A well-rounded resource for professionals and students at all levels, this state-of-the-art book systematically explores the application of artificial neural networks in power electronics, with particular emphasis on the sensorless control of AC drives, including for active power filtering. The authors briefly review space-vector theory and then cover voltage source inverters and their control, as well as AC electrical drive control. The core of the book examines specific original applications of linear neural networks. Simulation and experimental results are provided to validate the theories.

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.

Review of Basic Concepts: Space-Vector Analysis. Part I Power Converters: Pulsewidth Modulation of Voltage Source Inverters. Power Quality. Part II Electrical Drives: Dynamic and Steady-State Models of the Induction Machine. Control Techniques of Induction Machine Drives. Sensorless Control of Induction Machine Drives. Permanent Magnet Synchronous Motor Drives. Part III Neural Based Orthogonal Regression: Neural-Based Orthogonal Regression. Part IV Selected Applications: Least Square and Neural Identification of Electrical Machines. Neural-Enhanced Single-Phase DG Systems with APF Capability. Neural Sensorless Control of AC Drives. Index.