Coupled switched reluctance machine modeling and simulations

Yang, Yinye; Emadi, Ali

doi:10.1109/itec-ap.2014.6940842

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Then, a dynamic model for the considered SRM is developed solving the time-domain differential equation for the phase current based on the look-up tables. Interpolation is applied to obtain the torque as a function of the rotor position and calculated phase current [37], [38].…”

Section: B Analytical Modeling Methodsmentioning

confidence: 99%

Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors

et al. 2022

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This work presents a comprehensive review of the developments in using Machine Learning (ML)-based algorithms for the modeling and design optimization of switched reluctance motors (SRMs). We reviewed Machine Learning-based numerical and analytical approaches used in modeling SRMs. We showed the difference between the supervised, unsupervised and reinforcement learning algorithms. More focus is placed on supervised learning algorithms as they are the most used algorithms in this area. The supervised learning algorithms studied in this work include the feedforward neural networks, recurrent neural networks, support vector machines, extreme learning machines, and Bayesian networks. This work also discusses several essential aspects of the considered machine learning algorithms, such as core concept, structure, and computational time. It also surveys sample data acquisition methods and data size. Finally, comparisons between the different considered ML-based algorithms are conducted in terms of electric motor type, dataset inputs and outputs, and algorithm's structure and accuracy to provide a summary overview of the ML-based algorithms for SRMs modeling and design.INDEX TERMS Electric machine design, electric machine modeling, machine learning (ML), switched reluctance motor (SRM).

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Section: B Analytical Modeling Methodsmentioning

confidence: 99%

Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors

et al. 2022

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“…This technique is simple to implement with good accuracy using tools such as MATLAB, OCTAVE, and Compose and Activate by Altair [41], [42], [45], [46]. The machine phase flux linkage/inductance and torque characteristics at different stator currents and different rotor positions are saved in This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: ) Look-up Tablesmentioning

confidence: 99%

Electromagnetic Modeling Techniques for Switched Reluctance Machines: State-of-the-Art Review

Watthewaduge

Sayed

Emadi

et al. 2020

IEEE Open J. Ind. Electron. Soc.

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Switched Reluctance Machines (SRMs) are gaining more attention due to their simple and rugged construction, low manufacturing cost, and high-speed operation capability. An electromagnetic model of the machine is needed in the design and analysis processes. The required accuracy level of the model depends mainly on the application. A high-fidelity model is required to achieve a good design and predict the performance accurately. However, it requires high computational cost and longer simulation time. Other fast and less-comprehensive models with less computational burden could be utilized in the design and analysis of the motor drives. This paper extensively analyzes various electromagnetic modeling techniques of SRMs. Analytical, numerical, and hybrid models are considered. The paper investigates analytical models that are based on Maxwell's equations in addition to interpolation and curve fitting techniques. Numerical techniques such as Finite Element Method (FEM) and Boundary Element Method (BEM) are presented. Moreover, Magnetic Equivalent Circuit (MEC) method is discussed. Finally, potential research areas are proposed for the electromagnetic modeling of SRMs. INDEX TERMS Analytical model, boundary element method (BEM), electromagnetic modeling, finite element method (FEM), magnetic equivalent circuit (MEC) model, numerical model, switched reluctance machine (SRM).

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Coupled switched reluctance machine modeling and simulations

Cited by 2 publications

References 12 publications

Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors

Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors

Electromagnetic Modeling Techniques for Switched Reluctance Machines: State-of-the-Art Review

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