A Switched Reluctance Machine Rotor Position Estimator: A Neural Network Application

1993 ◽  
Author(s):  
Jenifer M. Shannon
Keyword(s):  
Neural Network ◽  
Switched Reluctance Machine ◽  
Switched Reluctance ◽  
Rotor Position ◽  
Reluctance Machine ◽  
Network Application

scholarly journals Numerical Investigation on Vibration Performance of an Improved Switched Reluctance Machine with Double Auxiliary Slots

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhengyuan Gao ◽  
Shanming Wang ◽  
Zhiguo An ◽  
Pengfei Sun
Keyword(s):  
Acoustic Noise ◽  
Structural Characteristics ◽  
Orthogonal Experimental Design ◽  
Switched Reluctance Machine ◽  
Switched Reluctance ◽  
Rotor Position ◽  
Turn On ◽  
Reluctance Machine ◽  
Noise Limit ◽  
Optimal Set

Considerable vibration and acoustic noise limit the further application of Switched Reluctance Machine (SRM) due to its structural characteristics and working principle. An improved SRM model with double auxiliary slots (DAS) was proposed, in which the direction of the magnetic line of force was adjusted, and the radial magnetic density in the air gap was reduced by changing the local tooth profiles of the stator and the rotor. The effects of initial rotor position and turn-on angle and turn-off angle on radial Electromagnetic Force (EMF) and maximum torque were investigated. The results indicate the radial EMF and torque increase significantly with the advancement of the turn-on angle or the delay of the turn-off angle. In the orthogonal experimental design, initial rotor position, turn-on angle, and turn-off angle were taken as the factors, and the optimal set of parameters that minimized radial EMF was determined according to a greater output torque. In contrast to conventional SRM, the radial EMF of the SRM with DAS significantly reduces when the optimal set is applied.

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

2020 ◽  
Vol 39 (6) ◽  
pp. 1411-1430
Author(s):  
Ana Camila Ferreira Mamede ◽  
José Roberto Camacho ◽  
Rui Esteves Araújo ◽  
Igor Santos Peretta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Computational Cost ◽  
Neural Network Modeling ◽  
Switched Reluctance Machine ◽  
Data Set ◽  
Content Type ◽  
Switched Reluctance ◽  
Reluctance Machine ◽  
Artificial Neural

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.

Radial Force Characteristics of Switched Reluctance Machine

1999 ◽  
Author(s):  
Neil R. Garrigan ◽  
Albert Storace ◽  
Wen L. Soong ◽  
Thomas A. Lipo ◽  
Charles M. Stephens
Keyword(s):  
Radial Force ◽  
Switched Reluctance Machine ◽  
Switched Reluctance ◽  
Reluctance Machine ◽  
Force Characteristics
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