Model Predictive Control for Doubly-Fed Induction Generators and Three-Phase Power Converters

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Model Predictive Control for Doubly-Fed Induction Generators and Three-Phase Power Converters describes the application of model predictive control techniques with modulator and finite control sets to squirrel cage induction motor and in doubly-fed induction generators using field orientation control techniques as both current control and direct power control. Sections discuss induction machines, their key modulation techniques, introduce the utility of model predictive control, review core concepts of vector control, direct torque control, and direct power control alongside novel approaches of MPC. Mathematical modeling of cited systems, MPC theory, their applications, MPC design and simulation in MATLAB are also considered in-depth.

The work concludes by addressing implementation considerations, including generator operation under voltage sags or distorted voltage and inverters connected to the grid operating under distorted voltage. Experimental results are presented in full.


Autorentext
Alfeu J. Sguarezi Filho received the master's and Ph.D. degrees from Campinas University, Campinas, Brazil, in 2007 and 2010, respectively. He is an IEEE Senior member. Since 2012, he is a Professor with the Federal University of ABC, Santo André, Brazil, where he teaches on electrical machines, power electronics, and electrical drives. His current research interests include machine drives, wind and photovoltaic energies, doubly fed induction generators, power control, predictive control and electrical power systems. He has published more than 100 papers in journals and conferences, 9 book chapters and he was the advisor of 8 Phd and 26 Master theses.

Inhalt

  1. Induction machine and power converter models
    1. Fundamentals of Vector Control
    2. Fundamentals of model predictive control (MPC)
    3. MPC-WM of Doubly-fed induction generator and power converter
    4. FCS of induction machines
    5. New MPC approach - Repetitive MPC (RMPC)
    6. Simulink/Matlab implementation

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09780323909648
    • Genre Electrical Engineering
    • Sprache Englisch
    • Herausgeber Elsevier Science & Technology
    • Größe H229mm x B152mm x T20mm
    • Jahr 2022
    • EAN 9780323909648
    • Format Kartonierter Einband
    • ISBN 978-0-323-90964-8
    • Veröffentlichung 24.01.2022
    • Titel Model Predictive Control for Doubly-Fed Induction Generators and Three-Phase Power Converters
    • Autor Alfeu Sguarezi
    • Gewicht 410g

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