Power Converters and AC Electrical Drives with Linear Neural NetworksКНИГИ » ТЕХНИЧЕСКИЕ НАУКИ
Название: Power Converters and AC Electrical Drives with Linear Neural Networks (Energy, Power Electronics, and Machines) Автор: Maurizio Cirrincione, Marcello Pucci, Gianpaolo Vitale Издательство: CRC Press Год: 2012 Формат: PDF Страниц: 631 Размер: 22.1 Mb Язык: English
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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Power Converters and AC Electrical Drives with Linear Neural Networks 
