Control of Power Electronic Converters and Systems: Volume 4

Control of Power Electronic Converters and Systems: Volume 4
Copyright
Contributors
Preface
1. Z-source converters and their classifications
	1.1 Background of Z-source converters (impedance-source converters)
		1.1.1 Limitations of traditional voltage source inverters and current source inverters
			1.1.1.1 The voltage source inverter is widely used, but it has several limitations
			1.1.1.2 The current source inverter has several theoretical limitations
		1.1.2 Features of Z-source converter
		1.1.3 Operation principle of Z-source converter
		1.1.4 Classification
			1.1.4.1 DC/DC converter topologies
			1.1.4.2 DC/AC inverter topologies
			1.1.4.3 Two-level H-bridge topologies
			1.1.4.4 Multilevel/neutral point clamped
			1.1.4.5 AC/AC converter topologies (matrix converter)
			1.1.4.6 AC/DC converter topologies
		1.1.5 Design and optimization of impedance source network
			1.1.5.1 Design considerations
			1.1.5.2 Topologies
			1.1.5.3 Modulation strategies
			1.1.5.4 Switching frequencies
			1.1.5.5 Inductor design
			1.1.5.6 Capacitor design
			1.1.5.7 Design procedure
		1.1.6 Design example of quasi-Z-source inverter
		1.1.7 Application
	1.2 Future directions
	References
2. Control and modulation techniques of Z-source converter
	2.1 Modeling of Z-source inverter
	2.2 Modulation strategy classification and basic introduction
		2.2.1 Modulation techniques for single-phase H-bridge topologies
		2.2.2 Modulation techniques for traditional three-phase H-bridge topologies (two-level)
		2.2.3 Modulation techniques for three-phase multilevel topologies (NPC)
		2.2.4 Modulation techniques matrix topologies
		2.2.5 Modulation techniques for DC/DC converter with intermediate H-bridge
	2.3 Impact of modulation strategies on reliability and harmonics of impedance-source inverters
		2.3.1 Current stress on devices
		2.3.2 Power loss
		2.3.3 Junction temperature
		2.3.4 Number of cycles to failure
		2.3.5 Harmonics
	2.4 Control strategy of Z-source networked converter
	References
	Further reading
3. Dual active bridge converter and its control
	3.1 Introduction
	3.2 Operation principle and performance characterization of dual active bridge converter
		3.2.1 Circuit topology and modulation schemes
		3.2.2 Power flow analysis
		3.2.3 Component current stresses and zero-voltage switching operation range
	3.3 Modeling and control for dual active bridge converter
		3.3.1 Large- and small-signal modeling
		3.3.2 Output voltage control
	3.4 Summary
	References
4. Matrix converter: Model and control
	4.1 Introduction
	4.2 Modeling and control of third-harmonic injection matrix converter
		4.2.1 Converter topologies
		4.2.2 Operation and model of third-harmonic injection matrix converters
		4.2.3 Sinusoidal currents and controllable power factor of three-level third-harmonic injection MC
		4.2.4 Control of third-harmonic injection matrix converters
			4.2.4.1 Injected third-harmonic current calculation
			4.2.4.2 Design of third-harmonic injected current controller
		4.2.5 Experimental results
	4.3 Model and control of high-frequency link matrix converter
		4.3.1 Model of high-frequency link matrix converter
		4.3.2 Backstepping control of high-frequency link matrix converter
		4.3.3 Experimental results
	4.4 Summary
	References
5. Switched-boost-based multilevel inverters
	5.1 Introduction
	5.2 Switched-boost-based 3L converter: A basic cell
	5.3 Switched-boost-based 5L voltage source inverters
	5.4 Switched-boost-based hybrid multilevel voltage source inverters
	5.5 Interleaved configuration of switched-boost-based multilevel voltage source inveters with a comparative study
	5.6 Conclusion
	References
6. Power electronics building blocks: Control and applications
	6.1 Introduction
	6.2 Design considerations of power electronics building block architecture
		6.2.1 Selecting proper switching devices for power electronics building blocks for selected applications
		6.2.2 Selecting proper configuration of power electronics building blocks for different applications
		6.2.3 Layout design optimization of power electronics building block
		6.2.4 Control and protection architectures of power electronics building blocks
	6.3 Summary
	References
7. Multisampled current control of grid-following voltage source converters
	7.1 Introduction
	7.2 Multisampling pulse width modulation analysis and aliasing suppression
		7.2.1 Multisampling pulse width modulation analysis
		7.2.2 Antialiasing filter design
		7.2.3 Case study
	7.3 Dissipation of converter-side current control
		7.3.1 Admittance modeling and dissipativity analysis
		7.3.2 Dissipativity enhancement with active damping
		7.3.3 Case study
	7.4 Dissipation of grid-side current control
		7.4.1 Admittance modeling and dissipativity enhancement
		7.4.2 Internal stability of alternating current controller
		7.4.3 Case study
			7.4.3.1 Internal stability validation
			7.4.3.2 Voltage source converter-grid interactive stability validation
	7.5 Summary
	References
8. Artificial intelligence–assisted data-driven control of power electronics systems
	8.1 Introduction
	8.2 Metaheuristic methods
		8.2.1 Genetic algorithm
		8.2.2 Particle swarm optimization
	8.3 Fuzzy logic
		8.3.1 Mamdani-type fuzzy logic
		8.3.2 Takagi-Sugeno-Kang–type fuzzy logic
	8.4 Machine learning
		8.4.1 Neural network
		8.4.2 Fuzzy neural network
		8.4.3 Recurrent neural network
		8.4.4 Reinforcement learning
	8.5 Perspectives and outlooks
	8.6 Conclusions
	References
9. Electric vehicle charging technology and its control
	9.1 Introduction to electric vehicle charging
	9.2 Onboard charger
		9.2.1 Conventional power electronics and feedback control strategy
		9.2.2 High-performance power electronics circuits for onboard chargers
			9.2.2.1 Universal single- and three-phase power factor correction front-end circuit
			9.2.2.2 Bidirectional single-phase power factor correction rectifier and active power decoupling circuit
	9.3 Offboard charger
	9.4 Contactless charger
		9.4.1 Coil topology
		9.4.2 Compensation topology
		9.4.3 Soft-switching operation and resonant frequency tracking
		9.4.4 Power flow and model predictive control of interphase transformer systems
	9.5 Power quality of EV charging
		9.5.1 Power quality parameters and grid codes
		9.5.2 Voltage fluctuation
		9.5.3 Nonfundamental distortion
	9.6 Smart charging
		9.6.1 Definition of smart charging
		9.6.2 Examples of smart charging
		9.6.3 Vehicle to grid
		9.6.4 Implementing smart charging and vehicle to grid
			9.6.4.1 Smart charging via Type 1 and 2 AC charging
			9.6.4.2 Vehicle to grid via type 1 and 2 AC charging
			9.6.4.3 Smart charging via CHAdeMO
			9.6.4.4 Implementing vehicle to grid using CHAdeMO
			9.6.4.5 Smart charging via CCS/COMBO
		9.6.5 Smart charging protocols
	9.7 Summary
	References
10. Physics-informed neural network-based control of power electronic converters
	10.1 Introduction
	10.2 Trends in scientific computing
		10.2.1 Physics-guided neural networks
			10.2.1.1 Features
			10.2.1.2 Limitations
		10.2.2 Physics-informed neural networks
			10.2.2.1 Features
			10.2.2.2 Comprehensive design steps
			10.2.2.3 Limitations
		10.2.3 Physics-encoded neural networks
	10.3 Data-driven estimation problems in controlling power electronics under saturation boundaries
	10.4 Physics-informed neural network for power electronics
		10.4.1 Design of physics-informed neural networks for power electronics
		10.4.2 Generalization of physics-informed neural networks for controlling power electronics
		10.4.3 Data collection policy
	10.5 Results using physics-informed neural networks
	10.6 Conclusions
	References
11. Surrogate models for power electronic systems applying machine learning techniques
	11.1 Introduction
	11.2 Basic framework of surrogate model
	11.3 Examples of applying surrogate modeling in power electronics
		11.3.1 Surrogate model for heat sink
		11.3.2 Surrogate model for magnetic components
		11.3.3 Reliability evaluation and design
		11.3.4 Next generation of simulation and optimization techniques
	11.4 Detailed example applying surrogate modeling to power semiconductor thermal modeling considering cross-coupling effects
	11.5 Conclusions
	References
12. Topologies and control for battery balancing applications
	12.1 Introduction
	12.2 Balancing topologies
		12.2.1 Intrapack balancing circuits
			12.2.1.1 Dissipative structure
				12.2.1.1.1 Passive type
				12.2.1.1.2 Active type
			12.2.1.2 Nondissipative structure
				12.2.1.2.1 Adjacent cell-to-cell type
				12.2.1.2.2 Direct cell-to-cell type
				12.2.1.2.3 Cell-to-pack type
				12.2.1.2.4 Pack-to-cell type
				12.2.1.2.5 Cell-to-pack-to-cell type
		12.2.2 Interpack balancing circuits
			12.2.2.1 DC side–cascaded configuration
			12.2.2.2 AC side–cascaded configuration
	12.3 Balancing control
		12.3.1 Intrapack balancing control
			12.3.1.1 State-of-charge balancing control
			12.3.1.2 State-of-health balancing control
		12.3.2 Interpack balancing control
			12.3.2.1 Interphase/arm balancing control
				12.3.2.1.1 Interphase balancing control for cascaded H-bridge battery energy storage systems
				12.3.2.1.2 Interphase and interarm balancing control for modular multilevel converter battery energy storage systems
			12.3.2.2 Intraphase/arm balancing control
		12.3.3 Multilayer balancing control
	12.4 Field examples
	12.5 Summary
	References
13. Battery state-of-health estimation using machine learning
	13.1 Introduction—what is battery state of health?
	13.2 Battery performance and degradation
		13.2.1 Battery capacity degradation
		13.2.2 Battery internal resistance degradation
	13.3 Overview of state-of-health estimation methods
		13.3.1 Direct measurement
		13.3.2 Model-based method
		13.3.3 Data-driven method
	13.4 Feature-based battery state-of-health estimation
		13.4.1 Examples of features
		13.4.2 Linear regression
		13.4.3 Support vector machine
		13.4.4 Feed-forward neural network
		13.4.5 Example: state-of-health estimation with fuzzy entropy and support vector machine
			13.4.5.1 Effect of data noise on estimation accuracy
			13.4.5.2 Effect of parameter selection on estimation accuracy
			13.4.5.3 Effect of data size on estimation accuracy
			13.4.5.4 Effect of test temperature on estimation accuracy
			13.4.5.5 Effect of test state of charge on estimation accuracy
			13.4.5.6 State-of-health estimation for different battery chemistries
	13.5 Sequence-based battery state-of-health estimation
		13.5.1 Deep learning
		13.5.2 Ensemble learning
		13.5.3 Example: state-of-health estimation with window voltage and ensemble learning
	13.6 Comparison of machine learning-based state-of-health estimation
	13.7 Summary
	References
14. Operation and control of data centers
	14.1 Introduction
	14.2 Full power processing–based point-of-load converters
		14.2.1 Inductive point-of-load converters
			14.2.1.1 Coupled inductor–based point-of-load converters
			14.2.1.2 Transformer-based point-of-load converters
		14.2.2 Capacitive point-of-load converters
			14.2.2.1 Switched capacitor–based point-of-load converters with resonant inductor
			14.2.2.2 Switched capacitor–based point-of-load converters with coupled inductor
	14.3 Differential power processing–based point-of-load converters
		14.3.1 Bus-to-load architecture
		14.3.2 Load-to-load architecture
		14.3.3 Other derived architectures
	14.4 Operation and control for data centers
		14.4.1 Power flow in data center
		14.4.2 Differential power processing–based data center
	14.5 Summary
	References
15. Operation and control of uninterruptible power supply system
	15.1 Introduction
		15.1.1 Power quality
		15.1.2 Function of uninterruptible power supply
		15.1.3 Classification of uninterruptible power supply
			15.1.3.1 Backup uninterruptible power supply
			15.1.3.2 Interactive uninterruptible power supply
			15.1.3.3 Online uninterruptible power supply
		15.1.4 Uninterruptible power supply applications
	15.2 Power converter topologies for uninterruptible power supply systems
		15.2.1 Line-frequency transformer-based uninterruptible power supply systems
		15.2.2 High-frequency transformer-based uninterruptible power supply systems
		15.2.3 Transformer-less uninterruptible power supply systems
	15.3 Uninterruptible power supply control techniques
		15.3.1 Control targets for uninterruptible power supply systems
		15.3.2 Cascaded control
			15.3.2.1 Stationary-frame-based control
			15.3.2.2 Synchronous-frame-based control
		15.3.3 Harmonic loop control technique
			15.3.3.1 Sinusoidal current control for pulse-width modulation rectifier
			15.3.3.2 Low-distortion voltage control for inverter
		15.3.4 Phase lock loop [19]
		15.3.5 Universal controller for both rectifier and inverter
			15.3.5.1 Instantaneous sinusoidal waveform control loop
			15.3.5.2 Harmonic control loop
			15.3.5.3 Synchronization control
			15.3.5.4 Universal controller
		15.3.6 Current sharing control of paralleled uninterruptible power supply
			15.3.6.1 Control model of paralleled uninterruptible power supply system
			15.3.6.2 Control loop design for current sharing of parallel uninterruptible power supply
	15.4 Progress in uninterruptible power supplies
		15.4.1 Uninterruptible power supply with hybrid insulated gate bipolar transistor devices
		15.4.2 Soft-switching uninterruptible power supplies
		15.4.3 Super-uninterruptible power supply
	15.5 Summary
	References
16. Efficient modeling and simulation of wear-out and state-of-charge in storage systems
	16.1 Introduction
	16.2 Wear-out concept of battery energy storage
		16.2.1 Battery performance modeling
		16.2.2 Battery degradation modeling
		16.2.3 Example of wear-out modeling for stationary storage applications
	16.3 Challenges in estimating degradation
		16.3.1 Cycle counting rules and limitations for online implementation
		16.3.2 Online rainflow cycle counting
	16.4 Online incremental degradation estimation
		16.4.1 Working principle
		16.4.2 Validation of incremental degradation method
	16.5 Case study
		16.5.1 System setup
		16.5.2 Simulation results
	16.6 Conclusion
	References
17. Solid-state transformer and magnetic properties with potential topologies: Magnetic properties of soft magnetic material fo ...
	17.1 Introduction
		17.1.1 Typical functions and topologies of solid-state transformers
	17.2 Classification of soft magnetic materials for solid-state transformers
	17.3 Magnetic properties of soft magnetic materials under actual conditions
		17.3.1 Temperature dependency of properties for soft magnetic materials
		17.3.2 Magnetic properties under nonsinusoidal excitation
		17.3.3 Vibration and noise properties of medium-frequency core materials under nonsinusoidal excitation
	17.4 Summary
	References
18. Voltage control of solid-state transformer to guarantee smart transformer functionalities
	18.1 Solid-state transformer architectures
	18.2 Smart transformer control structures
	18.3 Stability and power quality assessment of smart transformer–fed LVAC grids
		18.3.1 Stability of LV grids dominated by passive loads
		18.3.2 Stability of LV grids dominated by grid converters
	18.4 Influence of current feedback
	18.5 Tuning of current and voltage controllers
	18.6 Conclusions
	References
19. Solid-state transformer applied in electrified railway systems
	19.1 Introduction
	19.2 Development of solid-state transformers in traction power system
		19.2.1 E-transformer in 2003 by Alstom
		19.2.2 Medium frequency topology in 2007 by Bombardier
		19.2.3 Power electronic traction transformer developed by ABB
	19.3 Solid-state transformers in AC electrified railway
	19.4 Solid-state transformers in DC electrified railway systems
	19.5 Simulation results
		19.5.1 Case I: Voltage control mode verification
		19.5.2 Case II: Power control mode verification
		19.5.3 Case III: Regenerative braking-supporting mode verification
	19.6 Challenges and opportunities
		19.6.1 Reliability and safety of solid-state transformers
		19.6.2 Design and standardization of solid-state transformers
		19.6.3 Other issues
	19.7 Summary
	References
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	Z