Rigatos Intelligent Renewable Energy Systems

Modelling and Control
1. Auflage 2016
ISBN: 978-3-319-39156-4
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 562 Seiten

Reihe: Green Energy and Technology

Focused on renewable energy systems and the development of information and communication technologies (ICTs) for their integration in smart grids, this book presents recent advances and methods that help to ensure that power generation from renewable sources remains stable, that power losses are minimized, and that the reliable functioning of these power generation units is maintained.
The book highlights key topics and technologies for renewable energy systems including the intelligent control of power generators, power electronics that connect renewable power generation units to the grid, and fault diagnosis for power generators and power electronics. In particular, the following topics are addressed:
•Modeling and control of power generators (PMSGs, DFIGs);•Modeling and control of power electronics (converters, inverters); •Modeling and fault diagnosis of the transmission and distribution Grid; and<•Modelling and control of distributed power generation units (interconnected synchronous generators or photovoltaic units).
Because of the above coverage, members of the wider engineering community will find that the nonlinear control and estimation methods presented provide essential insights into the functioning of renewable energy power systems, while the academic community will find the book a valuable textbook for undergraduate or graduate courses on renewable energy systems.

Dr. G. Rigatos, obtained a diploma (1995) and a Ph.D. (2000) both from the Department of Electrical and Computer Engineering, of the National Technical University of Athens (NTUA), Greece. He currently holds a Researcher position at the Industrial Systems Institute (Greek Secretariat for Research and Technology), on the topic of 'Modelling and Control of Industrial Systems'. 
In 2001 he was a post-doctoral researcher at the Institut de Recherche en Informatique et Systèmes Aléatoires IRISA, in Rennes France, while in 2007 he was an invited professor (maître des conférences) at Université Paris XI (Institut d' Electronique Fondamentale). In 2012 he held a Lecturer Position at the Department of Engineering, of Harper-Adams University College, in Shropshire, UK on the topic of 'Mechatronics and Artificial Intelligence'. He has also been an adjunct professor in Greek Universities where he has taught courses on systems and control theory.
His research interests include control and robotics, optimization and fault diagnosis, adaptive systems and computational intelligence. He is editor-in-chief of the Journal of Intelligent Industrial Systems (Springer) which sets among its priorities research on renewable energy systems. He is a Senior member of the IEEE, and member of IET and IMACS.

Autoren/Hrsg.

Rigatos, Gerasimos

Inhaltsverzeichnis

1;Foreword;7
2;Preface;9
3;Acknowledgments;16
4;Contents;17
5;Acronyms;25
6;1 Electric Machines and Power Electronics;26
6.1;1.1 Outline;26
6.2;1.2 Main Types of Power Generators;29
6.2.1;1.2.1 Asynchronous Generators;29
6.2.2;1.2.2 Synchronous Generators;34
6.3;1.3 Main Types of Multi-phase Machines;37
6.3.1;1.3.1 The 6-Phase Synchronous Machine;37
6.3.2;1.3.2 Doubly-Fed Reluctance Machine;40
6.4;1.4 Main Types of Power Electronics;45
6.4.1;1.4.1 Voltage Source Converters;45
6.4.2;1.4.2 Inverters;46
6.4.3;1.4.3 Active Power Filters;49
6.4.4;1.4.4 DC to DC Converters;51
6.4.5;1.4.5 Fuel Cells;52
6.4.6;1.4.6 Batteries;56
6.5;1.5 Components of the Transmission and Distribution System;60
6.5.1;1.5.1 Power Transformers;60
6.5.2;1.5.2 AC Lines;62
6.5.3;1.5.3 HVDC Lines;65
7;2 Control of the Functioning of Doubly-Fed Induction Generators;68
7.1;2.1 Outline;68
7.2;2.2 Flatness-Based Control of the DFIG in Successive Loops;69
7.2.1;2.2.1 Overview;69
7.2.2;2.2.2 Field Orientation for Induction Machines;71
7.2.3;2.2.3 Differential Flatness of the Doubly-Fed Induction Generator;74
7.2.4;2.2.4 Control of the Doubly-Fed Induction Generator;78
7.2.5;2.2.5 Flux and Rotation Speed Estimator;80
7.2.6;2.2.6 Implementation of the EKF for Sensorless Control of the DFIG;82
7.2.7;2.2.7 Estimation of the Wind-Generated Mechanical Torque Using EKF;83
7.2.8;2.2.8 Simulation Tests;85
7.3;2.3 Control of the DFIG Based on Global Linearization Approaches;88
7.3.1;2.3.1 Outline;88
7.3.2;2.3.2 Input-Output Linearization of the DFIG Using Lie Algebra Theory;89
7.3.3;2.3.3 Differential Flatness for Nonlinear Dynamical Systems;92
7.3.4;2.3.4 Input-Output Linearization of the DFIG Using Differential Flatness Theory;93
7.3.5;2.3.5 Kalman Filter-Based Disturbance Observer for the DFIG Model;97
7.3.6;2.3.6 Simulation Tests;99
7.3.7;2.3.7 Input-Output Linearization of the DFIG Model with Use of Lie Algebra;102
7.4;2.4 Nonlinear H-Infinity Control of DFIGs;106
7.4.1;2.4.1 Outline;106
7.4.2;2.4.2 Approximate Linearization of the Doubly-Fed Induction Generator's Dynamic Model;107
7.4.3;2.4.3 The Nonlinear H-Infinity Control;109
7.4.4;2.4.4 Lyapunov Stability Analysis;111
7.4.5;2.4.5 Simulation Tests;114
7.5;2.5 Flatness-Based Adaptive Fuzzy Control of DFIGs;114
7.5.1;2.5.1 Overview;114
7.5.2;2.5.2 Flatness-Based Adaptive Neurofuzzy Control;115
7.5.3;2.5.3 Estimation of the State Vector;120
7.5.4;2.5.4 Application of Flatness-Based Adaptive Neurofuzzy Control to the DFIG;121
7.5.5;2.5.5 Lyapunov Stability Analysis;126
7.5.6;2.5.6 Simulation Tests;133
8;3 Control of the Functioning of Synchronous Generators;136
8.1;3.1 Outline;136
8.2;3.2 Flatness-Based Control of Synchronous Generators;137
8.2.1;3.2.1 Outline;137
8.2.2;3.2.2 Lie Algebra-Based Design of Nonlinear State Estimators;139
8.2.3;3.2.3 Nonlinear Observer Design for Exactly Linearizable Systems;140
8.2.4;3.2.4 Differential Flatness for Nonlinear Dynamical Systems;144
8.2.5;3.2.5 Differential Flatness and Transformation into the Canonical Form;146
8.2.6;3.2.6 Differential Flatness of the Synchronous Generator;147
8.2.7;3.2.7 Robust State Estimation-Based Control of the PMSG;149
8.2.8;3.2.8 Estimation of PMSG Disturbance Input with Kalman Filtering;152
8.2.9;3.2.9 Simulation Experiments;155
8.3;3.3 Flatness-Based Control of Synchronous Generators in Successive Loops;159
8.3.1;3.3.1 Outline;160
8.3.2;3.3.2 Flatness-Based Control Through Transformation into the Canonical Form;161
8.3.3;3.3.3 A New Approach to Flatness-Based Control for Nonlinear Power Systems;162
8.3.4;3.3.4 Closed-Loop Dynamics;165
8.3.5;3.3.5 Comparison to Backstepping Control;167
8.3.6;3.3.6 Simulation Tests;169
8.4;3.4 Stabilizing Control of Synchronous Generators Using Interval Polynomials Theory;170
8.4.1;3.4.1 Outline;170
8.4.2;3.4.2 Stabilization for the Single-Machine Infinite-Bus Model;172
8.4.3;3.4.3 Kharitonov's Stability Theory;175
8.4.4;3.4.4 Design of the Power System Stabilizer;178
8.4.5;3.4.5 Simulation Tests;179
9;4 Control of the Functioning of MultiphaseElectric Machines;183
9.1;4.1 Outline;183
9.2;4.2 Nonlinear H-infinity Control of Multi-phase Electric Machines;184
9.2.1;4.2.1 Overview;184
9.2.2;4.2.2 Dynamic Model of the 6-Phase Synchronous Machine;185
9.2.3;4.2.3 State-Space Description of the 6-Phase PMSM;187
9.2.4;4.2.4 The Nonlinear H-infinity Control;190
9.2.5;4.2.5 Lyapunov Stability Analysis;192
9.2.6;4.2.6 Robust State Estimation with the Use of the Hinfty Kalman Filter;195
9.2.7;4.2.7 Simulation Tests;196
9.3;4.3 An H-infinity Approach to Optimal Control of Doubly-Fed Reluctance Machines;199
9.3.1;4.3.1 Overview;199
9.3.2;4.3.2 Dynamic Model of the Doubly-Fed Reluctance Machine;200
9.3.3;4.3.3 Linearization of the Reluctance Machine's State-Space Models;204
9.3.4;4.3.4 The Nonlinear H-infinity Control;205
9.3.5;4.3.5 Lyapunov Stability Analysis;207
9.3.6;4.3.6 Robust State Estimation with the Use of the Hinfty Kalman Filter;210
9.3.7;4.3.7 Simulation Tests;211
9.4;4.4 Flatness-Based Adaptive Control of Brushless Doubly-Fed Reluctance Machines;214
9.4.1;4.4.1 Overview;214
9.4.2;4.4.2 Outline of the Dynamic Model of the DFRM;215
9.4.3;4.4.3 Differential Flatness Properties of the Reluctance Machine;216
9.4.4;4.4.4 Flatness-Based Adaptive Neurofuzzy Control;219
9.4.5;4.4.5 Application of Flatness-Based Adaptive Neurofuzzy Control to the DFRM;225
9.4.6;4.4.6 Lyapunov Stability Analysis;230
9.4.7;4.4.7 Simulation Tests;235
10;5 Control of the Functioning of DC to DC and AC to DC Converters;237
10.1;5.1 Outline;237
10.2;5.2 Control of DC to DC Converters;238
10.2.1;5.2.1 Overview;238
10.2.2;5.2.2 Differential Flatness of the Model of a DC-DC Converter Connected to a DC Motor;239
10.2.3;5.2.3 Transformation of the Dynamic Model into the Canonical Form;241
10.2.4;5.2.4 Disturbances Compensation with the Derivative-Free Nonlinear Kalman Filter;242
10.2.5;5.2.5 Simulation Tests;245
10.3;5.3 Control of Three-Phase AC to DC Converters;248
10.3.1;5.3.1 Overview;248
10.3.2;5.3.2 Linearization of the Converter's Model Using Lie Algebra;250
10.3.3;5.3.3 Differential Flatness of the Voltage Source Converter;254
10.3.4;5.3.4 Kalman Filter-Based Disturbance Observer for the VSC Model;258
10.3.5;5.3.5 Simulation Tests;260
10.4;5.4 Nonlinear H-infinity Control of VSC;263
10.4.1;5.4.1 Outline;263
10.4.2;5.4.2 Linearization of the Voltage Source Converter's Dynamic Model;264
10.4.3;5.4.3 Nonlinear H-infinity Control for the Three-Phase VSC;266
10.4.4;5.4.4 Lyapunov Stability Analysis;268
10.4.5;5.4.5 Simulation Tests;270
10.5;5.5 Control of the VSC-HVDC Transmission System;274
10.5.1;5.5.1 Outline;274
10.5.2;5.5.2 Lie Algebra-Based Linearization of the VSC-HVDC Dynamics;276
10.5.3;5.5.3 Differential Flatness of the VSC-HVDC System;279
10.5.4;5.5.4 Flatness-Based Control of the VSC-HVDC System;282
10.5.5;5.5.5 Compensation of Disturbances Using the Derivative-Free Nonlinear Kalman Filter;285
10.5.6;5.5.6 Simulation Tests;287
11;6 Control of the Functioning of DC to AC Converters;291
11.1;6.1 Outline;291
11.2;6.2 Flatness-Based Control of Inverters;292
11.2.1;6.2.1 Outline;292
11.2.2;6.2.2 Lie Algebra-Based Control of the Inverter's Model;293
11.2.3;6.2.3 Differential Flatness of the Inverter's Model;297
11.2.4;6.2.4 Flatness-Based Control of the Inverter;300
11.2.5;6.2.5 State and Disturbances Estimation with Nonlinear Kalman Filtering;303
11.2.6;6.2.6 Simulation Tests;304
11.3;6.3 Flatness-Based Adaptive Control of Active Power Filters;307
11.3.1;6.3.1 Overview;307
11.3.2;6.3.2 Dynamic Model of the Active Power Filter;308
11.3.3;6.3.3 Application if Flatness-Based Adaptive Fuzzy Control to Inverters;309
11.3.4;6.3.4 Flatness-Based Adaptive Control for Active Power Filters;312
11.3.5;6.3.5 Lyapunov Stability Analysis for the Active Power Filter;315
11.3.6;6.3.6 Simulation Tests;319
12;7 Control of Fuel Cells and Batteries;321
12.1;7.1 Outline;321
12.2;7.2 Flatness-Based Control of PEM Fuel Cells;322
12.2.1;7.2.1 Outline;322
12.2.2;7.2.2 Linearization of the Fuel Cells Dynamics;323
12.2.3;7.2.3 Linearization of the Fuel Cells Dynamics Using Lie Algebra;328
12.2.4;7.2.4 Flatness-Based Control of the Nonlinear Fuel Cells Dynamics;329
12.2.5;7.2.5 Simulation Tests;332
12.3;7.3 Nonlinear H-Infinity Control of PEM Fuel Cells;334
12.3.1;7.3.1 Overview;334
12.3.2;7.3.2 Linearization of the PEM Fuel Cells Model;334
12.3.3;7.3.3 Design of an H-Infinity Nonlinear Feedback Controller;337
12.3.4;7.3.4 Lyapunov Stability Analysis;339
12.3.5;7.3.5 Simulation Tests;343
12.4;7.4 Control of the Diffusion PDE in Li-ion Batteries;346
12.4.1;7.4.1 Modeling in State-Space Form of the Li-ions Diffusion PDE;351
12.4.2;7.4.2 Differential Flatness of the Battery's PDE Diffusion Model;352
12.4.3;7.4.3 Computation of a Boundary Conditions-Based Feedback Control Law;353
12.4.4;7.4.4 Closed Loop Dynamics;355
12.4.5;7.4.5 State Estimation for the PDE Diffusion Model;357
12.4.6;7.4.6 Simulation Tests;360
13;8 Synchronization and Stabilization of Distributed Power Generation Units;362
13.1;8.1 Outline;362
13.2;8.2 State Estimation-Based Control of Distributed PMSGs;364
13.2.1;8.2.1 Outline;364
13.2.2;8.2.2 Dynamic Model of the Distributed Power Generation Units;366
13.2.3;8.2.3 Linearization of the Distributed Power Generation System Using Lie Algebra;368
13.2.4;8.2.4 Differential Flatness of the Distributed PMSG Model;371
13.2.5;8.2.5 Estimation of PMSG Disturbance Input with Kalman Filtering;375
13.2.6;8.2.6 Simulation Experiments;377
13.3;8.3 Nonlinear H-Infinity Control of Distributed Synchronous Generators;381
13.3.1;8.3.1 Overview;381
13.3.2;8.3.2 Dynamic Model of the Multi-machine Power System;382
13.3.3;8.3.3 Linearization of the Model of the Distributed Synchronous Generators;385
13.3.4;8.3.4 The Nonlinear H-Infinity Control;386
13.3.5;8.3.5 Lyapunov Stability Analysis;388
13.3.6;8.3.6 Robust State Estimation with the Use of the Hinfty Kalman Filter;391
13.3.7;8.3.7 Simulation Tests;392
13.4;8.4 Flatness-Based Adaptive Control of Distributed PMSGs;392
13.4.1;8.4.1 An Adaptive Fuzzy Control for the System of the Distributed Synchronous Generators;395
13.4.2;8.4.2 Flatness-Based Adaptive Fuzzy Control for MIMO Nonlinear Systems;397
13.4.3;8.4.3 Application of Flatness-Based Adaptive Fuzzy Control to the Distributed Power Generators' Model;402
13.4.4;8.4.4 Lyapunov Stability Analysis;407
13.4.5;8.4.5 Simulation Tests;413
13.5;8.5 Control and Synchronization of Distributed Inverters;414
13.5.1;8.5.1 Outline;414
13.5.2;8.5.2 Dynamic Model of the Inverter;419
13.5.3;8.5.3 The Synchronization Problem for Parallel Inverters;420
13.5.4;8.5.4 State and Disturbances Estimation of Parallel Inverters with Nonlinear Kalman Filtering;424
13.5.5;8.5.5 Simulation Tests;426
14;9 Condition Monitoring and Fault Diagnosis for Electric Power Generators;433
14.1;9.1 Outline;433
14.2;9.2 Fault Diagnosis for Distributed Power Generators Using Kalman Filtering;435
14.2.1;9.2.1 Overview;435
14.2.2;9.2.2 Dynamic Model of the Multi-machine Power System;436
14.2.3;9.2.3 Linearization of the Power Generation System Using Differential Flatness Theory;438
14.2.4;9.2.4 Fault Detection with the Use of Statistical Criteria;441
14.2.5;9.2.5 Disturbances Estimation with the Derivative-Free Nonlinear Kalman Filter;443
14.2.6;9.2.6 Simulation Tests;446
14.3;9.3 Neural Network-Based Fault Diagnosis in Distributed Power Generators;448
14.3.1;9.3.1 Outline;449
14.3.2;9.3.2 Power System Faults and Cascading Events;453
14.3.3;9.3.3 Neural Networks for Power System Identification;455
14.3.4;9.3.4 Fault Diagnosis for Electric Power Transmission Systems;458
14.3.5;9.3.5 Simulation Tests;464
14.4;9.4 Fault Diagnosis for Power Generators Using Spectral Analysis Methods;469
14.4.1;9.4.1 Outline;469
14.4.2;9.4.2 Feed-Forward Neural Networks for Nonlinear Systems Modelling;470
14.4.3;9.4.3 Neural Networks Using Hermite Activation Functions;472
14.4.4;9.4.4 Signals Power Spectrum and the Fourier Transform;475
14.4.5;9.4.5 Gauss-Hermite Modeling of Electric Power Generators;478
14.4.6;9.4.6 Fault Diagnosis for Doubly-Fed Induction Generators;480
15;10 Condition Monitoring of the Electric Power Transmission and Distribution System;485
15.1;10.1 Outline;485
15.2;10.2 Fault Diagnosis in Power Transformers Using Statistical Signal Processing;486
15.2.1;10.2.1 Outline;486
15.2.2;10.2.2 Reasons for Failures in Electric Power Transformers;488
15.2.3;10.2.3 Condition Monitoring Methods for Power Transformers;489
15.2.4;10.2.4 Fault Management Practices for Power Transformers;491
15.2.5;10.2.5 Analytical Thermal Model of Electric Power Transformers;491
15.2.6;10.2.6 Neuro-Fuzzy Modelling of Power Transformers' Thermal Condition;493
15.2.7;10.2.7 Simulation Tests;495
15.3;10.3 Distributed Filtering for Condition Monitoring of the Electric Power Grid;499
15.3.1;10.3.1 Outline;499
15.3.2;10.3.2 State of the Art in State Estimation and Fault Diagnosis for the Power Grid;501
15.3.3;10.3.3 Fault Diagnosis in the Power Transmission and Distribution System;505
15.3.4;10.3.4 State Estimation with the Extended Information Filter;506
15.3.5;10.3.5 State Estimation with the Unscented Information Filter;511
15.3.6;10.3.6 State Estimates Fusion with the Covariance Intersection Method;515
15.3.7;10.3.7 Simulation Tests;518
15.3.8;10.3.8 Distributed State Estimation for Detection of Voltage Dips and Harmonics Variation;520
16;Glossary;528
17;References;533
18;Index;557