book

Power Converters and AC Electrical Drives with Linear Neural Networks

by Maurizio Cirrincione, Marcello Pucci, Gianpaolo Vitale

December 2017

Intermediate to advanced

661 pages

19h 46m

English

CRC Press

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Cover Page
Title Page
Copyright Page
Dedication Page
Table of Contents
Foreword
Preface
Acknowledgments
Authors
1 Review of Basic Concepts: Space-Vector Analysis
1.1 Introduction1.2 Space-Vector Definition1.3 3 → 2 and 2 → 3 Transformations1.3.1 Non-Power-Invariant Form Ver.11.3.2 Power-Invariant Form1.3.3 Non-Power-Invariant Form Ver.21.4 Coordinate Transformation1.5 Instantaneous Real and Imaginary Powers

Part I Power Converters
2 Pulsewidth Modulation of Voltage Source Inverters
2.1 Fundamentals of Voltage Source Inverters2.1.1 Performance Criteria2.1.1.1 Current Harmonics2.1.1.2 Harmonic Spectrum2.1.1.3 Maximum Modulation Index2.1.1.4 Torque Harmonics2.1.1.5 Switching Frequency and Switching Losses2.1.1.6 Common-Mode Voltage ( CMV )2.2 Open-Loop PWM2.2.1 Carrier-Based PWM2.2.1.1 Suboscillation Method2.2.1.2 Modified Suboscillation Method2.2.1.3 Sampling Techniques2.2.1.4 Space-Vector Modulation2.2.1.5 Discontinuous Pulsewidth Modulation2.2.1.6 General Expression of the Duty Cycle in Carrier-Based PWM Techniques2.2.1.7 Modified Space-Vector Modulation2.2.1.8 Synchronized Carrier Modulation2.2.2 Carrierless PWM2.2.3 Overmodulation2.2.4 SV-PWM for the Minimization of the Common-Mode Emissions2.2.4.1 Common-Mode Voltage2.2.4.2 Switching Strategy2.2.5 Optimized Open-Loop PWM2.2.6 Experimental Verification of Open-Loop PWM Techniques2.3 Closed-Loop Control of VSIs2.3.1 Classification of Closed-Loop Control Strategy2.3.1.1 Hysteresis Current Control2.3.1.2 Suboscillation Current Control2.3.1.3 Space-Vector Current Control2.3.1.3.1 Stationary Vector Controller2.3.1.3.2 Synchronous Vector Controller2.3.1.3.3 Stationary Resonant Controller2.3.1.4 Closed-Loop PWM with Real-Time Optimization2.3.1.5 Predictive Current Control2.3.1.6 Pulsewidth Control with Field Orientation2.3.1.7 Trajectory Tracking Control2.3.2 From the Six-Pulse Rectifier to the Active Rectifier2.3.3 Current Control of VSIs2.3.3.1 Voltage-Oriented Control ( VOC )2.3.3.1.1 Circuital Analysis of the Active Rectifier2.3.3.1.2 Stationary Reference Frame Analysis2.3.3.1.3 Synchronous Reference Frame Analysis2.3.3.2 Virtual Flux-Oriented Control2.3.3.3 Experimental Results with VOC and VF-OC2.3.4 Power Control of VSIs2.3.4.1 Direct Power Control2.3.4.2 Switching Table DPC2.3.4.3 Virtual Flux Direct Power Control2.3.4.4 Experimental Results with DPC and VF-DPC2.3.4.5 DPC EMC (Electromagnetic Compatible)2.3.4.6 Simulation Results with DPC-EMC 1 and DPC-EMC 22.3.4.7 Comparative Experimental Analysis of DPC, DPC-EMC 1, and DPC-EMC 22.3.4.8 Standard Compliance: Comparative Analysis
3 Power Quality
3.1 Nonlinear Loads3.1.1 Current Source Type of Harmonic Sources (Harmonic Current Sources)3.1.2 Voltage Source Type of Harmonic Sources (Harmonic Voltage Sources)3.2 Harmonic Propagation on the Distribution Network3.3 Passive Filters3.4 Active Power Filters3.4.1 Introduction on APFs3.4.2 Basic Operating Issues of Parallel and Series Filters3.4.3 Shunt Active Filters3.4.3.1 Harmonic Current Source Compensation by PAF3.4.3.2 Harmonic Voltage Source Compensation by PAF3.4.3.3 Control of PAF Based on p-q Theory3.4.3.3.1 PWM Converters for Shunt Active Filters3.4.3.3.2 Active Filter Controllers Using p-q Theory3.4.3.3.3 Constant Instantaneous Power Control Strategy3.4.3.3.4 Sinusoidal Current Control Strategy3.4.3.3.5 Generalized Fryze Current Control Strategy3.4.4 Series Active Filters3.4.4.1 Harmonic Current Loads3.4.4.2 Voltage Harmonic Loads3.4.4.3 Control of SAF Based on the p-q Theory3.4.5 Comparison between PAFs and SAFs3.4.6 Hybrid Active Filters
Part II Electrical Drives
4 Dynamic and Steady-State Models of the Induction Machine
4.1 Introduction4.2 Definition of the Machine Space-Vector Quantities4.3 Phase Equations of the IM4.4 Space-Vector Equations in the Stator Reference Frame4.5 Space-Vector Equations in the Rotor Reference Frame4.6 Space-Vector Equations in the Generalized Reference Frame4.6.1 Mutually Coupled Magnetic Circuits4.6.2 Space-Vector Equations in the Rotor Flux-Linkage Reference Frame4.6.3 Space-Vector Equations in the Stator Flux-Linkage Reference Frame4.6.4 Space-Vector Equations in the Magnetic Flux-Linkage Reference Frame4.7 Mathematical Dynamic Model of the IM Taking into Account Magnetic Saturation4.8 Steady-State Space-Vector Model of the IM4.9 Experimental Validation of the Space-Vector Model of the IM4.10 IM Model Including Slotting Effects4.10.1 Space-Vector Model of the IMs Including Stator and Rotor Slotting Effects4.10.2 Space-Vector State Model of IM Including Rotor Slotting Effects4.10.3 Space-State Model of IM Including Rotor Slotting Effects4.10.4 Space-State Model of IM Including Stator and Rotor Slotting Effects4.10.5 Experimental Validation of the Space-Vector Model Considering the Stator and Rotor Slotting Effects4.10.5.1 Stator Current Harmonics Caused by the Stator and Rotor Slotting Effect4.10.5.2 ResultsList of Symbols
5 Control Techniques of Induction Machines Drives
5.1 Introduction on Induction Machine (IM) Control5.2 Scalar Control of IMs5.2.1 Scalar Control with Impressed Voltages5.2.1.1 Experimental Results with Open-Loop Scalar Control with Impressed Voltages5.2.2 Scalar Control with Impressed Currents5.3 FOC of IMs5.3.1 Principle of Field-Oriented Control5.3.2 Rotor Flux-Oriented Control5.3.3 Rotor Flux-Linkage Acquisition5.3.3.1 Voltage Flux Models5.3.3.2 Current Flux Models5.3.3.3 Rotor Flux-Oriented Control with Impressed Currents5.3.3.4 Rotor Flux-Oriented Control with Impressed Voltages5.3.3.5 Experimental Results on a Rotor Flux-Oriented Drive with Impressed Voltages5.3.4 Stator Flux-Oriented Control5.3.4.1 Stator Flux-Linkage Acquisition5.3.5 Magnetizing Flux-Oriented Control5.4 DTC of IM5.4.1 Electromagnetic Torque Generation in the IM5.4.2 Relationship between the Stator Flux-Linkage Space-Vector and the Inverter Configurations5.4.3 Criteria for the Selection of the Voltage Space-Vectors and Control Strategies5.4.4 Estimation of the Stator Flux and Electromagnetic Torque5.4.5 DTC Scheme5.4.6 DTC EMC5.4.6.1 Common-Mode Voltage5.4.6.2 Switching Strategy5.4.6.3 Common-Mode Voltage Spectrum of DTC EMC5.4.7 Experimental Results with Classic DTC and DTC EMC5.4.8 DTC-SVM5.4.9 Experimental Results with a DTC-SVM Drive5.4.10 Direct Self-Control5.4.11 Comparison between FOC and DTCList of Symbols
6 Sensorless Control of Induction Machine Drives
6.1 Introduction on Sensorless Control6.2 Model-Based Sensorless Control6.3 Anisotropy-Based Sensorless Control6.4 Model-Based Sensorless Techniques6.4.1 Open-Loop Integration6.4.1.1 Low-Pass Filter6.4.2 Inverter Nonlinearity6.4.3 Machine Parameter Mismatch6.4.4 Estimators and Observers6.4.5 Open-Loop Speed Estimators6.4.6 Model Reference Adaptive Systems6.4.6.1 Classic MRAS6.4.6.2 Closed-Loop MRAS6.4.7 Full-Order Luenberger Adaptive Observer6.4.7.1 State-Space Model of the IM6.4.7.2 Adaptive Speed Observer6.4.8 Full-Order Sliding-Mode Observer6.4.9 Reduced-Order Adaptive Observer6.4.9.1 Reduced-Order Observer Equations6.4.9.2 Possible Choices of the Observer Gain Matrix6.4.9.3 Speed Estimation6.4.10 Extended Kalman Filter6.5 Anisotropy-Based Sensorless Techniques6.5.1 Signal Injection Techniques6.5.1.1 Revolving Carrier Techniques6.5.1.2 IM Saliency Analysis under Rotating Carrier by FEA6.5.1.3 Pulsating Carrier Techniques6.5.1.4 High-Frequency Excitation Techniques6.5.1.4.1 INFORM Technique6.5.1.4.2 Instantaneous Rotor Position Measurement6.6 Conclusions on Sensorless Techniques for IM Drives
7 Permanent Magnet Synchronous Motor Drives
7.1 Introduction7.1.1 DC Brushless Motors7.1.2 AC Brushless Motors7.1.3 Permanent Magnets7.2 Space-Vector Model of Permanent Magnet Synchronous Motors7.3 Control Strategies of PMSM Drives7.3.1 Field-Oriented Control of PMSM Drives7.3.2 Torque-Controlled Drives7.3.2.1 Surface-Mounted PMSM7.3.2..1 FOC with Impressed Currents7.3.2.1.2 FOC with Impressed Voltage7.3.2.2 Interior-Mounted PMSM7.3.2.3 Feed-Forward Control7.3.3 Speed-Controlled Drives7.3.3.1 Experimental Results7.3.4 Direct Torque Control7.3.4.1 Electromagnetic Torque Production in the PMSM7.3.4.2 Criteria for the Selection of the Voltage Space-Vectors and Control Strategies7.3.4.3 Estimation of the Stator Flux and the Electromagnetic Torque7.3.4.4 The Direct Torque Control Scheme7.4 Sensorless Control of PMSM Drives7.4.1 Anisotropy-Based Sensorless Techniques7.4.1.1 Rotating Carrier Signal Injection7.4.1.2 Alternating Carrier Signal Injection7.4.1.2.1 Experimental Application of Pulsating Voltage Carrier Estimator with the Adaptive Linear Neural Filter*7.4.1.3 INFORM Sensorless Technique7.4.2 Model-Based Sensorless Techniques7.4.2.1 Open-Loop Estimators7.4.2.1.1 emf-Based Estimators7.4.2.2 Observer-Based Estimators7.4.2.2.1 Extended Kalman Filter7.4.2.2.2 TLS EXIN Neuron–Based Speed EstimatorAppendix: Experimental Test Setup
Part III Neural-Based Orthogonal Regression
8 Neural-Based Orthogonal Regression
8.1 Introduction: ADALINE and Least Squares Problems8.2 Approaches to the Linear Regression8.2.1 OLS Problem8.2.2 DLS Problem8.2.3 TLS Problem8.3 Minor Component Analysis and the MCA EXIN Neuron8.3.1 Some MCA Applications8.3.2 Neural Approach8.4 MCA EXIN Neuron8.4.1 Convergence during the First Transient Phase8.4.2 Dynamic Behavior of the MCA Neuron8.4.3 Dynamic Stability and Learning Rate8.4.4 Numerical Considerations8.4.4.1 Computational Cost8.4.4.2 Quantization Errors8.4.5 Acceleration Techniques8.4.6 Simulations8.4.7 Conclusions and Prospects for the MCA Neuron8.5 TLS EXIN Neuron8.5.1 Stability Analysis (Geometrical Approach)8.5.2 Convergence Domain8.5.3 Nongeneric TLS Problem8.6 Generalization of Linear Least Squares Problems8.7 GeMCA EXIN Neuron8.7.1 Qualitative Analysis of the Critical Points of the GeMCA EXIN Error Function8.7.2 Analysis of the Error Function GeTLS (Geometrical Approach)8.7.3 Critical Loci: Center Trajectories8.8 GeTLS EXIN Neuron8.8.1 GeTLS Domain of Convergence8.8.2 Scheduling8.8.3 Accelerated MCA EXIN Neuron (MCA EXIN+)
Part IV Selected Applications
9 Least-Squares and Neural Identification of Electrical Machines
9.1 Parameter Estimation of Induction Machines (IMs)9.2 Sensitivity of the Flux Model to Parameter Variations9.2.1 Sensitivity of the Current Flux Model9.2.2 Sensitivity of the Voltage Flux Model9.3 Experimental Analysis of the Effects of Flux Model Detuning on the Control Performance9.4 Methods for the On-Line Tracking of the Machine Parameter Variations9.5 On-Line Estimation of the IM Parameters with the Ordinary Least Squares Method9.5.1 Space-Vector Voltage Equations in the General Reference Frame9.5.2 Estimation of the Magnetizing Curve9.5.3 Ordinary Least Squares Identification9.5.4 RLS Algorithm9.5.5 Signal Processing System9.5.6 Description of the Test Setup for the Experimental Application9.5.7 Simulation and Experimental Results9.6 Constrained Minimization for Parameter Estimation of IMs in Saturated and Unsaturated Conditions9.6.1 Constrained Minimization: Analytical Solution9.6.1.1 First Constrained Minimization Method9.6.1.1.1 First Constrained Minimization Method: Simulation and Experimental Results9.6.1.2 Second Constrained Minimization Method9.6.1.2.1 Second Constrained Minimization Method: Simulation and Experimental Results9.7 Parameter Estimation of an IM with the Total Least Squares Method9.7.1 Simulation and Experimental Results9.8 Application of the RLS-Based Parameter Estimation to Flux Model Adaptation in FOC and DTC IM Drives9.9 Estimation of the IM Parameters at StandstillList of Symbols
10 Neural-Enhanced Single-Phase DG Systems with APF Capability
10.1 Introduction10.2 General Operating Principle10.3 ADALINE Design Criteria10.3.1 Notch Operation10.3.2 Band Operation10.3.3 MATLAB®–Simulink® Implementation10.3.4 Comparison with Traditional Digital Filters10.3.5 NN Band Filter versus PLL: Theoretical Comparison10.4 Building the Current Reference10.5 Multiresonant Current Controller10.6 Stability Issues10.7 Test Rig10.8 Experimental Results10.8.1 APF Insertion10.8.2 Power Reference Insertion10.8.3 Load Variation10.8.4 NN Filter versus PLL10.8.5 NN Filter versus p-q Theory10.8.6 Compliance with International Standards10.9 APF Connection Procedure
11 Neural Sensorless Control of AC Drives
11.1 NN-Based Sensorless Control11.2 11.2 BPN-Based MRAS Speed Observer11.2.1 On-Line Training of the BPN MRAS Observer11.2.2 Implementation Issues of the BPN MRAS Observer11.2.3 Experimental Results with the BPN MRAS Observer11.3 LS-Based MRAS Speed Observer11.3.1 Experimental Results with the OLS MRAS Observer11.3.2 TLS EXIN MRAS Observer11.3.2.1 Neural Adaptive Integrator11.3.2.1.1 Experimental Results with the Neural Adaptive Integrator11.3.2.2 Experimental Results with the TLS EXIN MRAS Observer11.3.2.2.1 TLS EXIN MRAS Observer with FOC Drive11.3.2.2.2 TLS EXIN MRAS Observer with DTC Drive11.3.3 Modified Euler Neural Adaptive Model11.3.3.1 Simulation Mode and Prediction Mode in MRAS Observers: Modified Euler against Simple Euler11.3.4 MCA EXIN + MRAS Observer11.4 TLS EXIN Full-Order Luenberger Adaptive Observer11.4.1 State-Space Model of the IM11.4.2 Adaptive Speed Observer11.4.3 TLS-Based Speed Estimation11.4.4 Stability Issues of the TLS EXIN Full-Order Adaptive Observer11.4.5 Experimental Results with the TLS EXIN Full-Order Luenberger Adaptive Observer11.4.5.1 Dynamic Performance11.4.5.2 Accuracy in the Low-Speed Ranges11.4.5.3 Accuracy at Very Low Speed (below 2 rad/s)11.4.5.4 Robustness to Load Perturbations11.4.5.5 Regenerative Mode at Very Low Speed11.4.5.6 Field-Weakening Operation11.4.5.7 Zero-Speed Operation11.4.6 Experimental Comparative Tests11.5 MCA EXIN + Reduced-Order Observer11.5.1 Reduced-Order Observer Equations11.5.2 MCA EXIN + Based Speed Estimation11.5.3 Proposed Choice of the Observer Gain Matrix11.5.4 Computational Complexity11.5.5 Experimental Results with the MCA EXIN + Reduced-Order Adaptive Observer11.5.5.1 Dynamic Performance11.5.5.2 Accuracy at Low Speed11.5.5.3 Zero-Speed OperationAppendix A Implemented Control SchemesAppendix B Description of the Test SetupList of Symbols
Index

Content preview from Power Converters and AC Electrical Drives with Linear Neural Networks

9 Least-Squares and Neural Identification of Electrical Machines

9.1 Parameter Estimation of Induction Machines (IMs)

The instantaneous knowledge of the electrical parameters of an IM, either supplied by the power grid or in the framework of a variable speed electric drive, is extremely important. It is proved by the huge amount of scientific papers published in the last 20 years in the field of on-line parameter estimation of IMs [1–80].

The instantaneous knowledge of the electrical parameters of an IM is useful in the power system analysis. It is well known that, in the analysis of such systems, the knowledge of the dependence of the loads from the power grid voltage and frequency is crucial for retrieving the information about the behavior ...

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Predictive Control of Power Converters and Electrical Drives

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Power Converters and AC Electrical Drives with Linear Neural Networks

by Maurizio Cirrincione, Marcello Pucci, Gianpaolo Vitale

9

Least-Squares and Neural Identification of Electrical Machines

9.1 Parameter Estimation of Induction Machines (IMs)

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