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.
Author(s): Alfeu J. Sguarezi Filho
Publisher: Elsevier
Year: 2022
Language: English
Pages: 243
City: Amsterdam
Front Cover
Model Predictive Control for Doubly-Fed Induction Generators and Three-Phase Power Converters
Copyright
Contents
List of figures
List of tables
List of contributors
Biography
Abbreviation list
1 Introduction
1.1 Overview
1.2 Structure of the book
2 Induction machine and three-phase power converter dynamic models
2.1 Space vector notation
2.1.1 The stationary reference frame (αβ)
2.1.2 The synchronous reference frame (dq)
2.2 Induction machine dynamic model
2.2.1 IM representation in three-phase systems
2.2.2 IM representation in stationary reference frame (αβ)
2.2.2.1 Electromagnetic torque and power equations
2.2.3 IM representation in synchronous reference frame (dq)
2.2.3.1 Electromagnetic torque and power equations
2.2.4 Speed dynamics representation
2.3 Three-phase power converter connected to the grid dynamic model
2.3.1 Three-phase power CCG representation in three-phase systems
2.3.2 CCG representation in stationary frame (αβ)
2.3.2.1 Power equations
2.3.3 CCG representation in synchronous frame dq
2.3.3.1 Power equations
2.4 Pulse-width-modulation techniques
2.4.1 Sinusoidal PWM
2.4.1.1 Sinusoidal PWM with third harmonic injection
2.4.2 Space vector modulation
2.4.2.1 Scaling of input signals to the SVM algorithm
2.4.2.2 Determination of the reference voltage vector sector
2.4.2.3 Determination of times for PWM signals
2.5 Summary
2.6 Further reading
3 Fundamentals of vector control for DFIG and for the three-phase CCG
3.1 Doubly-fed induction generator
3.1.1 Vector control
3.1.1.1 Frame orientations
3.1.1.2 Transformations employed in this book
3.1.2 Closed loop rotor current control using PI controllers
3.1.3 Deadbeat rotor current control for DFIG
3.1.4 Deadbeat direct power control for DFIG
3.1.4.1 Fundamentals of direct power control
3.1.4.2 Rotor side equations for DPC
3.1.4.3 DPC using deadbeat control for DFIG
3.2 Three-phase power CCG vector control
3.2.1 Filter elements
3.2.2 Vector control fundamentals for the CCG
3.2.2.1 Closed loop grid current control by using PI controllers
3.3 Summary
3.4 Further reading
4 Fundamentals of model predictive control
4.1 Overview
4.1.1 MPC applied in power electronics systems
4.2 Finite control set model predictive control
4.2.1 Principles of finite control set model predictive control
4.3 MPC with modulator (MPC-WM)
4.3.1 Constrains in MPC
4.4 Summary
4.5 Further reading
5 Modulated FCS-MPC for DFIG-DPC
5.1 Representation of DFIG using DPC
5.1.1 Rotor voltage representation
5.2 DPC for DFIG using the modulated FCS-MPC
5.3 Experimental results
5.4 Summary
6 A wireless coded modulated FCS-MPC DPC for renewable energy sources in smart grid environment
6.1 Overview
6.2 Three-phase power CCG using direct power predictive control
6.3 Representation of the wireless communication system
6.4 Analysis of the experimental results
6.4.1 OFDM-CC results
6.4.2 OFDM-LDPC results
6.4.3 Fast Fourier transform analysis
6.5 Summary
7 MPC-WM for doubly-fed induction generator and three-phase CCG
7.1 DFIG rotor current control using MPC-WM
7.1.1 Space state equations
7.1.2 Rotor current control using MPC-WM
7.2 DFIG DPC using MPC-WM
7.2.1 DPC using MPC-WM
7.2.1.1 Experimental results
7.3 Three-phase CCG current control using MPC-WM
7.3.1 Space state equations
7.3.2 Grid current control using MPC-WM
7.3.3 Simulation results
7.3.4 Experimental results
7.4 Information about the choice of weighting matrices and horizons values
7.5 Summary
8 Fundamentals of the model predictive repetitive control
8.1 Fundamentals of repetitive control
8.1.1 IMP for any periodic signal
8.1.2 Basic RC structure and design
8.2 Fundamentals of model predictive repetitive control
8.2.1 Periodic signals representation
8.2.1.1 Signal generator for the MPRC
8.2.2 MPRC technique
8.3 Summary
9 MPRC-WM for DFIG and three-phase CCG operation under voltage distortions
9.1 Representation of voltage distortions
9.2 Model of DFIG under stator distorted voltage
9.2.1 Influence of distorted voltage in the stator active and reactive power representation
9.2.2 Influence of distorted voltage in DC link voltage
9.3 DFIG rotor current control using MPRC-WM
9.3.1 Criterion for choosing polynomial D(z)
9.4 Three-phase power CCG model under grid distorted voltage
9.4.1 Influence of distorted voltage in the active and reactive power representation
9.5 Three-phase power CCG current control using MPRC-WM
9.6 Summary
10 Finite position set phase-locked loop operating under nonideal grid voltages
10.1 PLL fundamentals
10.1.1 PLL for three-phase systems
10.2 Representation of grid voltage disturbances
10.3 Finite position set PLL operation under grid disturbances
10.3.1 Representation of the DSOGI
10.3.2 Representation of the MAF
10.3.3 Finite position set PLL
10.4 Experimental results
10.5 Summary
11 Implementation of DFIG MPC-WM and three-phase power CCG MPRC-WM using Simulink/MATLAB®
11.1 Introduction
11.2 Building embedded functions for Park–Clarke transformation
11.2.1 Park–Clarke transformation
11.2.1.1 Clarke transformation
11.2.1.2 Park transformation
11.2.2 Inverse Park–Clarke transformation
11.2.2.1 Inverse Clarke transformation
11.2.2.2 Inverse Park transformation
11.2.3 Pulse width modulation
11.2.3.1 Sinusoidal pulse width modulation (PWM)
11.2.3.2 Space vector modulation (SVM)
11.2.3.3 Three-phase power converter model
11.2.3.4 Three-phase grid voltages model
11.3 Building simulation model for DFIG
11.3.1 Building simulation model for DFIG using MPC-WM
11.3.2 Building simulation model for DFIG using MPRC-WM
11.4 Building simulation model for three-phase power CCG
11.4.1 Building simulation MPC for power converter
11.5 Summary
12 DFIG and three-phase power CCG experimental setup
12.1 Experimental setups
12.1.1 DFIG setup
12.1.2 Three-phase CCG setup
12.1.3 The PLL setup
12.1.4 Data acquisition, power supply, and DC motor
12.1.5 System initialization
12.1.5.1 System initialization using DFIG
12.1.5.2 System initialization using three-phase CCG
12.2 Information about the microcontroller
12.2.1 Functionality of the microcontroller
12.3 Predictive control implementation
12.4 Summary
A DFIG parameters
B Three-phase power CCG parameters
C DC link voltage representation
Bibliography
Index
Back Cover
