Tze-Fun Chan, Keli Shi

Applied Intelligent Control of Induction Motor Drives (eBook, PDF)

• Format: PDF

Produktbeschreibung

Induction motors are the most important workhorses in industry. They are mostly used as constant-speed drives when fed from a voltage source of fixed frequency. Advent of advanced power electronic converters and powerful digital signal processors, however, has made possible the development of high performance, adjustable speed AC motor drives. This book aims to explore new areas of induction motor control based on artificial intelligence (AI) techniques in order to make the controller less sensitive to parameter changes. Selected AI techniques are applied for different induction motor control strategies. The book presents a practical computer simulation model of the induction motor that could be used for studying various induction motor drive operations. The control strategies explored include expert-system-based acceleration control, hybrid-fuzzy/PI two-stage control, neural-network-based direct self control, and genetic algorithm based extended Kalman filter for rotor speed estimation. There are also chapters on neural-network-based parameter estimation, genetic-algorithm-based optimized random PWM strategy, and experimental investigations. A chapter is provided as a primer for readers to get started with simulation studies on various AI techniques. * Presents major artificial intelligence techniques to induction motor drives * Uses a practical simulation approach to get interested readers started on drive development * Authored by experienced scientists with over 20 years of experience in the field * Provides numerous examples and the latest research results * Simulation programs available from the book's Companion Website This book will be invaluable to graduate students and research engineers who specialize in electric motor drives, electric vehicles, and electric ship propulsion. Graduate students in intelligent control, applied electric motion, and energy, as well as engineers in industrial electronics, automation, and electrical transportation, will also find this book helpful. Simulation materials available for download at href="http://www.wiley.com/go/chanmotor">www.wiley.com/go/chanmotor

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 448
• Erscheinungstermin: 30. Dezember 2010
• Englisch
• ISBN-13: 9780470825570
• Artikelnr.: 37299454

Autorenporträt

Tze-Fun Chan is an associate professor of electrical engineering at the Hong Kong Polytechnic University, where he has been working for over 30 years since 1978. Chan's research interests are self-excited induction generators, brushless AC generators, permanent-magnet machines, finite element analysis of electric machines, and electric motor drives control. In June 2006, Chan was awarded a Prize Paper by IEEE Power Engineering Society Power Generation and Energy Development Committee. In 2007, Chan co-authored a book published by Wiley. He received the B.Sc. and M.Phil degrees in electrical engineering from the University of Hong Kong in 1974 and 1980 respectively. He received his PhD degree in electrical engineering from City University London in 2005. Keli Shi is a Research Engineer of Netpower Technologies Inc.. His research interests are DSP applications and intelligent control of induction and permanent magnet machines. He received his BS degree in electronics and electrical engineering from Chengdu University of Science and Technology and MS degree in electrical engineering from Harbin Institute of Technology in 1983 and 1989 respectively. He received his PhD in electrical engineering from The Hong Kong Polytechnic University in 2001.

Inhaltsangabe

Preface. Acknowledgments. About the Authors. List of Symbols. 1
Introduction. 1.1 Induction Motor. 1.2 Induction Motor Control. 1.3 Review
of Previous Work. 1.4 Present Study. 2 Philosophy of Induction Motor
Control. 2.1 Introduction. 2.2 Induction Motor Control Theory. 2.3
Induction Motor Control Algorithms. 2.4 Speed Estimation Algorithms. 2.5
Hardware. 3 Modeling and Simulation of Induction Motor. 3.1 Introduction.
3.2 Modeling of Induction Motor. 3.3 Current-Input Model of Induction
Motor. 3.4 Voltage-Input Model of Induction Motor. 3.5 Discrete-State Model
of Induction Motor. 3.6 Modeling and Simulation of Sinusoidal PWM. 3.7
Modeling and Simulation of Encoder. 3.8 Modeling of Decoder. 3.9 Simulation
of Induction Motor with PWM Inverter and Encoder/Decoder. 3.10
MATLAB/Simulink Programming Examples. 3.11 Summary. 4 Fundamentals of
Intelligent Control Simulation. 4.1 Introduction. 4.2 Getting Started with
Fuzzy Logical Simulation. 4.3 Getting Started with Neural-Network
Simulation. 4.4 Getting Started with Kalman Filter Simulation. 4.5 Getting
Started with Genetic Algorithm Simulation. 4.6 Summary. 5
Expert-System-based Acceleration Control. 5.1 Introduction. 5.2
Relationship between the Stator Voltage Vector and Rotor Acceleration. 5.3
Analysis of Motor Acceleration of the Rotor. 5.4 Control Strategy of
Voltage Vector Comparison and Voltage Vector Retaining. 5.5 Expert-System
Control for Induction Motor. 5.6 Computer Simulation and Comparison. 5.7
Summary. 6 Hybrid Fuzzy/PI Two-Stage Control. 6.1 Introduction. 6.2
Two-Stage Control Strategy for an Induction Motor. 6.3 Fuzzy Frequency
Control. 6.4 Current Magnitude PI Control. 6.5 Hybrid Fuzzy/PI Two-Stage
Controller for an Induction Motor. 6.6 Simulation Study on a 7.5 kW
Induction Motor. 6.7 Simulation Study on a 0.147 kW Induction Motor. 6.8
MATLAB / Simulink Programming Examples. 6.9 Summary. 7 Neural-Network-based
Direct Self Control. 7.1 Introduction. 7.2 Neural Networks. 7.3
Neural-Network Controller of DSC. 7.4 Simulation of Neural-Network-based
DSC. 7.5 MATLAB/Simulink Programming Examples. 7.6 Summary. 8 Parameter
Estimation Using Neural Networks. 8.1 Introduction. 8.2 Integral Equations
Based on the 'T' Equivalent Circuit. 8.3 Integral Equations based on the
'G' Equivalent Circuit. 8.4 Parameter Estimation of Induction Motor Using
ANN. 8.5 ANN-based Induction Motor Models. 8.6 Effect of Noise in Training
Data on Estimated Parameters. 8.7 Estimation of Load, Flux and Speed. 8.8
MATLAB / Simulink Programming Examples. 8.9 Summary. 9 GA-Optimized
Extended Kalman Filter for Speed Estimation. 9.1 Introduction. 9.2 Extended
State Model of Induction Motor. 9.3 Extended Kalman Filter Algorithm for
Rotor Speed Estimation. 9.4 Optimized Extended Kalman Filter. 9.5
Optimizing the Noise Matrices of EKF Using GA. 9.6 Speed Estimation for a
Sensorless Direct Self Controller. 9.7 Speed Estimation for a
Field-Oriented Controller. 9.8 MATLAB / Simulink Programming Examples. 9.9
Summary. 10 Optimized Random PWM Strategies Based On Genetic Algorithms.
10.1 Introduction. 10.2 PWM Performance Evaluation. 10.3 Random PWM
Methods. 10.4 Optimized Random PWM Based on Genetic Algorithm. 10.5
MATLAB/Simulink Programming Examples. 10.6 Experiments on Various PWM
Strategies. 10.7 Summary. 11 Experimental Investigations. 11.1
Introduction. 11.2 Experimental Hardware Design for Induction Motor
Control. 11.3 Software Development Method. 11.4 Experiment 1: Determination
of Motor Parameters. 11.5 Experiment 2: Induction Motor Run Up. 11.6
Experiment 3: Implementation of Fuzzy/PI Two-Stage Controller. 11.7
Experiment 4: Speed Estimation Using a GA-Optimized Extended Kalman Filter.
11.8 DSP Programming Examples. 11.9 Summary. 12 Conclusions and Future
Developments. 12.1 Main Contributions of the Book. 12.2 Industrial
Applications of New Induction Motor Drives. 12.3 Future Developments.
Appendix A Equivalent Circuits of an Induction Motor. Appendix B Parameters
of Induction Motors. Appendix C M-File of Discrete-State Induction Motor
Model. Appendix D Expert-System Acceleration Control Algorithm. Appendix E
Activation Functions of Neural Network. Appendix F M-File of Extended
Kalman Filter. Appendix G ADMC331-based Experimental System. Appendix H
Experiment 1: Measuring the Electrical Parameters of Motor 3. Appendix I
DSP Source Code for the Main Program of Experiment 2. Appendix J DSP Source
Code for the Main Program of Experiment 3. Index.