Switched reluctance motor control without position sensor by using data obtained from finite element method in artificial neural network

2015 ◽  
Vol 98 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Mehmet Polat ◽  
Eyyup Oksuztepe ◽  
Hasan Kurum
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Motor Control ◽  
Switched Reluctance Motor ◽  
Position Sensor ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Using Data

scholarly journals Influence of Control and Structure Parameters on the Starting Performance of a 12/8 Pole Switched Reluctance Motor

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3744
Author(s):  
Jichao Han ◽  
Baojun Ge ◽  
Kai Zhang ◽  
Yang Wang ◽  
Chao Wang
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Switched Reluctance Motor ◽  
Structure Parameters ◽  
Two Phase ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Calculation Results ◽  
Starting Torque ◽  
Element Method

To investigate the influence of control and structure parameters on the starting performance of a switched reluctance motor, a 12/8 pole switched reluctance motor is analyzed in this paper. The novel field-circuit coupled finite element method of switched reluctance motor is proposed in the paper. The influence of the controller on the switched reluctance motor is considered. The influence of rotor initial position angle, starting mode, starting current, and structure parameters on the starting performance of the switched reluctance motor is studied using the field-circuit coupled finite element method. The starting performance of the switched reluctance motor is obtained under the different control and structure parameters. The alternating starting mode of single- and two-phase winding can improve the starting torque of switched reluctance motor (SRM). As the stator pole arc coefficient increases, the starting torque of SRM increases. The appropriate reduction of the air gap length can improve the starting torque of SRM. Experimental results of the prototype are compared with the calculation results, which verifies the reliability of the calculation method and accuracy of the calculation results.

scholarly journals Multi-Physics Multi-Objective Optimal Design of Bearingless Switched Reluctance Motor Based on Finite-Element Method

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2374 ◽  
Author(s):  
Jingwei Zhang ◽  
Honghua Wang ◽  
Sa Zhu ◽  
Tianhang Lu
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Optimal Design ◽  
Switched Reluctance Motor ◽  
Loss Model ◽  
Switched Reluctance ◽  
Multi Objective ◽  
Initial Design ◽  
Reluctance Motor ◽  
Comprehensive Framework

The bearingless switched reluctance motor (BSRM) integrates the switched reluctance motor (SRM) with the magnetic bearings, which avoids mechanical bearings-loss and makes it promising in high-speed applications. In this paper, a comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on finite-element method (FEM) is proposed. At first, the 2-D electromagnetic model of a fabricated initial design prototype is built and solved by the open-source FEM software, Elmer. The iron loss model in Elmer based on the Fourier series is modified by a transient iron loss model with less computation time. Besides, a simplified lumped-parameter (LP) thermal model of the BSRM is applied to estimate the temperature rise of BSRM in the steady state. Then, the comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on FEM is proposed. The objectives, constraints, and decision variables for optimization are determined. The multi-objective genetic particle swarm optimizer is utilized to obtain the Pareto front of optimization. The electromagnetic performance of the final optimal design is compared with the initial design. Comparison results show that the average electromagnetic torque and the efficiency are significantly enhanced.

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