2021
DOI: 10.24200/sci.2021.59113.6067
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Modeling and detection of demagnetization fault in permanent magnet vernier machine using flexible magnetic equivalent circuit method

Abstract: In this paper, an analytical model is proposed for evaluating electromagnetic performances of permanent magnet vernier machines (PMVMs) under healthy and faulty conditions. The proposed model employs flexible magnetic equivalent circuit (MEC) method, which its accuracy can be selected by tunable parameters. The model is capable of considering the influence of saturation effect, skewed slots, slot leakage fluxes, and various winding arrangements for the machines with desired properties. First, the proposed mode… Show more

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Cited by 1 publication
(5 citation statements)
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“…The Nf and Ns are the turn number of each coil, and Ln is considered for modelling of Y and Y N connection of the phases windings. Clearly, Ln → ∞ indicates ia + ib + ic = 0 in which Y denotes connection, and the Y N connection can be modelled by Ln = 0 [22, 29–31]. Notably, the end‐effect is an important phenomenon that should be considered for accurate analysis of the linear machines.…”
Section: Proposed Dc‐fslmmentioning
confidence: 99%
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“…The Nf and Ns are the turn number of each coil, and Ln is considered for modelling of Y and Y N connection of the phases windings. Clearly, Ln → ∞ indicates ia + ib + ic = 0 in which Y denotes connection, and the Y N connection can be modelled by Ln = 0 [22, 29–31]. Notably, the end‐effect is an important phenomenon that should be considered for accurate analysis of the linear machines.…”
Section: Proposed Dc‐fslmmentioning
confidence: 99%
“…Noticing Figures 1, and 2, the main parameters are tabulated in Table 1. In Equation (), μ r ( B ) is a non‐linear function for saturation effect as written in Equation (), where the B‐H curve of the flux tubes can be fitted on the actual martial curve by a , b , c , and d parameters [22–27, 29, 31]. Rij={lijμ0.μr()Bij.Aij1emforthecoreelementslijμ0.Aij1emfortheairelementslij/2μ0.Aij+lij/2μ0.μr()Bij.Aij1emfortheairfalse|normalcnormalonormalrnormale0.3333emnormalenormallnormalenormalmnormalenormalnnormaltnormals $R{i}_{j}=\left\{\begin{array}{ll}\frac{l{i}_{j}}{{\mu }_{0}.…”
Section: A Flexible Mec Proposal For Dc‐fslm Modelmentioning
confidence: 99%
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