Design and optimization of switched reluctance motor by Taguchi method

Gong, Yang; Zhao, Shiwei; Luo, Shan

doi:10.1109/iciea.2018.8398014

Cited by 2 publications

(1 citation statement)

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“…The second phase of Taguchi's method is to conduct all the simulations planned in phase 1. The results are then analyzed to know the effect of each parameter on the objective function through a statistical study, and the best performance is selected [144]. Taguchi's method was utilized in the design of SRMs, and more details can be found in [144]- [146].…”

Section: C) Taguchi's Methodsmentioning

confidence: 99%

Geometry and Topology Optimization of Switched Reluctance Machines: A Review

Abdalmagid

Sayed²,

Bakr

et al. 2022

IEEE Access

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Switched reluctance machines (SRMs) have recently attracted more interest in many applications due to the volatile prices of rare-earth permanent magnets (PMs) used in permanent magnet synchronous machines (PMSMs). They also have rugged construction and can operate at high speeds and high temperatures. However, acoustic noise and high torque ripples, in addition to the relatively low torque density, present significant challenges. Geometry and topology optimization are applied to overcome these challenges and enable SRMs to compete with PMSMs. Key geometric design parameters are optimized to minimize various objective functions within geometry optimization. On the other hand, the material distribution in a particular design space within the machine domain may be optimized using topology optimization. We discuss how these techniques are applied to optimize the geometries and topologies of SRMs to enhance machine performance. As optimizing the machine geometry and material distribution at the design phase is of substantial significance, this work offers a comprehensive literature review on the current state of the art and the possible trends in the optimization techniques of SRMs. The paper also reviews different configurations of SRMs and stochastic and deterministic optimization techniques utilized in optimizing different configurations of the machine.

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Section: C) Taguchi's Methodsmentioning

confidence: 99%