Power Converters and AC Electrical Drives with Linear Neural Networks
: CRC PRESS
- Presents a set of original applications of linear neural networks for electrical drives and power electronics
- Provides a concise theoretical background on power converters, electrical drives, and the adopted linear neural networks
- Supplies simulation and experimental results to validate the theories and help readers better understand the theoretical aspects
- Offers practical and technical information to help readers reproduce the proposed techniques in test set-ups
- Contains more than 530 black-and-white illustrations and a 16-page color insert
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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