Power Converters and AC Electrical Drives with Linear Neural Networks  book cover
1st Edition

Power Converters and AC Electrical Drives with Linear Neural Networks

ISBN 9781138077461
Published March 29, 2017 by CRC Press
661 Pages - 543 B/W Illustrations

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Book Description

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.

Table of Contents

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
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
Generalization of Linear Least Squares Problems

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.


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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