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Modeling, Control and Diagnosis of Electrical Machines and Devices
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At present, the growing use of electric machines and drives in more critical applications has driven research on condition monitoring and fault tolerance. The condition monitoring of electrical machines has a very important impact in the field of electrical systems maintenance, mainly for its potential functions of failure prediction, fault identification, and dynamic reliability estimation. The fault diagnosis of electrical machines and drives has received a great deal of attention due to its benefits in maintenance cost reduction, unscheduled downtime prevention, and, in many cases, harm prevention and failure disruption. Fault-tolerant design provides a solution combining fault occurrence conditions, failure detection and location tools, and the reconfiguration of control features. On the other hand, recent advancements in smart technology using artificial intelligence and advanced machine learning capabilities provide new perspectives for meaningful fault diagnostics and fault-tolerant control. These outstanding advancements enhance the performance of condition monitoring and have significant potential for the fault detection of electrical machines and devices. This reprint collected research and technological achievements related to the following topics: robust control strategies; failure detection and diagnosis; fault-tolerant control; and artificial intelligence (AI) and machine learning techniques for control, fault diagnosis, and tolerant control of electrical machines and devices.
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Keywords
- adaptive dynamic programming (ADP)
- air-gap field modulation
- analytical approach
- ARM Cortex
- buckâboost converter
- combined modes ensemble empirical mode decomposition
- Diagnosis
- dual permanent magnet vernier (DPMV)
- electric vehicle
- Electric vehicles
- embedded system
- enhanced minimum entropy deconvolution
- external rotor permanent magnet synchronous motor
- fault analysis
- fault detection
- fault diagnosis
- fault harmonics
- field-oriented control
- Finite element
- finite element analysis
- high-frequency common mode current
- hub machine
- induction motor drive
- induction motors
- insulation monitoring
- inter-turn short circuit in excitation windings
- interharmonics
- inverse optimal control
- inverter-fed motors
- KMAD indicator
- lack of turns
- machine learning method
- mains communication voltage
- maximum torque per ampere
- n/a
- neural identifier
- Neural Network (NN)
- optimal current calculation
- Optimization Problems
- power quality
- regenerative braking
- reinforcement learning (RL)
- ripple control
- rolling element bearing faults
- Sliding mode control
- stator parallel currents
- stator winding fault
- stator winding unbalance fault
- supply voltage unbalance
- synchronous condenser
- synchronous reluctance machine
- three-sigma rule
- Torque
- torque ripple minimization
- unbalanced voltage
- variable reluctance motor
- Vibration
Links
DOI: 10.3390/books978-3-7258-1340-7Editions