Doubly Fed Induction Generators (e-bog) af Ruiz-Cruz, Riemann
Ruiz-Cruz, Riemann (forfatter)

Doubly Fed Induction Generators e-bog

2738,46 DKK (ekskl. moms 2190,77 DKK)
Doubly Fed Induction Generators: Control for Wind Energy provides a detailed source of information on the modeling and design of controllers for the doubly fed induction generator (DFIG) used in wind energy applications. Focusing on the use of nonlinear control techniques, this book:Discusses the main features and advantages of the DFIGDescribes key theoretical fundamentals and the DFIG mathemati…
Doubly Fed Induction Generators: Control for Wind Energy provides a detailed source of information on the modeling and design of controllers for the doubly fed induction generator (DFIG) used in wind energy applications. Focusing on the use of nonlinear control techniques, this book:Discusses the main features and advantages of the DFIGDescribes key theoretical fundamentals and the DFIG mathematical modelDevelops controllers using inverse optimal control, sliding modes, and neural networksDevises an improvement to add robustness in the presence of parametric variationsDetails the results of real-time implementationsAll controllers presented in the book are tested in a laboratory prototype. Comparisons between the controllers are made by analyzing statistical measures applied to the control objectives.
E-bog 2738,46 DKK
Forfattere Ruiz-Cruz, Riemann (forfatter)
Forlag CRC Press
Udgivet 05.08.2016
Længde 148 sider
Genrer THX
Sprog English
Format epub
Beskyttelse LCP
ISBN 9781315351735
Doubly Fed Induction Generators: Control for Wind Energy provides a detailed source of information on the modeling and design of controllers for the doubly fed induction generator (DFIG) used in wind energy applications. Focusing on the use of nonlinear control techniques, this book:Discusses the main features and advantages of the DFIGDescribes key theoretical fundamentals and the DFIG mathematical modelDevelops controllers using inverse optimal control, sliding modes, and neural networksDevises an improvement to add robustness in the presence of parametric variationsDetails the results of real-time implementationsAll controllers presented in the book are tested in a laboratory prototype. Comparisons between the controllers are made by analyzing statistical measures applied to the control objectives.