Modeling of a switched reluctance motor under stator winding fault condition

Chen, H.; Yan, Wenju; Wang, Q.; Lu, Shengli; Chen, Z.

doi:10.1109/intmag.2015.7157245

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…Among the artificial intelligence techniques, fuzzy logic and artificial neural networks (ANNs) are employed to model the nonlinear magnetic characteristics of SRMs. They have been reported in SRM modeling in [28][29][30][31]. In [28], a two-layer recurrent ANN is employed to identify the damper currents and resistance of phase winding from operating data.…”

Section: Artificial Intelligence-based Modelsmentioning

confidence: 99%

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Modeling and Simulation of Switched Reluctance Machines

Hamouda¹,

Számel²

2020

Modelling and Control of Switched Reluctance Machines

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This chapter discusses the modeling and simulation approaches for switched reluctance machines (SRMs). First, it presents the modeling methods for SRMs including analytical models, Artificial intelligence based models, and lookup tables based models. Furthermore, it introduces the finite element method (FEM) and experimental measurement methods to obtain high fidelity magnetic characteristics for SRMs. Step-by-step procedure is explained for SRM modeling and analysis using FEM. The direct and indirect measurement methods of SRM magnetic characteristics are included, comparison between the measured and FEM-calculated characteristics is achieved, and good agreement is seen. In addition, this chapter gives the mathematical modeling of SRM, and explains its model development using MATLAB/Simulink environment. Simulation and experimental results are obtained, a very good agreement is observed.

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Section: Artificial Intelligence-based Modelsmentioning

confidence: 99%

“…Likewise, complex expressions and fitting algorithms are circumvented. In [29], a four-layer back-propagation (BP) ANN is applied to estimate the electromagnetic characteristics under the stator winding fault condition. Similarly, fuzzy logic systems also have strong nonlinear approximation ability.…”

Section: Artificial Intelligence-based Modelsmentioning

confidence: 99%

Modeling and Simulation of Switched Reluctance Machines

Hamouda¹,

Számel²

2020

Modelling and Control of Switched Reluctance Machines

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“…A comprehensive analysis of SRM drive under different fault conditions is presented in [33] and a method to predict the performance characteristics under normal and fault operating conditions is presented in [34]. The use of ANN is also used in [35] to model stator winding fault.…”

Section: Introductionmentioning

confidence: 99%

“…This problem was addressed in some inverters like the NPC, T-Type and Flying capacitor [47]- [49]. A NPC multilevel topology adapted for the SRM presenting redundant states is also presented in [35], [39]. However, these works do not address the problem of using the redundant states for the balance of the capacitors or transistor fault condition.…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Fault-Tolerant Power Converter for a Switched Reluctance Machine Drive

et al. 2020

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Reliability in the electrical drives is becoming an important issue in many applications. In this context, the reliability associated to the switched reluctance machine (SRM) is also an important area of research. One of the major problems, that strongly affect its operation, are drive power semiconductors faults. Typical power converter topologies used in SRM drives cannot handle faults in their power semiconductors. So, this paper presents a power converter topology that provides fault-tolerant capabilities to the drive under a switch fault. This power converter will be used considering that a change in the direction of the current that flows in the SRM windings does not affect the behavior of the machine. Besides that, the proposed power converter will allow to generate multilevel phase voltages in order to apply different voltage levels as function of the SRM speed. A laboratory power converter was developed to test the SRM drive in normal and faulty conditions. From the obtained results it was possible to verify the fault-tolerant capability of the drive under switch faults in different devices and failure modes. It was also possible to confirm the multilevel operation of the drive.

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“…Efforts have been made in recent years to alleviate the need for conducting exhaustive measurements through the development of mathematical methods for mapping the complete flux linkage data of an SRLM. Many corresponding mathematical methods have been proposed, which include interpolation methods [14,15], function fitting methods [16][17][18], and intelligent modelling methods [19]. The above-mentioned methods conduct flux linkage mapping based on the measurement of a limited number of magnetisation curves, and therefore greatly reduce the time required for conducting measurements.…”

Section: Introductionmentioning

confidence: 99%

High‐precision non‐linear modelling method for a switched reluctance linear machine by interpolation with using variant sigmoid function

Nie

Chen

Wang

et al. 2020

IET Electric Power Applications

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This study presents a non-linear modelling method for a switched reluctance linear machine (SRLM), which achieves high modelling precision while requiring no prior knowledge regarding the specific structural dimensions of an SRLM. The proposed modelling method provides accurate flux linkage data based on limited experimental measurements through an interpolation process using a variant sigmoid function. The accuracy of the interpolation process is further increased by considering mutual coupling among the three phases. The performance of the proposed method is validated experimentally by comparing it with the performances of the Fourier series method and the two-dimensional finite element method. The results demonstrate that the proposed method achieves greater modelling precision than the other methods considered in modelling an SRLM.

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Modeling of a switched reluctance motor under stator winding fault condition

Cited by 4 publications

References 13 publications

Modeling and Simulation of Switched Reluctance Machines

Modeling and Simulation of Switched Reluctance Machines

A Multilevel Fault-Tolerant Power Converter for a Switched Reluctance Machine Drive

High‐precision non‐linear modelling method for a switched reluctance linear machine by interpolation with using variant sigmoid function

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