Applied Intelligent Control of Induction Motor Drives
Published on 4. March 2011
Book
Hardback
448 pages
978-0-470-82556-3 (ISBN)
Description
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 www.wiley.com/go/chanmotor
More details
Product info
gebunden
Edition
1. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Paper over boards
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 29 mm
Weight
797 gr
ISBN-13
978-0-470-82556-3 (9780470825563)
Schweitzer Classification
Other editions
Additional editions
Tze-Fun Chan | Keli Shi
Applied Intelligent Control of Induction Motor Drives
E-Book
01/2011
Wiley
€118.99
Available for download
Tze-Fun Chan | Keli Shi
Applied Intelligent Control of Induction Motor Drives
E-Book
12/2010
Wiley
€118.99
Available for download
Persons
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.
Content
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.