2016
DOI: 10.1109/tie.2016.2521343
|View full text |Cite
|
Sign up to set email alerts
|

Real-Time Emulation of Switched Reluctance Machines via Magnetic Equivalent Circuits

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
18
0
1

Year Published

2017
2017
2024
2024

Publication Types

Select...
5
3
1

Relationship

0
9

Authors

Journals

citations
Cited by 69 publications
(19 citation statements)
references
References 45 publications
0
18
0
1
Order By: Relevance
“…Based on Kirchhoff's second law of magnetic circuit, Hanic et al [95] proposed an analysis approach for saturated surface-mounted permanent magnet motor with no-load by using the conformal mapping and MEC method. Fleming et al [96] conducted real-time simulation of switched reluctance motor by MEC.…”
Section: Magnetic Equivalent Circuitmentioning
confidence: 99%
“…Based on Kirchhoff's second law of magnetic circuit, Hanic et al [95] proposed an analysis approach for saturated surface-mounted permanent magnet motor with no-load by using the conformal mapping and MEC method. Fleming et al [96] conducted real-time simulation of switched reluctance motor by MEC.…”
Section: Magnetic Equivalent Circuitmentioning
confidence: 99%
“…( r ) = cu − ca = 0 (19) Then, this paper proposes another way to define the converged of the iteration process. It expresses as: (20) means that the differences between the magnetic flux density Bca and the nonlinear magnetizing curve flux density Bcu is not bigger than a predefined tolerance ɛ.…”
Section: E Nonlinear Analysismentioning
confidence: 99%
“…In order to take into account, the variation period and the phase shift between the teeth, [19] use analytical functions for the air gap reluctance calculation. Reference [20] proposes a real-time equivalent magnetic circuit (EMC) machine model to accurate electromagnetic device characteristics calculation. By considering only the main flux path of the machine, the possible mesh paths are reduced to one per phase.…”
Section: Introductionmentioning
confidence: 99%
“…To reduce the initialization effort and to account for production uncertainties, the amount of auto-parameterizing [41], genetic algorithms, self-learning and neural network tuning algorithms, that adjust the characteristic stored in the LUT to the actual machine, has increased steadily. Alternatively, to model saturation and phase coupling in an SRM, Fleming proposes to use MEC-based models [42], [43]. However, the network needs to be reduced to enable realtime capability, leading to a strongly reduced analytical phase model.…”
Section: B Online: Srm Models For Real-timementioning
confidence: 99%