Modeling, Control and Diagnosis of Electrical Machines and Devices

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 potent...

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محفوظ في:

التفاصيل البيبلوغرافية
التنسيق:	Online
اللغة:	الإنجليزية
منشور في:	MDPI - Multidisciplinary Digital Publishing Institute 2024
الموضوعات:	electric vehicles regenerative braking inverse optimal control buck–boost converter neural identifier combined modes ensemble empirical mode decomposition KMAD indicator three-sigma rule enhanced minimum entropy deconvolution rolling element bearing faults fault detection electric vehicle synchronous reluctance machine field-oriented control maximum torque per ampere optimal current calculation sliding mode control torque ripple minimization stator winding unbalance fault external rotor permanent magnet synchronous motor fault harmonics diagnosis lack of turns analytical approach finite element analysis variable reluctance motor optimization problems reinforcement learning (RL) adaptive dynamic programming (ADP) neural network (NN) machine learning method synchronous condenser unbalanced voltage inter-turn short circuit in excitation windings finite element fault analysis stator parallel currents hub machine dual permanent magnet vernier (DPMV) air-gap field modulation torque induction motors interharmonics mains communication voltage power quality ripple control vibration induction motor drive fault diagnosis stator winding fault supply voltage unbalance ARM Cortex embedded system high-frequency common mode current inverter-fed motors insulation monitoring n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering
الوصول للمادة أونلاين:	ONIX_20240704_9783725813391_163
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

الوصف
الملخص:	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.

Modeling, Control and Diagnosis of Electrical Machines and Devices

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