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E-raamat: Model Predictive Control of High Power Converters and Industrial Drives

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  • Ilmumisaeg: 12-Sep-2016
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119010869
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Model Predictive Control of High Power Converters and Industrial DrivesSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 12-Sep-2016
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119010869
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In this original book on model predictive control (MPC) for power electronics, the focus is put on high-power applications with multilevel converters operating at switching frequencies well below 1 kHz, such as medium-voltage drives and modular multi-level converters.

Consisting of two main parts, the first offers a detailed review of three-phase power electronics, electrical machines, carrier-based pulse width modulation, optimized pulse patterns, state-of-the art converter control methods and the principle of MPC. The second part is an in-depth treatment of MPC methods that fully exploit the performance potential of high-power converters. These control methods combine the fast control responses of deadbeat control with the optimal steady-state performance of optimized pulse patterns by resolving the antagonism between the two.

MPC is expected to evolve into the control method of choice for power electronic systems operating at low pulse numbers with multiple coupled variables and tight operating constraints it. Model Predictive Control of High Power Converters and Industrial Drives will enable to reader to learn how to increase the power capability of the converter, lower the current distortions, reduce the filter size, achieve very fast transient responses and ensure the reliable operation within safe operating area constraints.

Targeted at power electronic practitioners working on control-related aspects as well as control engineers, the material is intuitively accessible, and the mathematical formulations are augmented by illustrations, simple examples and a book companion website featuring animations. Readers benefit from a concise and comprehensive treatment of MPC for industrial power electronics, enabling them to understand, implement and advance the field of high-performance MPC schemes.

Preface xvii
Acknowledgments xix
List of Abbreviations
xxi
About the Companion Website xxvii
Part I INTRODUCTION
1 Introduction
3(26)
1.1 Industrial Power Electronics
3(4)
1.1.1 Medium-Voltage, Variable-Speed Drives
3(2)
1.1.2 Market Trends
5(1)
1.1.3 Technology Trends
6(1)
1.2 Control and Modulation Schemes
7(4)
1.2.1 Requirements
7(1)
1.2.2 State-of-the-Art Schemes
8(1)
1.2.3 Challenges
9(2)
1.3 Model Predictive Control
11(8)
1.3.1 Control Problem
11(1)
1.3.2 Control Principle
12(4)
1.3.3 Advantages and Challenges
16(3)
1.4 Research Vision and Motivation
19(1)
1.5 Main Results
19(2)
1.6 Summary of this Book
21(4)
1.7 Prerequisites
25(4)
References
26(3)
2 Industrial Power Electronics
29(48)
2.1 Preliminaries
29(13)
2.1.1 Three-Phase Systems
29(2)
2.1.2 Per Unit System
31(2)
2.1.3 Stationary Reference Frame
33(3)
2.1.4 Rotating Reference Frame
36(4)
2.1.5 Space Vectors
40(2)
2.2 Induction Machines
42(9)
2.2.1 Machine Model in Space Vector Notation
42(2)
2.2.2 Machine Model in Matrix Notation
44(1)
2.2.3 Machine Model in the Per Unit System
45(3)
2.2.4 Machine Model in State-Space Representation
48(2)
2.2.5 Harmonic Model of the Machine
50(1)
2.3 Power Semiconductor Devices
51(3)
2.3.1 Integrated-Gate-Commutated Thyristors
51(2)
2.3.2 Power Diodes
53(1)
2.4 Multilevel Voltage Source Inverters
54(14)
2.4.1 NPC Inverter
54(8)
2.4.2 Five-Level ANPC Inverter
62(6)
2.5 Case Studies
68(9)
2.5.1 NPC Inverter Drive System
68(2)
2.5.2 NPC Inverter Drive System with Snubber Restrictions
70(1)
2.5.3 Five-Level ANPC Inverter Drive System
71(1)
2.5.4 Grid-Connected NPC Converter System
72(3)
References
75(2)
3 Classic Control and Modulation Schemes
77(76)
3.1 Requirements of Control and Modulation Schemes
77(7)
3.1.1 Requirements Relating to the Electrical Machine
11(69)
3.1.2 Requirements Relating to the Grid
80(3)
3.1.3 Requirements Relating to the Converter
83(1)
3.1.4 Summary
83(1)
3.2 Structure of Control and Modulation Schemes
84(1)
3.3 Carrier-Based Pulse Width Modulation
85(18)
3.3.1 Single-Phase Carrier-Based Pulse Width Modulation
86(8)
3.3.2 Three-Phase Carrier-Based Pulse Width Modulation
94(7)
3.3.3 Summary and Properties
101(2)
3.4 Optimized Pulse Patterns
103(14)
3.4.1 Pulse Pattern and Harmonic Analysis
104(3)
3.4.2 Optimization Problem for Three-Level Converters
107(5)
3.4.3 Optimization Problem for Five-Level Converters
112(5)
3.4.4 Summary and Properties
117(1)
3.5 Performance Trade-Off for Pulse Width Modulation
117(4)
3.5.1 Current TDD versus Switching Losses
118(2)
3.5.2 Torque TDD versus Switching Losses
120(1)
3.6 Control Schemes for Induction Machine Drives
121(32)
3.6.1 Scalar Control
122(1)
3.6.2 Field-Oriented Control
123(7)
3.6.3 Direct Torque Control
130(9)
Appendix 3.A Harmonic Analysis of Single-Phase Optimized Pulse Patterns
139(2)
Appendix 3.B Mathematical Optimization
141(1)
3.B.1 General Optimization Problems
142(1)
3.B.2 Mixed-Integer Optimization Problems
142(1)
3.B.3 Convex Optimization Problems
143(2)
References
145(8)
Part II DIRECT MODEL PREDICTIVE CONTROL WITH REFERENCE TRACKING
4 Predictive Control with Short Horizons
153(42)
4.1 Predictive Current Control of a Single-Phase RL Load
153(11)
4.1.1 Control Problem
153(1)
4.1.2 Prediction of Current Trajectories
154(2)
4.1.3 Optimization Problem
156(1)
4.1.4 Control Algorithm
156(2)
4.1.5 Performance Evaluation
158(3)
4.1.6 Prediction Horizons of more than 1 Step
161(2)
4.1.7 Summary
163(1)
4.2 Predictive Current Control of a Three-Phase Induction Machine
164(19)
4.2.1 Case Study
164(1)
4.2.2 Control Problem
165(1)
4.2.3 Controller Model
166(1)
4.2.4 Optimization Problem
167(1)
4.2.5 Control Algorithm
168(2)
4.2.6 Performance Evaluation
170(5)
4.2.7 About the Choice of Norms
175(3)
4.2.8 Delay Compensation
178(5)
4.3 Predictive Torque Control of a Three-Phase Induction Machine
183(10)
4.3.1 Case Study
183(1)
4.3.2 Control Problem
184(1)
4.3.3 Controller Model
184(1)
4.3.4 Optimization Problem
185(1)
4.3.5 Control Algorithm
186(1)
4.3.6 Analysis of the Cost Function
187(1)
4.3.7 Comparison of the Cost Functions for the Torque and Current Controllers
188(3)
4.3.8 Performance Evaluation
191(2)
4.4 Summary
193(2)
References
194(1)
5 Predictive Control with Long Horizons
195(22)
5.1 Preliminaries
196(5)
5.1.1 Case Study
196(1)
5.1.2 Controller Model
197(1)
5.1.3 Cost Function
197(1)
5.1.4 Optimization Problem
198(2)
5.7.5 Control Algorithm based on Exhaustive Search
200(1)
5.2 Integer Quadratic Programming Formulation
201(3)
5.2.1 Optimization Problem in Vector Form
201(1)
5.2.2 Solution in Terms of the Unconstrained Minimum
202(1)
5.2.3 Integer Quadratic Program
202(1)
5.2.4 Direct MPC with a Prediction Horizon of 1
203(1)
5.3 An Efficient Method for Solving the Optimization Problem
204(7)
5.3.1 Preliminaries and Key Properties
205(1)
5.3.2 Modified Sphere Decoding Algorithm
205(2)
5.3.3 Illustrative Example with a Prediction Horizon of 1
207(2)
5.3.4 Illustrative Example with a Prediction Horizon of 2
209(2)
5.4 Computational Burden
211(6)
5.4.1 Offline Computations
211(1)
5.4.2 Online Preprocessing
211(1)
5.4.3 Sphere Decoding
212(1)
Appendix 5.A State-Space Model
213(1)
Appendix 5.B Derivation of the Cost Function in Vector Form
214(2)
References
216(1)
6 Performance Evaluation of Predictive Control with Long Horizons
217(38)
6.1 Performance Evaluation for the NPC Inverter Drive System
218(14)
6.1.1 Framework for Performance Evaluation
218(2)
6.1.2 Comparison at the Switching Frequency 250 Hz
220(3)
6.1.3 Closed-Loop Cost
223(2)
6.7.4 Relative Current TDD
225(6)
6.1.5 Operation during Transients
231(1)
6.2 Suboptimal MPC via Direct Rounding
232(2)
6.3 Performance Evaluation for the NPC Inverter Drive System with an LC Filter
234(11)
6.3.1 Case Study
235(2)
6.3.2 Controller Model
237(1)
6.3.3 Optimization Problem
237(2)
6.3.4 Steady-State Operation
239(4)
6.3.5 Operation during Transients
243(2)
6.4 Summary and Discussion
245(10)
6.4.1 Performance at Steady-State Operating Conditions
245(1)
6.4.2 Performance during Transients
246(1)
6.4.3 Cost Function
246(1)
6.4.4 Control Objectives
247(1)
6.4.5 Computational Complexity
247(1)
Appendix 6.A State-Space Model
248(1)
Appendix 6.B Computation of the Output Reference Vector
248(1)
6.B.1 Step 1: Stator Frequency
248(1)
6.B.2 Step 2: Inverter Voltage
249(1)
6.B.3 Step 3: Output Reference Vector
250(1)
References
251(4)
Part III DIRECT MODEL PREDICTIVE CONTROL WITH BOUNDS
7 Model Predictive Direct Torque Control
255(34)
7.1 Introduction
255(2)
7.2 Preliminaries
257(6)
7.2.1 Case Study
257(2)
7.2.2 Control Problem
259(1)
7.2.3 Controller Model
259(3)
7.2.4 Switching Effort
262(1)
7.3 Control Problem Formulation
263(3)
7.3.1 Naive Optimization Problem
263(1)
7.3.2 Constraints
264(1)
7.3.3 Cost Function
265(1)
7.4 Model Predictive Direct Torque Control
266(11)
7.4.1 Definitions
267(1)
7.4.2 Simplified Optimization Problem
268(1)
7.4.3 Concept of the Switching Horizon
268(6)
7.4.4 Search Tree
274(1)
7.4.5 MPDTC Algorithm with Full Enumeration
275(2)
7.5 Extension Methods
277(7)
7.5.1 Analysis of the State and Output Trajectories
278(1)
7.5.2 Linear Extrapolation
279(1)
7.5.3 Quadratic Extrapolation
280(2)
7.5.4 Quadratic Interpolation
282(2)
7.6 Summary and Discussion
284(5)
Appendix 7.A Controller Model of the NPC Inverter Drive System
286(1)
References
287(2)
8 Performance Evaluation of Model Predictive Direct Torque Control
289(29)
8.1 Performance Evaluation for the NPC Inverter Drive System
289(11)
8.1.1 Simulation Setup
290(1)
8.1.2 Steady-State Operation
290(8)
8.1.3 Operation during Transients
298(2)
8.2 Performance Evaluation for the ANPC Inverter Drive System
300(14)
8.2.1 Controller Model
301(2)
8.2.2 Modified MPDTC Algorithm
303(1)
8.2.3 Simulation Setup
304(1)
8.2.4 Steady-State Operation
305(7)
8.2.5 Operation during Transients
312(2)
8.3 Summary and Discussion
314(4)
Appendix 8.A Controller Model of the ANPC Inverter Drive System
315(1)
References
316(2)
9 Analysis and Feasibility of Model Predictive Direct Torque Control
318(32)
9.1 Target Set
319(1)
9.2 The State-Feedback Control Law
320(11)
9.2.1 Preliminaries
321(1)
9.2.2 Control Law for a Given Rotor Flux Vector
322(9)
9.2.3 Control Law along an Edge of the Target Set
331(1)
9.3 Analysis of the Deadlock Phenomena
331(6)
9.3.1 Root Cause Analysis of Deadlocks
332(3)
9.3.2 Location of Deadlocks
335(2)
9.4 Deadlock Resolution
337(3)
9.5 Deadlock Avoidance
340(7)
9.5.1 Deadlock Avoidance Strategies
340(3)
9.5.2 Performance Evaluation
343(4)
9.6 Summary and Discussion
347(3)
9.6.1 Derivation and Analysis of the State-Feedback Control Law
347(1)
9.6.2 Deadlock Analysis, Resolution, and Avoidance
347(1)
References
348(2)
10 Computationally Efficient Model Predictive Direct Torque Control
350(19)
10.1 Preliminaries
351(1)
10.2 MPDTC with Branch-and-Bound
352(7)
10.2.1 Principle and Concept
352(2)
10.2.2 Properties of Branch-and-Bound
354(2)
10.2.3 Limiting the Maximum Number of Computations
356(1)
10.2.4 Computationally Efficient MPDTC Algorithm
357(2)
10.3 Performance Evaluation
359(8)
10.3.1 Case Study
359(1)
10.3.2 Performance Metrics during Steady-State Operation
359(4)
10.3.3 Computational Metrics during Steady-State Operation
363(4)
10.4 Summary and Discussion
367(2)
References
368(1)
11 Derivatives of Model Predictive Direct Torque Control
369(46)
11.1 Model Predictive Direct Current Control
370(19)
11.1.1 Case Study
370(2)
11.1.2 Control Problem
372(1)
11.1.3 Formulation of the Stator Current Bounds
373(3)
11.1.4 Controller Model
376(2)
11.1.5 Control Problem Formulation
378(1)
11.1.6 MPDCC Algorithm
379(1)
11.1.7 Performance Evaluation
380(8)
11.1.8 Tuning
388(1)
11.2 Model Predictive Direct Power Control
389(12)
11.2.1 Case Study
391(1)
11.2.2 Control Problem
392(1)
11.2.3 Controller Model
393(1)
11.2.4 Control Problem Formulation
394(1)
11.2.5 Performance Evaluation
395(6)
11.3 Summary and Discussion
401(14)
11.3.1 Model Predictive Direct Current Control
401(2)
11.3.2 Model Predictive Direct Power Control
403(1)
11.3.3 Target Sets
403(2)
Appendix 11.A Controller Model used in MPDCC
405(2)
Appendix 11.B Real and Reactive Power
407(2)
Appendix 11.C Controller Model used in MPDPC
409(1)
References
410(5)
Part IV MODEL PREDICTIVE CONTROL BASED ON PULSE WIDTH MODULATION
12 Model Predictive Pulse Pattern Control
415(32)
12.1 State-of-the-Art Control Methods
415(1)
12.2 Optimized Pulse Patterns
416(6)
12.2.1 Summary, Properties, and Computation
416(2)
12.2.2 Relationship between Flux Magnitude and Modulation Index
418(1)
12.2.3 Relationship between Time and Angle
419(1)
12.2.4 Stator Flux Reference Trajectory
420(2)
12.2.5 Look-Up Table
422(1)
12.3 Stator Flux Control
422(3)
12.3.1 Control Objectives
422(1)
12.3.2 Control Principle
422(1)
12.3.3 Control Problem
423(1)
12.3.4 Control Approach
424(1)
12.4 MP3C Algorithm
425(8)
12.4.1 Observer
426(2)
12.4.2 Speed Controller
428(1)
12.4.3 Torque Controller
428(1)
12.4.4 Flux Controller
428(1)
12.4.5 Pulse Pattern Loader
429(1)
12.4.6 Flux Reference
429(1)
12.4.7 Pulse Pattern Controller
429(4)
12.5 Computational Variants of MP3C
433(5)
12.5.1 MP3C based on Quadratic Program
433(4)
72.5.2 MP3C based on Deadbeat Control
437(1)
12.6 Pulse Insertion
438(9)
12.6.1 Definitions
439(1)
12.6.2 Algorithm
439(4)
Appendix 12.A Quadratic Program
443(1)
Appendix 12.B Unconstrained Solution
444(1)
Appendix 12.C Transformations for Deadbeat MP3C
445(1)
References
446(1)
13 Performance Evaluation of Model Predictive Pulse Pattern Control
447(27)
13.1 Performance Evaluation for the NPC Inverter Drive System
447(15)
13.1.1 Simulation Setup
447(1)
13.1.2 Steady-State Operation
448(7)
13.1.3 Operation during Transients
455(7)
13.2 Experimental Results for the ANPC Inverter Drive System
462(6)
13.2.1 Experimental Setup
462(1)
13.2.2 Hierarchical Control Architecture
463(2)
13.2.3 Steady-State Operation
465(3)
13.3 Summary and Discussion
468(6)
13.3.1 Differences to the State of the Art
469(3)
13.3.2 Discussion All References
472(2)
14 Model Predictive Control of a Modular Multilevel Converter
474(33)
14.1 Introduction
474(1)
14.2 Preliminaries
475(4)
14.2.1 Topology
475(2)
14.2.2 Nonlinear Converter Model
477(2)
14.3 Model Predictive Control
479(7)
14.3.1 Control Problem
479(1)
14.3.2 Controller Structure
480(1)
14.3.3 Linearized Prediction Model
481(1)
14.3.4 Cost Function
481(2)
14.3.5 Hard and Soft Constraints
483(1)
14.3.6 Optimization Problem
484(1)
14.3.7 Multilevel Carrier-Based Pulse Width Modulation
485(1)
14.3.8 Balancing Control
486(1)
14.4 Performance Evaluation
486(10)
14.4.1 System and Control Parameters
486(2)
14.4.2 Steady-State Operation
488(3)
14.4.3 Operation during Transients
491(5)
14.5 Design Parameters
496(3)
14.5.1 Open-Loop Prediction Errors
496(2)
14.5.2 Closed-Loop Performance
498(1)
14.6 Summary and Discussion
499(8)
Appendix 14.A Dynamic Current Equations
501(1)
Appendix 14.B Controller Model of the Converter System
501(2)
References
503(4)
Part V SUMMARY
15 Summary and Conclusion
507(18)
15.1 Performance Comparison of Direct Model Predictive Control Schemes
507(12)
15.1.1 Case Study
508(1)
15.1.2 Performance Trade-Off Curves
508(7)
15.1.3 Summary and Discussion
515(4)
15.2 Assessment of the Control and Modulation Methods
519(5)
15.2.1 FOC and VOC with SVM
519(1)
15.2.2 DTC and DPC
519(1)
15.2.3 Direct MPC with Reference Tracking
520(1)
15.2.4 Direct MPC with Bounds
521(1)
15.2.5 MP3C based on OPPs
521(2)
15.2.6 Indirect MPC
523(1)
15.3 Conclusion
524(1)
15.4 Outlook
525(1)
References 525(2)
Index 527
Tobias Geyer, ABB Corporate Research Center, Switzerland Tobias Geyer joined ABB's Corporate Research Center as a deputy group leader and principal scientist in 2012. In this role, he is building up a dedicated research team focusing on Model predictive control (MPC) for power electronic systems. After obtaining his PhD at ETH Zurich, he spent three years in GE's Corporate Research Center in Munich as a project leader for high-power electronics and drives. He subsequently worked at the intersection of academia and industrial research, fully funded by ABB and part of an ABB research team, whilst being employed by the University of Auckland as a Research Fellow. During this time, his focus was on the development of a new generation of drive control schemes that is intended to replace ABB's currently used schemes in their medium-voltage drive portfolio. Tobias Geyer has been working on MPC for power electronics since 2002, and was one of the first researchers who began working in this field. During the past 12 years he has written approximately 100 peer-reviewed journal and conference publications and patent applications. He is also an Associate Editor of Transactions on Power Electronics and Transactions on Industry Applications.
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