Description
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 DFIG
- Describes key theoretical fundamentals and the DFIG mathematical model
- Develops controllers using inverse optimal control, sliding modes, and neural networks
- Devises an improvement to add robustness in the presence of parametric variations
- Details the results of real-time implementations
All 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.
Table of Contents
Introduction
Introduction and Overview of Recent Research
Book Structure
Notation
Mathematical Preliminaries
Block Control
Sliding Modes
Optimal Control and Inverse Optimal Control
Discrete Time High-Order Neural Networks
EKF Training Algorithm
Neural Control
Particle Swarm Optimization
Modeling of Wind Turbines
Wind Energy Generation Systems
Discrete Time Mathematical Models
DFIG Control for Renewable Energy Systems
Block Control Sliding Modes
Inverse Optimal Control
Neural Network Control of Wind Turbine Induction Generators
Neural Identifiers
Neural Sliding Modes Block Control
Neural Inverse Optimal Control
Implementation of Wind Energy Testbed
Real-Time Controller Programing
Doubly Fed Induction Generator Prototype
Sliding Modes Real-Time Results
Neural Sliding Modes Real-Time Results
Neural Inverse Optimal Control Real-Time Results
Appendix A: Particle Swarm Optimization for Control Algorithms
Particle Swarm Optimization for Inverse Optimal Control
Particle Swarm Optimization for Neural Networks
Appendix B: DFIG Modeling
DFIG Mathematical Model
DC Link Mathematical Model
References
