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portada Power Converters and AC Electrical Drives with Linear Neural Networks
Type
Physical Book
Author
Publisher
Language
English
Pages
661
Format
Paperback
ISBN13
9781138077461
Edition No.
1
Categories

Power Converters and AC Electrical Drives with Linear Neural Networks

Gianpaolo Vitale (Author) · Maurizio Cirrincione (Author) · Marcello Pucci (Author) · CRC Press · Paperback

Power Converters and AC Electrical Drives with Linear Neural Networks - Cirrincione, Maurizio ; Pucci, Marcello ; Vitale, Gianpaolo

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Synopsis "Power Converters and AC Electrical Drives with Linear Neural Networks"

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.

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