Intelligent Renewable Energy Systems (eBook)

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.

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