1st Edition

Power Converters and AC Electrical Drives with Linear Neural Networks

    661 Pages 543 B/W Illustrations
    by CRC Press

    661 Pages 543 B/W Illustrations
    by CRC Press

    The first book of its kind, Power Converters and AC Electrical Drives with Linear Neural Networks systematically explores the application of neural networks in the field of power electronics, with particular emphasis on the sensorless control of AC drives. It presents the classical theory based on space-vectors in identification, discusses control of electrical drives and power converters, and examines improvements that can be attained when using linear neural networks.

    The book integrates power electronics and electrical drives with artificial neural networks (ANN). Organized into four parts, it first deals with voltage source inverters and their control. It then covers AC electrical drive control, focusing on induction and permanent magnet synchronous motor drives. The third part examines theoretical aspects of linear neural networks, particularly the neural EXIN family. The fourth part highlights original applications in electrical drives and power quality, ranging from neural-based parameter estimation and sensorless control to distributed generation systems from renewable sources and active power filters. Simulation and experimental results are provided to validate the theories.

    Written by experts in the field, this state-of-the-art book requires basic knowledge of electrical machines and power electronics, as well as some familiarity with control systems, signal processing, linear algebra, and numerical analysis. Offering multiple paths through the material, the text is suitable for undergraduate and postgraduate students, theoreticians, practicing engineers, and researchers involved in applications of ANNs.

    Review of Basic Concepts: Space-Vector Analysis
    Space-Vector Definition
    3 → 2 and 2 → 3 Transformations
    Coordinate Transformation
    Instantaneous Real and Imaginary Powers

    Part I Power Converters

    Pulsewidth Modulation of Voltage Source Inverters
    Fundamentals of Voltage Source Inverters
    Open-Loop PWM
    Closed-Loop Control of VSIs
    List of Symbols
    Further Readings

    Power Quality
    Nonlinear Loads
    Harmonic Propagation on the Distribution Network
    Passive Filters
    Active Power Filters
    List of Symbols

    Part II Electrical Drives

    Dynamic and Steady-State Models of the Induction Machine
    Definition of the Machine Space-Vector Quantities
    Phase Equations of the IM
    Space-Vector Equations in the Stator Reference Frame
    Space-Vector Equations in the Rotor Reference Frame
    Space-Vector Equations in the Generalized Reference Frame
    Mathematical Dynamic Model of the IM Taking into Account the Magnetic Saturation
    Steady-State Space-Vector Model of the IM
    Experimental Validation of the Space-Vector Model of the IM
    IM Model Including Slotting Effects
    List of Symbols

    Control Techniques of Induction Machine Drives
    Introduction on Induction Machine (IM) Control
    Scalar Control of IMs
    FOC of IMs
    DTC of IM
    List of Symbols

    Sensorless Control of Induction Machine Drives
    Introduction on Sensorless Control
    Model-Based Sensorless Control
    Anisotropy-Based Sensorless Control
    Model-Based Sensorless Techniques
    Anisotropy-Based Sensorless Techniques
    Conclusions on Sensorless Techniques for IM Drives

    Permanent Magnet Synchronous Motor Drives
    Space-Vector Model of Permanent Magnet Synchronous Motors
    Control Strategies of PMSM Drives
    Sensorless Control of PMSM Drives
    Appendix: Experimental Test Setup

    Part III Neural Based Orthogonal Regression

    Neural-Based Orthogonal Regression
    Introduction: ADALINE and Least Squares Problems
    Approaches to the Linear Regression
    Minor Component Analysis and the MCA EXIN Neuron
    MCA EXIN Neuron
    TLS EXIN Neuron
    Generalization of Linear Least Squares Problems
    GeMCA EXIN Neuron
    GeTLS EXIN Neuron

    Part IV Selected Applications

    Least Square and Neural Identification of Electrical Machines
    Parameter Estimation of Induction Machines (IMs)
    Sensitivity of the Flux Model to Parameter Variations
    Experimental Analysis of the Effects of Flux Model Detuning on the Control Performance
    Methods for the On-line Tracking of the Machine Parameter Variations
    On-line Estimation of the IM Parameters with the Ordinary Least Squares Method
    Constrained Minimization for Parameter Estimation of IMs in Saturated and Unsaturated Conditions
    Parameter Estimation of an IM with the Total Least Squares Method
    Application of the RLS-Based Parameter Estimation to Flux Model Adaptation in FOC and DTC IM Drives
    Estimation of the IM Parameters at Standstill
    List of Symbols

    Neural-Enhanced Single-Phase DG Systems with APF Capability
    General Operating Principle
    ADALINE Design Criteria
    Building the Current Reference
    Multiresonant Current Controller
    Stability Issues
    Test Rig
    Experimental Results
    APF Connection Procedure

    Neural Sensorless Control of AC Drives
    NN-Based Sensorless Control
    BPN-Based MRAS Speed Observer
    LS-Based MRAS Speed Observer
    TLS EXIN Full-Order Luenberger Adaptive Observer
    MCA EXIN + Reduced-Order Observer
    Appendix A: Implemented Control Schemes
    Appendix B: Description of the Test Setup
    List of Symbols


    All chapters include references.



    Maurizio Cirrincione, PhD, is a full professor of control and signal processing at the University of Technology of Belfort, Montbeliard, France. His current research interests include neural networks, modeling and control, system identification, intelligent control, and electrical machines and drives.

    Marcello Pucci, PhD, is a senior researcher at the Institute of Intelligent Systems for Automation (ISSIA) section of Palermo of the National Research Council of Italy (CNR). His current research interests include electrical machines and drives, power converters, wind and photovoltaic generation systems, intelligent control, and neural networks applications.

    Gianpaolo Vitale is a senior researcher at the Institute of Intelligent Systems for Automation (ISSIA) section of Palermo of the National Research Council of Italy (CNR). He has been professor of power electronics and applied electronics at the University of Palermo, Italy. His current research interests include power electronics, generation from renewables, and related problems of electromagnetic compatibility.

    "I am not aware of [a] book as thorough as the present book. ... I am teaching Power Electronics and Drives Control and I will strongly recommend this book for my students."

    —Prof. Mohamed Benbouzid, LBMS-IUT of Brest, France

    "I sincerely hope that this novel and state-of-the-art book on power electronics and motor drives gets wide and enthusiastic acceptance from the professional community of power electronics consisting of R&D professionals, practicing engineers, university professors, and even graduate students. ... This state-of-the-art book, authored by Maurizio Cirrincione, Marcello Pucci, and Gianpaolo Vitale, is the first book that systematically explores the application of neural networks in the field of power electronics. It emphasizes, particularly, neural network applications in sensorless control of AC drives, including their applications in active power filtering."

    —From the Foreword by Dr. Bimal K. Bose, Life Fellow, IEEE, Condra Chair of Excellence/Emeritus in Power Electronics, Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, USA