in digital signal processing controller (DSP) and gave the control signal to IGBT for run three phase ... the speed estimation system for three-phase induction motor and the experimental research ..... transfer function of an analog PID regulator:.
SPEED ESTIMATION TECHNIQUES FOR INDUCTION MOTOR USING DIGITAL SIGNAL PROCESSING
SOLLY ARYZA
Thesis submitted in fulfillment of the requirements For the award of the degree of Master of Engineering (Electrical)
Faculty Kejuruteraan Electrical Engineering UNIVERSITI MALAYSIA PAHANG 2011
vi
ABSTRACT
Speed estimation is one of the methods of speed sensor-less control for three phase induction motors. With the advancement of the power electronics switching devices and digital technologies, the developments of speed estimation methods have been intensively implemented from many researchers. Thus, this field of research has become more interested to investigate. Speed sensor-less control techniques can make the hardware simple and improve the reliability of the motor without the introducing the feedback sensor and it becomes more important in the modern AC servo drive. It is one of the attracting research directions in the high-precision servo control field because of its robust characteristics, simple realization and excellent dynamic response. Several common rotor speed estimation was introduced in the thesis. The model must accurately represent both the electrical and electromagnetic interactions within the machine and associated mechanical systems. In this Thesis, the neural networks controller for speed estimation has been developed approach to induction motor that has been implemented in digital signal processing controller (DSP) and gave the control signal to IGBT for run three phase inductions motor. Analysis of speed estimation nonlinear characteristics is carried out and makes a comparison with traditional linear method speed sensor less method. First, the simulation of the proposed control system is performed by using the MATLAB software and then the real time implementation is performed by using the MATLAB and the hardware. According to the mathematical model of the induction motor, the simulation of model and hardware implementation of speed sensor-less induction motor had been successfully implemented. The design and implementation of the speed estimation system for three-phase induction motor and the experimental research is presented in this Thesis. Finally, this Thesis shows the implementation of the speed estimation using DSP controller and the design of hardware and software for speed sensorless of induction motor. The experiment is completed at different speed and experiment results show that artificial neural network controller obtained a good response when compared to conventional methods.
vii
ABSTRAK
Anggaran kelajuan adalah salah satu kaedah kawalan kelajuan sensor-kurang selama tiga fasa motor aruhan. Dengan kemajuan elektronik kuasa menukar peranti dan teknologi digital, perkembangan kaedah anggaran kelajuan telah dilaksanakan dengan intensif daripada ramai penyelidik. Oleh itu, bidang penyelidikan ini telah menjadi lebih berminat untuk menyiasat. Teknik sensor-kurang kawalan kelajuan boleh membuat mudah perkakasan dan meningkatkan kebolehpercayaan motor tanpa sensor maklum balas memperkenalkan dan ia menjadi lebih penting dalam pemacu servo moden yang AC. Ia merupakan salah satu arah penyelidikan yang menarik dalam bidang kawalan servo kepersisan tinggi kerana ciri-ciri yang teguh, kesedaran mudah dan gerak balas dinamik yang sangat baik. Beberapa kelajuan biasa anggaran pemutar telah diperkenalkan dalam tesis. Model harus secara tepat mewakili kedua-dua interaksi elektrik dan elektromagnet dalam mesin dan sistem mekanikal yang berkaitan. Tesis ini, pengawal rangkaian neural untuk anggaran kelajuan telah dibangunkan pendekatan untuk motor aruhan yang telah dilaksanakan dalam pemprosesan isyarat digit pengawal (DSP) dan memberi isyarat kawalan ke IGBT untuk jangka masa tiga fasa motor induksi. Analisis ciri-ciri tak linear anggaran kelajuan dijalankan dan membuat perbandingan dengan kelajuan tradisional kaedah linear kaedah sensor yang kurang. Pertama, simulasi sistem kawalan yang dicadangkan dijalankan dengan menggunakan perisian MATLAB dan pelaksanaan masa nyata dilakukan dengan menggunakan MATLAB dan perkakasan. Menurut model motor aruhan matematik, simulasi model dan pelaksanaan perkakasan kelajuan sensor-kurang motor aruhan telah berjaya dilaksanakan. Reka bentuk dan pelaksanaan sistem anggaran kelajuan motor aruhan tiga fasa dan penyelidikan eksperimen dibentangkan di dalam tesis ini. Akhirnya, Tesis ini menunjukkan pelaksanaan anggaran kelajuan menggunakan DSP pengawal dan reka bentuk perkakasan dan perisian untuk sensorless kelajuan motor aruhan. Eksperimen itu selesai pada kelajuan yang berbeza dan hasil eksperimen menunjukkan bahawa pengawal rangkaian neural tiruan mendapat sambutan yang baik berbanding dengan kaedah konvensional.
viii
TABLE OF CONTENTS
SUPERVISOR’S DECLARATION STUDENT’S DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF SYMBOLS AND ABREVIATION
CHAPTER 1
INTRODUCTION
1.1 Background of Research
1
1.2 State Estimation Sensor less Electric Drives
2
1.2.1
Flux Estimation for Field Orientation
4
1.2.2
Flux Observers From State Space Control Theory
4
1.3
Problem Statements
5
1.4
Research Objectives
5
1.5
Scope of Thesis Work
6
1.6
Contribution
6
1.7
Thesis Outline
6
ix
CHAPTER 2
LITERATURE REVIEW
2.1 Induction Motor
8
2.2.1
Stator
10
2.2.2
Squirel Cage Rotor
11
2.2 Frequency Controller
11
2.3 Space Vector Pwm Control
12
2.4 Adaptive Pid Regulator
16
2.5 Artifial Neural Network
18
2.6 Digital Signal Processing Implementation Of Speed Sensorless
23
FOC 2.6.1 Hardware Implementation of FOC
24
2.6.2 DSP Software Implementation of FOC
25
2.7 Summary
CHAPTER 3
25
METHODOLOGY
3.1 Introduction
26
3.2 Speed Estimation Using Direct Torque Control
26
3.3 Speed Estimation Using Vector Control
27
3.4 Speed Estimation Using State Equations
28
3.5 Speed Estimation Using Rotor Slot/ Bar Harmonics
31
3.5.1
Tapped Windings
32
3.5.2
Stator Current
33
3.5.3
High frequency Injection
33
3.6 Strategy Controller Motor
35
3.6.1
Scalar Control
38
3.6.2
Vector Control
41
x
3.6.3
Direct Torque Control
43
3.6.4
Feedback Linearized Control
44
3.7 Intelligent Artificial Controller
47
3.8
Proposed Adaptive System Using Neural Networks
54
3.9
System Configuration
55
3.9.1 System Overview
55
3.9.2 Motor Controller
56
3.9.3 Synchronous Angle Calculator
57
3.9.4 Current Control of Voltage Converter
57
3.9.5 Inverter Controller
57
3.9.6 Speed Estimator
58
3.10 Voltage Model Compensated by Rotor FOC Model
CHAPTER 4
RESULTS AND DISCUSSION
4.1
Introduction
61
4.2
Simulation Result
61
4.3
Comparison of Induction Motor Operation
61
4.4
Hardware Experimental Setup
68
4.5
Performance
72
4.6
Result
73
4.7
Conclusion
76
CHAPTER 5
CONCLUSION AND FUTURE WORK
5.1
Conclusion
77
5.2
Suggestion Future Work
77
REFERENCE APPENDIX
59
xi
LIST OF TABLES
Table No.
Title
Page
3.1
Parameter of three phase induction motor
59
4.1
Comparison Operation of Induction Motor Used ANN and
66
Modified Voltage Model Flux
xii
LIST OF FIGURES
Figure No.
Title
Page
1.1
Sensorless Speed Estimation Methods
4
2.1
Cross Section of An Induction machines
10
2.2
Resultant Vector Representation of The Three Phase Currents
11
2.3
Conducting Rotor Bars Without The Rotor
12
2.4
Open loop Voltage and Frequency Controller
14
2.5
Close Loop Speed Control With Volts/Hertz Control and slip
16
regulation 2.6
Current / Slip scalar Control scheme
19
2.7
Direct Torque Controlled Induction Motor Drive
20
2.8
Feed Back Linearization Control
20
2.9
Reference Frames and Representation of Stator current and Rotor
21
Flux as Space Vectors 2.10
Three Phase Voltages Sources Inverter
24
2.11
Configuration of Space Vector PWM
26
2.12 a
Example of Space Vector PWM Pattern Reference Voltage and
28
Projections 2.12 b
Example of Space Vector PWM Pattern in Sector 3
28
2.13
Adaptive PID Control
30
2.14
Schemes of ANN for Controller
33
2.15
Experimental Set Up for Sensorless Control System
38
3.1
Speed Observer Using State Equations
43
3.2 a
Rotor Flux Orientation Control Stator Reference Frame
49
3.2 b
Rotor Flux Orientation Control In Estimated Rotor Flux
49
Reference Frame
xiii
3.3
Typical Induction Motor Drive
55
3.4
Detailed Laboratory Set Up
56
3.5
Implementation Procedure of The Control and Estimation
57
3.6
Block Diagram of MRAS Speed Observer
57
3.7
Proposed System Configuration
60
3.8
Proposed Modified Voltage Model
64
4.1
Experiment Set Up Induction Motor Based On DSP
66
4.2
Block Diagram TMS320F28335
67
4.3
Induction Motor 3 phase
68
4.4
Experimental Set Up
68
4.5
Simulation Speed for High Voltage
70
4.6
Simulation for 35 V Constant Rotating Simulations
70
4.7
Simulation for Torque of speed Estimations IM with Percentage
71
Full Load 4.8
Modeling Speed Estimation Controlling Induction Motor
73
4.9
Stator Controlling Circuit
73
4.10
Estimatiors Adaptive in Controlling
74
4.11
Stator Current
74
4.12
Speed Result
75
4.19
Torque Result
75
xiv
LIST OF SYMBOL AND ABREVIATION
PWM
Pulse Width Modulation
AC
Alternating current
HP
HP Horse Power
ANN
Artificial Neural Network
S
Slip
R1
Stator resistance in ohms
R2
Rotor resistance referred to stator in ohms
R0
Equivalent resistance corresponding to the iron
losses in
ohms L1
Leakage inductance of Stator in henry
L2
Leakage inductance of Rotor referred to stator in henry
L0
Magnetizing inductance of the stator in henry
X1
Leakage reactance of Stator in ohms
X2
Leakage reactance of Rotor referred to stator in ohms
X0
Magnetizing reactance of the stator in ohms
Vi
Input voltage in volts
V0
Output voltage in volts
V1
Voltage across the variable rotor resistance in volts
Vf
Output voltage due to forward field in volts
Vb
Output voltage due to backward field in volts
V0
Output voltage
I
Current flowing through the stator in Amps
I1
Iron-loss and magnetizing component of the noload current in Amps due to forward field
I2
Rotor current referred to the stator in Amps due to forward fiel
I3
Iron-loss and magnetizing component of the noload current in Amps due to backward field
xv
I4
Rotor current referred to the stator in Amps due
to
backward field Pgf
Airgap power developed by the motor due to forward field
Pgb
Airgap power developed by the motor due to backward field
T
Torque developed by the motor in Nm
TL
Load Torque in Nm
Ns
Synchronous speed in rps
J
Moment of inertia in Kgm2
B
Viscous friction in Nms
P
Number of Poles
ω
Angular speed in rad/sec
θ
Angular displacement in radians
Y
Output vector of the hidden layer
O
Output vector of the output layer
Vji
weight matrix
Wkj
weight matrix
B1
Bias vector
B2
Bias vector
Tr
Input
Lr
Output
Rr
Secondary time constant
Lm
Secondary inductance per phase
Rs
Secondary resistance per phase
Ls
Magnetizing inductance per phase
v
Primary winding resistance per phase
CHAPTER 1
INTRODUCTION
1.1
BACKGROUND OF RESEARCH
With the power electronics technology, computer technology and digital signal processing technology, the high-performance motor frequency control system should be widely applied. AC variable speed drive system with excellent and starting and braking performance and efficient energy-saving effect, the use of the motor frequency control technology, its capacity, speed and voltage levels can be high; speed control system is small, lightweight, inertia, high reliability, less maintenance, suitable for harsh working conditions and low cost. The frequency conversion technology, especially vector control salient features of the system technology, so from household appliances to sophisticated servo robots, and even aviation, military space industry, frequency control technology nothing less.
In the high-performance induction motor system, closed-loop motor speed control is a necessary part of the general essential, often using a photoelectric encoder speed sensor for detecting speed and feedback speed signal. However, the speed sensor installation, maintenance, cost, and poor working conditions and other issues, are shadowed ring to the induction motor speed control system simplicity and reliability, limiting the scope of application of AC variable-speed system.
Thus, the use of detection of stator voltage, current, etc. easily measured physical quantity's velocity estimation, namely, Speed sensor technology research has important practicable significance and broad space for development (Bose B.K, 2007).
2
Although the induction motor has been developed to a very advanced stage, but its speed sensor fewer control systems also need further research, there is a relatively large space for development. Speed sensor-less Application, one can complete highperformance closed-loop control without the need for speed, on the other hand, reduces the Install speed sensor in the system caused by increased hardware complexity and reliability issues down. In earlier research in this area, and there are already useful general-purpose inverter speed sensor products.
Therefore, development of independent intellectual property rights of speed sensor less AC drive products have become when the service.
1.2
STATE ESTIMATION SENSORLESS ELECTRIC DRIVES
The speed estimation is particular interest with the induction motor drives where the mechanical speed of the rotor is generally different with speed of the revolving magnetic field. The advantages of speed sensor induction motor drives is reduction of hardware complexity and cost, increase of circuit noise immunity and drive reliability, and reduction of maintenance requirements ( lin F.J and Chiu S.L, 2007 ). Operations in the hostile environments, mostly motor drives without speed/position sensors.
Many variable-speed electric drives used in general purpose applications ranging from a simple servo system to the complex traction system require a capability of speed variation with a pre-defined performance standard. In such applications, it is necessary that the actual drive-speed measurements be available at every instant to control the drive effectively. Therefore, many different kinds of speed sensors have been used to include taco generators, optical encoders, resolves, etc.
Elimination of such a requirement of having the speed sensor on the motor shaft represents a cost advantage, and also enhances the reliability of the drive owing to the absence of a mechanical sensor and associated cable accessories. The identification of the rotor speed is generally based on measured terminal voltages and currents. Various dynamic models are used to estimate the magnitude and the spatial orientation of the magnetic flux vector and for this purpose, open loop estimators or closed-loop observers
3
are used, which usually differ from respect to accuracy, robustness and sensitivity against model guideline variations. Figure 1.1 shows the block diagram of the speed sensor less induction motor control. Basically, there are two commonly used control methods: the voltage-to-frequency (V/f) control and the field oriented control (FOC). Both methods for the speed sensor less control required a speed estimation algorithm. In the V/f control, the ratio of the stator voltage to stator frequency is kept constant using feed forward control to maintain the magnetic flux in the motor at a desired level. This control is simple but it only cans satisfy moderate motor dynamic requirements. On the other hand, high motor dynamic performance can be achieved using FOC control, which is also called the vector control.
The stator currents are injected at a well-defined phase angle with respect to the spatial orientation of the rotating magnetic field, thus overcoming the complex dynamic properties of the induction motor. The spatial location of the magnetic field, that is the field angle, is difficult to measure.
There are several models and algorithms that can be used for the estimation of the field angle, for example, the open-loop estimator such as model reference adaptive system (MRAS), or the close loop observer such as the Kalman Filter. The induction motor control using the field orientation usually refers to the rotor field.
Figure 1.1: Sensor-less Speed Estimation Methods (Zhang Yan and Utkin V, 2005)
4
1.2.1
Flux Estimation for Field Orientation
Achieving high-quality torque and flux control in applications requiring both zero and very high speed operation is difficult with existing approaches to induction machines field orientation (Fitzgerald AE, 2006). There are two basic forms of field orientation; direct field orientation (DFO) which relies on direct measurement or estimation of the rotor or stator flux magnitude and angle and indirect field orientation (IFO) which uses and inherent slip relation.
1.2.2
Flux Observers from State Space Control Theory
Numerous researchers have applied conventional linear observer theory from modern state space control to the estimation of rotor flux for DFO systems, the induction machines electric model in state space and the stationery reference frame is:
p[
]
[
][
]
[
]
or simply px= Ax + Bu Where: p
= differential operator = stator current = rotor flux = stator voltage in complex vector
1.3 PROBLEM STATEMENTS
Sensors are widely used in electric drives degrade the reliability of the system especially in hostile environments and require special attention to electrical noise. Moreover, it is difficult to mount sensor in certain applications besides extra expenses involved. Therefore, a lot of researches are underway to develop accurate speed estimation techniques. With sensor-less vector control we have decoupled control
5
structure similar to that of a separately excited dc motor retaining the inherent ruggedness of the induction motor at the same time. Speed sensor-less control techniques first appeared in 1975 (C.Ilas 2005). Several reviews and comparison paper is available on sensor less control techniques (B. P. Panigrahi 2005). The commonly used method for speed estimation is model reference adaptive system (MRAS) (Zhang Yan 2008). Further, a high performance sensor less induction motor driver (G. Bartolini 2003) requires speed estimation besides estimating machine parameters most important of which is the rotor resistance which varies during the operation of the motor. Very few work have been reported on simultaneous estimation of speed and rotor resistance. (A.E. Fitzgerald 2006).
1.4
RESEARCH OBJECTIVES
The primary objective of this research as follow: -
To estimate induction motor speed using ANN
-
To design Hardware and Software of the speed and current controllers for induction motor drive using ANN method
-
1.5
To build the real time controlling MATLAB of induction machine.
SCOPE OF THESIS
- Make a hardware prototype of the proposed speed sensorless control. - To design a nonlinear feedback controller. -
Use artificial neural network for speed estimation control and make the comparison with another method.
1.6
CONTRIBUTION
In this thesis, some contribution has been provided as follow:
-
Develop speed estimation controller using digital signal processing (DSP).
-
Develop a real time speed estimation of induction machine, which can be used for teaching purpose.
6
-
Develop hardware controller for speed estimation induction motor real time design.
1.7 THESIS OUTLINE
The Thesis introduction addressed the research trends in the area of controlling motor drives for induction motor based on DSP (Digital Signal Processing) for speed estimation. The research motivation and objectives were then explained in detail.
Chapter two describes the all the literature review, components selection and sizing of the motor drive subsystems for the electric power transmission path, and highlights the issues with these subsystems that have motivated this research.
Chapter three presents some method from speed estimation, mathematical model for sensor less control induction motor , and modeling of an advanced induction motor A literature review on existing parameter estimation methods to improve the performance of propulsion motor drive system is also presented.
Chapter four describes the experiment laboratory and analysis calculation of three phase induction motor, including software-in-the-loop simulation results in experiment method of the induction Motor drive.
Chapter five concludes and future works this thesis, and presents future research topics related controlling Motor Drives.
7
CHAPTER 2
LITERATURE REVIEW
2.1
INDUCTION MOTOR The Torque – speed characteristic of an induction motor is directly related to the
resistance and reactance of the rotor (Kubota H and Matsuse K, 2006). Hence, different Torque- Speed characteristics may be obtained by designing rotor circuits with different ratios of rotor resistance to rotor reactance.
In induction machines, the field circuit is on the stator. The armature circuit is on the rotor, and the rotor poles are induced by transformer action. Both the stator poles and the rotor poles rotate at the synchronous speed, but the rotor rotates physically at a speed slightly less than a synchronous speed and slows down as the load torque and power requirements increase.
A rotating magnetic field, produced by a stationary winding (called the stator), induces an alternating, cmf and current in the rotor. The resultant interaction of the induced rotor current with the rotating field of the stationery winding produces motor torque Figure 2.1.
The stator is identical to a stator of a synchronous machine: three phases, P poles, sinusoidal mmf and flux distribution, and synchronous speed. In induction motors, the stator carries the field. The rotor is much different; in induction motors, the rotor is an iron cylinder with large embedded conductors, which are shorted to allow the free flow of current. The stator flux induces the ac current in the each of the rotor
8
conductors, and an ac voltage is induced in the rotor to drive the currents. The currents in the conductor produce a magnetic.
Figure 2.1: Cross section of an Induction machines
Induction machine technology is a mature technology with extensive research and development activities over past 100 years. Recent development in digital signal processor and advanced vector control algorithm allow controlling an induction machine like a DC machine without the maintenance requirements. Induction motors are considered as workhorses of the industry because of their low cost, robustness and reliability. Induction machines are used in electric and hybrid electric vehicle applications because they are rugged, lower-cost, operate over wide speed range, and are capable of operating at high speed.
The size of the induction machine is smaller than that of a separately excited DC machine for similar power rating. The induction machine is the most mature technology among the commutated fewer motor drives.
There are two types of induction machines: squirrel cage and wound rotor. In squirrel cage machines, the rotor winding consists of short-circuited copper or aluminium bars with ends welded to copper rings known as end rings. In wound rotor induction machines, the rotor windings are brought to the outside with the help of slip rings so that the rotor resistance can be varied by adding external resistance. If threephase voltages are applied to the stator, the stator magnetic field will cut the rotor conductors, and will induce voltages in the rotor bars. The induced voltages will cause
9
rotor currents to flow in the rotor circuit, since the rotor is short-circuited. The rotor current will interact with the air gap field to produce torque. As a result the rotor will start rotating in the direction of the rotating field. The difference between the rotor speed and the stator flux synchronous speed is the slip speed by which the rotor is slipping from the stator magnetic (Xu Z and Rahma M.F, 2007).
2.1.1.
Stator
The stator is made up of this laminations of a highly permeable steel that together yields high magnetic flux density and low core losses. The stator winding are wound and placed into slots in the stator, 120° apart in space. These are wound to create stator magnetic poles when current flows through them. The number of stator poles in conjunction with the frequency of the applied three phase power determined the speed of rotation og the stator’s magnetic field and thereby the speed of the induction motor. Figure 2.2 shows a stat Ice. pictures of the flux and current direction when only winding A of the stator is energized.
While normal operation of we-connected induction machine precludes having the current through only one winding, this drawing illustrates the resultant flux vector. During normal operation, three currents, oriented 120 degrees part in time, are applied to the three windings and can be combined to form a→ vector→ It is found to be. → = →
stator vector. The stator flux
/ R where R is defined to be the magnetic circuit
reluctance.
Figure 2.2: Resultant vector representation of the three phase currents.
10
It is important to note that a balanced three phase current applied to the stator winding's results in a rotating stator current and therefore, a rotating flux vector. The magnitude of the →
Stator vector is constant as it rotates within a plane, it can be
represented by two components; x and y. Analysis can be greatly simplified if we consider that the stator current has only these two components and represented it as
Is= [
2.1.2.
]
( 2.1 )
Squirel Cage Rotor.
The squirrel cage rotor is also made up of thin laminations of a highly permeable steel that together yield high magnetic flux density and low core losses. The rotor bars, or windings, are placed in the rotor slots and shorted together at each end as shown in Figure 2.3. The rotor is mounted on a shaft so that it can rotate and is placed inside the stator. The number, size, skew and depth of recess of the rotor conductors influence motor performance.
Figure 2.3: Conducting rotor bars without the rotor.
2.2
FREQUENCY CONTROLLER
The speed of an induction motor is very near to its synchronous speed. The difference between the two being characterized by the slip speed. If the synchronous speed of the induction motor is changed, there is corresponding change in the speedof
11
the motor and this can be done by changing the supply frequency of the a.c.source. The relationship between the synchronous speed and the frequency is given by
ηs =
(2.2)
where ηs is the synchronous speed in rev/min, fs is the supply frequency in Hz and P is the number of poles.
The a.c. supply available for the utility purpose is of a constant frequency and when an induction motor is operated with utility supply, it runs at a constant speed. For the purpose of speed control, a frequency changer is required to change the speed of the induction motor. The electrical motor drives which use frequency changers to achieve the speed control are referred to as Frequency-Controlled Electric Drives (Briz F 2002) .
2.3
SPACE VECTOR PWM CONTROL
Space vector pulse width modulation (PWM) technique has become a popular PWM technique for the control of three phase voltage source inverters (VSI) for applications such as induction motor drives. In comparison to the direct sinusoidal PWM techniques, the space vector PWM technique generates less harmonic distortion in the output voltages and currents and provides more efficient use of the dc supply voltage to the inverter (Trzynadlowski. A 2005).
Figure 2.4. Shows abasic three phase power inverter circuit, where Va, Vb, Vc is the voltages applied to the induction motor, and where Vdc is the inverter dc input voltage.
12
Figure 2.4: Three Phase Voltages Sources Inverter
The six switches Q1 to Q6 is insulated gate bipolar transistors IGBT). The ON-OFF sequence of these switches follows the conditions below:
-
Three of the switches must be always ON and three are always OFF.
-
The upper and the lower switches of the same leg are driven with two complementary pulsed signals with a dead band betwee n the two signals to avoid short circuit.
-
The induction motor is supplied with the required three phase voltages for the designed operating conditions using PWM technique. In this research paper, the space vector PWM method is used to generate the gating signals for the switches in the VSI inverter that drives the induction motor with high performance in terms of fast response to changes of loads and speed commands.
The relationship between the switching variable vector [a b c]T and the line to line output voltage vector [ Vab Vbc Vca]T and the phase voltage vector [Va Vb Vc]T is given by the following equations.
[
] = Vdc[
[ ]=
][ ]
[
][ ]
(2.3)
(2.4)
13
Where Vab = Va * Vb, Vbc = Vb * Vc, Vca = Vc*Va. The stator α-β voltages corresponding to the three phase voltages are:
Vsa = Va Vsβ =
√
(2.5) Va +
√
Vb
(2.6)
The above equation can also be expressed in matrix form by using the equation
Va+Vb+Vc = 0.
[
]= [
√
√
][ ]
(2.7)
There are eight (23) possible combinations for the switch commands. These eight switch combinations determine the eight phase voltages vectors, of which the results are six non zero vector (V1-V6) and two zero vectors(V0, V7) as shown in Figure 2.5.
Figure 2.5: Configuration Of Space Vector PWM
14
The objective of space vector PWM technique is to generate the desired instantaneous reference voltages from the corresponding basic space vectors based on the switching states. Figure 2.5. It shows that the basic space vectors divide the plan into six sectors. Depending on the sector that the reference voltages are in, two adjacent basic vectors are chosen. The two vectors are time weighted in a sample period T (PWM period) to produce the desired output voltage (Buja 2004).
Assuming that the reference vector Vout is in the sector 3 as shown in Figure 2 6a, the application time of two adjacent vectors is given by:
T = T4+T6+T0 → =
→+
(2.8) →
(2.9)
Where T4 and T6 are the duration of the basic vectors V4 and V6 to be applied respectively. T0 is the duration for the zero vectors (V0 or V7). Once the reference voltage Vout and the PWM period T are known, T4, T6 and T0 can be determined according to the above equation (3).
T4 =
(3
√
)
(2.10)
T6 = √
(2.11)
T0= T – (T4 + T6)
(2.12)
Where Vsa, Vsβ is α-β component of Vout. The voltage Vout is an approximation of the desired output voltage based on the assumption that the change of output voltage is negligible within a PWM period T.. Therefore, it is crucial that the PWM period is small with respect to the change of Vout. In practice the approximation is very good because the calureculation is performed in every PWM period (200μs).
15
2.4. ADAPTIVE PID REGULATOR
A motor drive based on the field oriented control needs two inputs; the torque component reference isqref and the flux component reference isdref. The classical proportional and integral (PI) regulator is often used to regulate the motor torque and flux to the desired values(Leksono Edi 2000). This regulator which is implemented in this thesis, is capable of reaching constant references by correctly setting both the P term (Kp) and the I term(K1). The P term and I term respectively regulate the error sensibility and steady state error. The regulation can be improved with the adaptive proportional integral derivative (PID) regulator (Lin F.J 1999).
To design a digital PID controller for the motor control, it may first consider the transfer function of an analog PID regulator:
D(s)= Kp +K1 +KDs
(2.13)
Where Kp is the proportional gain, Kt is the integral gain, and KD is the derivative gain. Similar to the Laplace Transform in continues time domain, the integrator and differentilator can be represented by pulse transfer function in discrete domain.
Where Kp is the proportional gain, Kt is the integral gain, and KD is the derivative gain. Similar to the laplace transform is continous time domain, the integrator and differentiator can be represented by pulse transfer function in discrete domain.
Integrator=
(2.14)
Differentiator =
(2.15)
Where T is the sampling period. Thus the transfer function of a digital non adaptive PID controller is
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D(z)= Kp +K1
+KD
=
(2.16)
(2.17)
Where
a0 =Kp+
+
(2.18)
a1=-Kp+
+
(2.19)
a2=
(2.20)
Figure 2.6. It shows the block diagram of an adaptive control system. The r is input or set point, c is the output feedback, y is the output and D(z) is the adaptive PID controller. The adaptive control scheme consists of two parts. First, the regulator uses initial (or updated) PID parameter and feedback input samples to determine the regulation. Second, the regular updates the PID parameters until the error signal e2 are approaching zero.
Figure 2.6: Adaptive PID control
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A quadratic objective function is used to minimize e2 with respect to the regulator parameters.
f(a0,a1,a2) = ½ (e2)2 (z)] 2
= = [
(
)
]2
(2.21)
The first order partial derivatives with respect to the regulator parameter a 0,a1,a2 are given below:
(
)e1 + (
)(
)e12
(2.22)
Where a0,a1,a2 can be solved according to the steepest decent method of the gradient techniques, so that the following equation can be obtained.
An(k+1) =an(k) + β
2.5.
n= 0,1,2;
k=0,1,2……
(2.23)
ARTIFICIAL INTELLIGENT CONTROL
Intelligent control is a class of control techniques, that use various AI computing approaches like neural networks,
Bayesian probability,
fuzzy logic, machine
learning, evolutionary computation and genetic algorithms(Qinghui Wu 2009). Before we begin with what an intelligent control is, it is important to note what an intelligent agent essentially means. An intelligent or a rational agent is one who simply does the right things. This leads to a definition of an ideal rational agent: For each possible percept sequence (Fitzgerald. A 2006), an Agent ideal rational agent should do whatever action is expected to maximize its performance measure, on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has. Intelligent control achieves automation via the emulation of biological intelligence(B.K
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2007). It either seeks to replace a human who performs a control task (e.g., a chemical process operator) or it borrows ideas from how biological systems solve problems and applies them to the solution of control problems (e.g., the use of neural networks for control).
Intelligent control can be divided into the following major sub-domains:
Neural network control
Bayesian control
Fuzzy (logic) control
Neuro-fuzzy control
Expert Systems
Genetic control
Intelligent agents (Cognitive/Conscious control)
New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them.
Neural network controllers
Neural networks have been used to solve problems in almost all spheres of science and technology. Neural network control basically involves two steps:
System identification
Controlling
It has been shown that a feed forward network with nonlinear, continuous and differentiable
activation
functions
have universal
approximation capability(Buja
2004). Recurrent networks have also been used for system identification. Given, a set of input-output data pairs, system identification aims to form a mapping among these data pairs. Such a network is supposed to capture the dynamics of a system(Qiang Lu and Proceedings of the CSEE 2006).
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Artificial neural networks are a computational tool, based on the properties of biological neural systems. Neural networks excel in a number of problem areas where conventional von Neumann computer systems have traditionally been slow and inefficient (Lascu C 2005). This thesis is discussed the creation and use of artificial neural networks for give signal in trigger and make open thyristor to be gave current to induction motor 3 phase. Artificial Neural Networks, also known as “Artificial neural nets”, “neural nets”, or ANN for short, are a computational tool modeled on the interconnection of the neuron in the nervous systems of the human brain and that of other organisms. Biological Neural Nets (BNN) are the naturally occurring equivalent of the ANN (Lee K.B 2001). Both BNN and ANN are network systems constructed from atomic components known as “neurons”. Artificial neural networks are very different from biological networks, although many of the concepts and characteristics of biological systems are faithfully reproduced in the artificial system. Artificial neural nets are a type of non-linear processing system that is ideally suited for a wide range of tasks, especially tasks where there is no existing algorithm for task completion. ANN can be trained to solve certain problems using a teaching method and sample data. In this way, identically constructed ANN can be used to perform different tasks depending on the training received. With proper training, ANN is capable of generalization, the ability to recognize similarities among different input patterns, especially patterns that have been corrupted by noise(Mohamadian M 2003)
Most of the control theory developed with linear time invariant systems. And powerful methods for designing controllers for such systems are currently available. However, as applications become more complex, the processes to be controlled are increasingly characterized by uncertainty in the system model. Non-linarites, presence of noise and effect of have distributed sensors and actuators with their associated delays and other problems. One approach used for handling a non-linear system has been to linearize it around an equilibrium point, and then use the well-established linear control theory to study issues like stability, controllability, and design controllers to function in an approximate linear region around the equilibrium point.
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In most cases where ANNs have been used in the control of induction motors, online training has been preferred. On line training has the potential to adapt to changing motor parameters, but it is computationally very expensive, and it is very difficult to run an average sized ANN in real time with on line training.
On way to control a plant using ANNs is to train the ANN off line to mimic an existing controller. This implies that the ANN must have as input, all the quantities that are input to the existing controller (with suitable number of previous values)
The ANN is trained off line to produce the same outputs as the controller and after sufficient training; the ANN should be able to replace the controller. A block diagram of this scheme is shown in Figure 2.7.
Figure 2.7: Schemes of ANN for controller.
Fuzzy Logic controllers Fuzzy control is a methodology to represent and implement a (smart) human’s knowledge about how to control a system. The fuzzy controller has several components: • The rule-base is a set of rules about how to control. • Fuzzification is the process of transforming the numeric inputs into a form that can be used by the inference mechanism.
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• The inference mechanism uses information about the current inputs (formed by fuzzification), decides which rules apply in the current situation, and forms conclusions about what the plant input should be. • Defuzzification converts the conclusions reached by the inference mechanism into a numeric input for the plant.
Genetic Control
To understand what Genetic control is, one must suffice oneself with the theory of genes and the importance of its study in an artificially intelligent machine Genes are the blueprint of our bodies, a blueprint that creates the variety of proteins essential to any organism’s survival. These proteins, which are used in countless ways by our bodies, are produced by genetic sequences, i.e. our genes, as described in the cell biology section, protein synthesis pages. Utilization of Genetic Information All cells have originated from the single zygote cell that formed it, and therefore possess all the genetic information that was held in that zygote. This means that an organism could be cloned from the genetic information in the nucleus of one cell, regardless of the volume of cells that make the organism (be it one or billions).
However, this brings about the following question, how can cells become differentiated and specialized to perform a particular function if they are all the same? The answer to this is each cell performing its unique role has some of its genes 'switched on' and some 'switched off'. In light of this, the cells in our body still contain the same genetic information, though only a partial amount of this information is being used in any one cell. Switched On and Switched off Some genes are permanently switched on, because they contain the blueprint for vital metabolites (enzymes required for respiration etc). However, since cells become specialized in multi-cellular organisms such as us, some genes become switched off because they are no longer required to be functional in that particular cell or tissue.
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For instance, insulin is produced in pancreas cells, which must have the gene that codes for insulin switched on, and perhaps other genes that are un-related to the role of the pancreas can be switched off.
Some other genes that will be functional during specialization determine the physical characteristics of the cell, i.e. long and smooth for a muscle cell or indented like a goblet cell.
Genetic control then transforms all of this available information into an algorithm to design and texture genes for avoiding mitotic growth and/or identifying probable threats to the proposed gene. This branch of science deals with protection, regeneration and transformation of the analytical information collected.
Bayesian controllers
Bayesian probability has produced a number of algorithms that are in common use in many advanced control systems, serving as state space estimators of some variables that are used in the controller.
The Kalman filter and the Particle filter are two examples of popular Bayesian control components. The Bayesian approach to controller design requires often an important effort in deriving the so-called system model and measurement model, which are the mathematical relationships linking the state variables to the sensor measurements available in the controlled system. In this respect, it is very closely linked to the system-theoretic approach to control design.
2.6
DIGITAL SIGNAL PROCESSING IMPLEMANTATION OF SPEED SENSORLESS FOC
The fixed point DSP TMF28335 is the core of the control system designed in this thesis. The following provides an overview of the DSP FOC operations.
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-
DSP operates its analog-to-digital converter (ADC) to collect the instantaneous induction motor input currents measured by a current transducer at the motor terminal.
-
DSP carries out a specific d-coordinate chosen to be in line with the rotor flux, using the α-β currents and the flux angle, to compute the d-coordinate and qcoordinate currents.
-
DSP carries out an inverse d-q transformation, using the d-q reference voltages, to compute the stator α and β reference voltages.
-
DSP executes three feedback regulators for the motor speed, the rotor torque, and the rotor flux, to determine the d-coordinate and the q-coordinate stator reference voltages.
-
DSP executed the space vector pulse width modulation (PWM) module, using the α-β reference voltages, to compute the PWM control signals.
-
DSP finally outputs the PWM control signal to the gating circuit of the power electronic inverter that drives the induction motor.
2.6.1
Hardware Implementation of Field Oriented Control
Figure 2.8 shows the set up general of DSP block diagram of the hardware required to
implement a sensorless speed control system, for an induction motor.
The DSP control system designed in this Thesis consists of the following major hardware components:
1. DSP controller 2. Power Inverter 3. Induction Motor
Figure 2.8: Experimental Set up general of DSP
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DSP is the key control element in this thesis for design of the FOC of induction motors. Setting DSP TMF28335 given as follows.
-
Development/Emulation: Code Composer 4.1. Support real time debugging.
-
CPU clock: 30 MHZ.
-
PWM frequency :5 kHz.
-
PWM mode: Symmetrical with the dead band 1,8μs.
-
Interrupts
-
System time base.
2.6.2
DSP Software Implementation of Field Oriented Control
DSP control software developed in this thesis is based on two modules: the initialization and the run module (Song J 2000). The initialization module is performed only once at the beginning of the software execution. The run module is based on a user interface loop interrupted by the PWM underflow.
The benefits of structured modulator software are well known. This is especially true for large complex system, such as the FOC motor control, with many sub-block. It reduces the developing time, and could be reused in the future a project. Therefore, a new method use DSP driver incremental build process is used in this research thesis.
2.7
SUMMARY
Modeling of Induction motor is the first and essential step for its identification and control. The mathematical model of the machine should on one hand have such a structure so as to completely describe the characteristics of the machine and on the other hand, be convenient to use it for implementing estimation algorithms. In this chapter, literature review is discussed. The chapter starts with parts of induction motor like stator, rotor, squire cage and, etc., introduction of the reference frames. Then principal operation.Differential equation, hardware DSP and software implementation.
