Abstract
The main objective of modeling a switched reluctance machine is to derive a mathematical function to relate the outputs to the inputs. Due to the nonlinear relationship between the variables of torque, flux linkage, current and angular position of the rotor, Switched Reluctance Machine (SRM) modeling is a very challenging task and an open problem. Modeling is usually done in two situations, modeling a single machine, or modeling a set of machines. Each one must fulfill different requirements. This work presents a survey of different SRM modeling approaches, evaluating its advantages and limitations when modeling a single machine or a set of machines.
Highlights
The Switched Reluctance Machine (SRM) have inherent several advantages such as high efficiency, simple construction, robustness, high reliability, low cost, fault tolerance and absence of magnets (Ahn 2011)
The experimental methods are not included in this table because they correspond to the actual data of the machine and are used to verify the methods
We can observe that the intelligent methods present the best relation between accuracy and computational cost; this happens because these models use experimental data in their training and present better accuracy
Summary
The Switched Reluctance Machine (SRM) have inherent several advantages such as high efficiency, simple construction, robustness, high reliability, low cost, fault tolerance and absence of magnets (Ahn 2011). In the context of closed-loop sensorless SRM drives, the modeling has been used in order to avoid the use of motion sensors in the drive, and methods of flux, torque and speed estimation have been developed. These are signal-processing models of machine equations,. A single SRM modeling consists of mapping the relationship between flux linkage, current and rotor position. These values depend on the geometry of the machine and B-H characteristics of the lamination material. Analytical methods have been widely used for a while, and are a good choice in some studies such as pre-design for example, but in recent years they have lost room for the accuracy and versatility of FEM and for the ability to model nonlinear systems and generalization of intelligent methods
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