control
5.SVM Control ModelAn SVM controller model was designed on the basis
of the Simulinkembedded Matlabfunction block to
compose and study both continuous and
discontinuous SVM algorithms (Fig. 7). Here, the
switching sequence of pulses is searched in each
sampling interval, the time durations of which are
computed in real on the basis of the value of the
reference voltage and speed at the beginning of each
sampling period. Three inputs of the function block
carry the reference amplitude, speed and clock
signals. The traces for the continuous SVM are
practically the same as for the PWM-fed drive. For
the discontinuous SVM, the gate signals, the phase-
to-neutral and line-to-line voltages as well as the
phase current are shown in Fig. 8 for the full-loaded
drive running with nominal speed. Some additionalcurrent distortion was registered here in contrast to
the continuous SVM as a result of non-linear inverter
adjustment.
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Fig. 7. SVM control system
6.ConclusionThree models of the motor drive control systems
were developed in the Matlab/Simulink
environment and studied using the proposed
models: low commutation simple six-stepmodulation, high commutation PWM, and
progressive space vector modulation SVM. Studies
focused on the impact of the modulation method on
the voltage and current harmonics, torque ripple,
acoustic noise emitted from the motor and
electromagnetic interference was studied.
Advantages and drawbacks of different control
methods were illustrated in the steady-state and
dynamic modes of the converter performance in ac
drive applications. Simulation revealed an
improvement in the switching performance of the
motor drive converter from the discontinuous SVM
operation when compared to the six-step
modulation, PWM and continuous SVM
techniques.
Acknowledgements
This paper is supported by Project DAR8130
II Doctoral School of Energy and
Geotechnology. Also, the author expresses
gratitude to his supervisor Professor Valery
Vodovozov for essential contribution to this work.
Fig. 8. Simulation results of SVM control
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