Development of a simple nonlinear switched reluctance motor model using measured flux linkage data and curve fit

Chan, Wei Min; Weldon, W.F.

doi:10.1109/ias.1997.643044

Cited by 10 publications

(1 citation statement)

References 30 publications

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“…This drawback results to attempts made to simulate an accurate model of the SRM [8 -10]. In [9], a special 3-D shape function has been developed to curve fit the flux linkage data using the steepest descent method. Few of these shape functions are added together to reduce the mean square error.…”

Section: Brief Review On Existing Modelsmentioning

confidence: 99%

Mathematical Modeling of Flux-Linkage Characteristics of Switched Reluctance Motors Using Polynomial Neural Networks

Rajandran

Sahoo

Gobbi

2006

2006 IEEE International Power and Energy Conference

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Switched Reluctance Motor (SRM), built using revolutionary concept, has highly nonlinear flux-linkage characteristics depending heavily on phase current and rotor position. A good mathematical model for these characteristics would help to understand the workings of the motor; thus providing path for better control algorithms and motor designs. It is very much proven by many researchers in this area, that a straightforward simple mathematical model has never satisfied the complete overall characteristics. Moreover, there is no distinct guideline about what sort of mathematical model would be suitable. To overcome this problem, a self-organizing polynomial neural network is proposed in this paper. In this scheme, without any prior knowledge of the mathematical model, the model is evolved iteratively and progressively. The simulation test results verify the effectiveness of this approach.

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Section: Brief Review On Existing Modelsmentioning

confidence: 99%

Mathematical Modeling of Flux-Linkage Characteristics of Switched Reluctance Motors Using Polynomial Neural Networks

Rajandran

Sahoo

Gobbi

2006

2006 IEEE International Power and Energy Conference

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Flux linkage characteristic measurement and parameter identification based on hybrid genetic algorithm for switched reluctance motors

Xia

Xue

Chen

et al. 2008

2008 3rd IEEE Conference on Industrial Electronics and Applications

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An Extended Equivalent Model for a Switched Reluctance Machine

Stevens¹,

Deprez²,

Bastiaensen³

et al. 2007

IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society

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An extended equivalent model (EEM) is introduced for designing and analyzing steady state regime or transient dynamics of a Switched Reluctance Machine (SRM) and validated by a Finite Element Model (FEM). The EEM includes magnetic, electrical and mechanical behavior of the machine incorporating the invertor and can be extended with a thermal and/or mechanical stress analysis. The method is more time efficient than FEMs without serious loss of accuracy. This makes it suitable for investigating long dynamic behavior eg. startup of a microturbine/generator couple or for parameterizing during the design stage of the machine. EEMs are not restricted to SRMs but can be used for any electrical machine. The EEM described in this paper is developed in the context of an autonomous high-speed power generation project. The core unit of the power generation module consists of a SRM/micro-turbine pair. The SRM will be used to start the turbine as well as to generate electrical power once the turbine is self-sustainable.

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Development of a simple nonlinear switched reluctance motor model using measured flux linkage data and curve fit

Cited by 10 publications

References 30 publications

Mathematical Modeling of Flux-Linkage Characteristics of Switched Reluctance Motors Using Polynomial Neural Networks

Mathematical Modeling of Flux-Linkage Characteristics of Switched Reluctance Motors Using Polynomial Neural Networks

Flux linkage characteristic measurement and parameter identification based on hybrid genetic algorithm for switched reluctance motors

An Extended Equivalent Model for a Switched Reluctance Machine

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