Design of thrust ripple minimization in wound secondary linear synchronous motors by response surface methodology (RSM)

Hosseini, Monir Sadat; Khameneh, Mehdi Mardaneh; Vaez‐Zadeh, Sadegh

doi:10.1109/pedstc.2011.5742484

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…In ref. [20], the average thrust, weight and thrust fluctuation of the motor are used as the optimisation objectives. RSM is used to establish the optimisation model, and the target weighting method is used to solve the problem of multi-objective conflict.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective optimisation design of a suspension‐guided permanent magnet synchronous linear motor for ropeless elevator

Zhang

Feng

et al. 2022

IET Electric Power Appl

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In this article, a suspension‐guided permanent magnet synchronous linear motor (SG‐PMSLM) for ropeless elevator is proposed, which can meet the requirements of high thrust and high thrust density for ropeless elevator, and reduce vibration noise and wheel‐rail wear during motor operation. The structure, working principle and electromagnetic characteristics of SG‐PMSLM are analysed. In order to improve the overall performance of SG‐PMSLM, this article aims at improving the average thrust and thrust density, reducing the thrust fluctuation of SG‐PMSLM. Firstly, the sensitivity analysis of the key structural parameters of SG‐PMSLM is conducted, and the parameters that have significant influence on the three optimisation objectives are selected as the optimisation variables. Secondly, the response surface model of three optimisation objectives is built, and the Particle Swarm Optimisation algorithm is combined to conduct the multi‐objective optimisation design for SG‐PMSLM. Thirdly, the optimised solution set is obtained for three objectives. Finite Element Analysis is used to verify that the optimised solution set has better diversity and can effectively improve SG‐PMSLM overall performance compared with the initial design. Finally, the electromagnetic performance of SG‐PMSLM is verified by experiments.

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Section: Introductionmentioning

confidence: 99%