Fault Diagnosis for Robust Inverter Power Drives

Cover
Contents
Preface
1 Fundamentals for reliability and early diagnosis for inverter power drives
1.1 Introduction
1.1.1 Manufacture defects (early failure)
1.1.2 Random failure
1.1.3 Wear-out failure
1.2 Statistical life estimation and failure rate: the bathtub curve
1.2.1 Reliability R(t) and unreliability F(t) functions
1.2.2 Probability density function and medium time before failure
1.2.3 Failure rate function
1.2.4 Exponential distribution
1.2.5 Weibull distribution
1.3 Degradation, failure mechanisms,and life model estimation
1.3.1 Solid-stare materials
1.3.1.1 Insulator
1.3.1.2 Conductors
1.3.1.3 Semiconductors
1.3.2 Failure modes and physics-based life model calculation
1.3.2.1 Physics-based life models
1.3.2.2 The Arrhenius model
1.3.2.3 Power model
1.3.2.4 Eyring model
1.3.2.5 Thermal cycling
1.4 Inverters failure and power drives
1.5 Circuit with ideal switches: power switches fundamentals
1.6 PWM, the enabler of power electronics
1.7 Switching under RL circuit load
1.8 RLC circuit1
1.8.1 Series RLC model
1.8.2 Shunt RLC model
1.9 PWM in inverters
1.10 Inverter basic operation
1.11 Three-phase and multilevel inverters
1.12 Operation principle of multilevel inverters
1.13 Dominant topology
1.14 Resonant converters
1.15 Real switches: power losses in hard switching
1.15.1 Conduction losses
1.15.2 Switching losses
1.16 Thermal consideration
1.16.1 State modeling of the thermal system
1.16.2 Thermal runaway
References
2 Early diagnosis in power semiconductors: MOSFET, IGBT, emerging materials (SiC and GaNs)
2.1 Introduction
2.1.1 Power device stress factors
2.1.2 Silicon power MOSFET structure and parasitics
2.1.3 SiC power MOSFET structure and parasitics
2.1.4 GaNs structure and parasitics
2.1.5 IGBT structure and latch-up
2.2 Switching process in semiconductors
2.2.1 Field distortion acceleration model
2.3 Relevant indicators in power semiconductors
2.3.1 Voltage Vth and capacitance shift
2.3.1.1 Fundamental equations and modeling of Vth displacement and capacitance change by aging
2.3.2 Ringing characterization and turn-on delay
2.3.3 Detachment and wire bond fatigue
2.3.3.1 Detachment in GANs
2.3.3.2 Wire bond and solder bond fatigue
2.3.3.3 Present and future in life estimation models
2.3.4 Junction temperature of power semiconductor
2.3.4.1 Observer based for junction temperature estimation
2.3.4.2 Device selection observer
References
3 Early diagnosis in DC-link capacitors: electrolytic and films
3.1 Introduction
3.1.1 Research challenges
3.1.2 Organization
3.2 Modeling for prognostics
3.3 Research methodology
3.4 Degradation in electrolytic capacitors
3.4.1 Degradation mechanisms
3.4.2 Capacitor degradation models
3.4.3 Physics-based models for C and ESR
3.4.3.1 Capacitance model
3.4.3.2 ESR model
3.4.3.3 Computing electrolyte volume from capacitor geometry
3.4.3.4 Electrolyte evaporation model
3.4.4 Time-dependent degradation models
3.5 Model-based prognostics framework
3.5.1 Kalman filter for state estimation
3.5.2 Future state forecasting
3.5.3 Noise models
3.5.4 Prognostics problem formulation
3.5.5 Physics-based modeling framework using unscented Kalman filter
3.5.5.1 Capacitance degradation dynamic model
3.5.5.2 UKF for capacitance state estimation
3.5.5.3 ESR degradation dynamic model
3.5.5.4 UKF for ESR state estimation
3.6 Accelerated aging experiments
3.6.1 Experimental setup
3.6.2 Electrical overstress
3.6.3 EOS experiment
3.7 Prediction of remaining useful life results and validation tests
3.7.1 Results for capacitor degradation model (D4)
3.7.1.1 Remaining useful life
3.7.1.2 Validation tests
3.7.1.3 Discussion
3.7.2 Results fordegradation model (D5)
3.7.2.1 Remaining useful life
3.7.2.2 Validation tests
3.8 Conclusion
References
4 Embedded fault diagnosis and prognosis
4.1 Introduction
4.2 Embedded systems
4.3 Diagnosis, prognosis, and condition monitoring
4.4 Review of hardware used in embedded diagnosis and prognosis systems
4.4.1 Sensors
4.4.2 Microprocessors, microcontrollers, and digital signal processors
4.4.3 Analog-to-digital converters
4.5 Switching devices and their faults
4.6 Analysis of aging in IGBT power modules
4.7 Prognosis and condition monitoring of power switches
4.7.1 VCE monitoring
4.7.2 RDS monitoring
4.7.3 Other indicators
4.8 Fault diagnosis techniques of power switches
4.8.1 Open-circuit fault detection
4.8.1.1 Current-based techniques
4.8.1.2 Voltage-based techniques
4.8.1.3 Hybrid techniques
4.8.2 Short-circuit fault detection
4.9 Fault prognosis and diagnosis in sources and loads
4.9.1 PV arrays
4.9.2 Detection of islanding in grid connected inverters
4.9.3 Condition monitoring in electric machines
4.9.4 State of health in batteries
4.9.5 Fault diagnosis in sensors
References
5 Fault-tolerance strategies for power converters
5.1 Fault prognosis/diagnosis and health management
5.1.1 Condition monitoring of IGBTs
5.1.2 Health prognosis of IGBTs
5.1.3 Diagnosis
5.2 Fault-tolerant topologies
5.2.1 2L-VSI with middle-point connection
5.2.2 Space vector generation during fault-tolerant operation
5.2.3 Fault-tolerant operation of the back-to-back converter
5.2.4 Three-level NPC converter
5.3 Fault-tolerant operation of the open-end converter
5.3.1 DTC algorithm
5.3.2 DTC operation for switch trigger suppression
5.3.2.1 Trigger suppression in one and two devices
5.3.2.2 Trigger suppression in six switches (asymmetric operation)
5.4 Summary
References
6 Motor diagnostics and protection using inverter capabilities
6.1 Introduction
6.2 Thermal monitoring and protection
6.2.1 Thermal models
6.2.1.1 Estimation of the motor losses
6.2.1.2 Thermal model parameter estimation
6.2.2 Parameter-based temperature estimation
6.3 Monitoring and protection of stator-related issues
6.3.1 Turn insulation
6.3.2 Primary insulation system
6.3.3 Open stator winding faults and open-switch faults
6.3.4 Stator core monitoring
6.4 Rotor-related issues
6.4.1 Rotor eccentricity
6.4.2 Broken rotor bars and end rings
6.4.3 Demagnetization
6.5 Bearings, gearbox, and other mechanical problems
6.5.1 Bearings faults
6.5.2 Gearbox faults
References
7 Battery storage
7.1 Introduction
7.1.1 Batteries principle of operation
7.1.1.1 Important parameters of batteries
7.1.2 Li-ion batteries
7.1.3 High power applications for Li-ion batteries
7.1.3.1 Electrical vehicles
7.1.3.2 Residential energy storage
7.2 Electrical model of Li-ion batteries
7.3 Aging of Li-ion batteries
7.3.1 Method for detection of aging in batteries using impedance measurement
7.3.1.1 Offline measurement methods
7.3.1.2 Online measurement methods
References
8 Prognostics: a battery case study
8.1 Introduction
8.2 Lebesgue sampling-based fault diagnosis and prognosis
8.2.1 Fault mechanism modeling
8.2.2 Lebesgue sampling
8.2.3 Lebesgue sampling-based diagnosis
8.2.4 Lebesgue sampling-based prognosis
8.3 Applications to batteries
8.4 Performance metrics for prognosis
8.4.1 Prognostic horizon
8.4.2 Acceptable predictions
8.4.3 α-λ Metrics
8.4.4 Relative accuracy
8.4.5 Convergence
8.4.6 Performance score
8.4.7 Experimental results
8.4.7.1 EKF in Riemann sampling framework
8.4.7.2 EKF in Lebesgue sampling framework
8.4.7.3 Comparison of RS-EKF and LS-EKF
8.5 Conclusion
References
Index
Back Cover