Power Converters and AC Electrical Drives with Linear Neural Networks: 1st Edition (Paperback) book cover

Power Converters and AC Electrical Drives with Linear Neural Networks

1st Edition

By Maurizio Cirrincione, Marcello Pucci, Gianpaolo Vitale

CRC Press

661 pages | 543 B/W Illus.

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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.


"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

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.


About the Authors

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.

About the Series

Energy, Power Electronics, and Machines

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Subject Categories

BISAC Subject Codes/Headings:
TECHNOLOGY & ENGINEERING / Electronics / General