Two Simple Analytical Models, Direct and Inverse, for Switched Reluctance Motors

Someşan, Liviu; Padurariu, Emil; Viorel, Ioan-Adrian

doi:10.2528/pierm12103001

Cited by 6 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the SRM depends heavily on its design and control, which allows reduced ripple in torque and improved torque-speed characteristics (Somesan et al, 2013). Few researchers have addressed the problem of the sensitivity of SRM performance in relation to its dimensions.…”

Section: Switched Reluctance Machine Model Considerationsmentioning

confidence: 99%

Moore-Penrose pseudo-inverse and artificial neural network modeling in performance prediction of switched reluctance machine

Mamede

Camacho

Araújo

et al. 2020

COMPEL

View full text Add to dashboard Cite

Purpose The purpose of this paper is to present the Moore-Penrose pseudoinverse (PI) modeling and compare with artificial neural network (ANN) modeling for switched reluctance machine (SRM) performance. Design/methodology/approach In a design of an SRM, there are a number of parameters that are chosen empirically inside a certain interval, therefore, to find an optimal geometry it is necessary to define a good model for SRM. The proposed modeling uses the Moore-Penrose PI for the resolution of linear systems and finite element simulation data. To attest to the quality of PI modeling, a model using ANN is established and the two models are compared with the values determined by simulations of finite elements. Findings The proposed PI model showed better accuracy, generalization capacity and lower computational cost than the ANN model. Originality/value The proposed approach can be applied to any problem as long as experimental/computational results can be obtained and will deliver the best approximation model to the available data set.

show abstract

Section: Switched Reluctance Machine Model Considerationsmentioning

confidence: 99%

Moore-Penrose pseudo-inverse and artificial neural network modeling in performance prediction of switched reluctance machine

Mamede

Camacho

Araújo

et al. 2020

COMPEL

View full text Add to dashboard Cite

show abstract

“…Most of these drawbacks can be mitigated by the development of control methods and/or geometry changes in its magnetic circuits. Accurate calculations of radial force and design of improved motor structures are the main routes for reducing SRM vibration (Somesan et al 2013). As far as the novelty is concerned, the main objective is to clarify the impact of the skewing methods in the SRM.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Skewing Effects on Radial Force for Different Topologies of Switched Reluctance Machines: 6/4 SRM, 8/6 SRM, and 12/8 SRM

Touati¹,

Araújo²,

Mahmoud³

et al. 2023

UPjeng

View full text Add to dashboard Cite

Reducing vibration and noise in electrical machines for a given application is not an easy task, especially when the application imposes some restrictions. There are many techniques for reducing vibration based on design or on control. Switched reluctance motors (SRMs) have a double-saliency structure, which results in a radial pulsation force. Consequently, they cause vibration and acoustic noise. This paper investigates the correlation between the radial force and the skew angle of the stator and/or rotor circuits. The analysis is computed from two-dimensional (2D) transient magnetic finite-element analysis (FEA) of three machine topologies, namely the 12/8 three-phase SRM, the 6/4 three-phase SRM and the 8/6 four-phase SRM. Compared to SRM, these topologies have the same basic dimensions (stator outer diameter, rotor outer diameter, and length) and operate in the same magnetic circuit saturation. The flux linkage and torque characteristics of the different motors are presented. The radial force distributed on the stator yoke under various skewing angles is studied extensively by FEA for the three machines. It is also demonstrated the effect of skewing angles in the reduction of radial force without any reduction in torque production.

show abstract

“…The performance of the switched reluctance machine depends heavily on its design and control, which allows reduced ripple in torque and improved torquespeed characteristics [3]. Few researchers have addressed the problem of the sensitivity of SRM performance in relation to its dimensions.…”

Section: Introductionmentioning

confidence: 99%

Review of Rotary Switched Reluctance Machine Design and Parameters Effect Analysis

Mamede¹,

Camacho²,

Araújo³

2020

Modelling and Control of Switched Reluctance Machines

View full text Add to dashboard Cite

The switched reluctance machine (SRM) has gained much interest in industrial applications, wind power systems, and electric vehicles. This happened because its main disadvantages, such as the ripple in the torque, were overcome due to continuous research and its advantages, such as simple and robust construction, ability to operate at high speeds and variable speeds, insensitivity to high temperatures, and fault tolerance, have made the SRM the right machine for many applications. The SRM project is apparently similar to the traditional machine design, but diverges on several points due to the unique features of the SRM. Over the years, several authors have proposed different project methodologies for SRM, each with its own particularities and often contradicting each other. Thus, for a beginner designer, the SRM project is a challenge from choosing the right design methodology to choosing the values of some dimensions, which are often empirical. This chapter aims to offer the beginner designer a detailed review of the main SRM design methodologies. In addition, an effect analysis will provide useful insights on how each design variable affects machine performance. The designer will thus have important data on which to base his choices during the SRM design.

show abstract

Two Simple Analytical Models, Direct and Inverse, for Switched Reluctance Motors

Cited by 6 publications

References 27 publications

Moore-Penrose pseudo-inverse and artificial neural network modeling in performance prediction of switched reluctance machine

Moore-Penrose pseudo-inverse and artificial neural network modeling in performance prediction of switched reluctance machine

Analysis of Skewing Effects on Radial Force for Different Topologies of Switched Reluctance Machines: 6/4 SRM, 8/6 SRM, and 12/8 SRM

Review of Rotary Switched Reluctance Machine Design and Parameters Effect Analysis

Contact Info

Product

Resources

About