scholarly journals SRM with 8/6 magnetic system topology for electric drive of mine battery electric locomotives: design and modelling

2022 ◽  
Vol 1211 (1) ◽  
pp. 012004
Author(s):  
V N Antipov ◽  
A D Grozov ◽  
A V Ivanova
Keyword(s):  
Neural Network ◽  
Control System ◽  
Electric Drive ◽  
Magnetic System ◽  
Switched Reluctance Motor ◽  
Dc Motor ◽  
Network Control ◽  
Electric Locomotive ◽  
Switched Reluctance ◽  
Reluctance Motor

Abstract The paper deals with the development and simulation results of the switched reluctance motor for electric drive of mine battery electric locomotives instead of the DRT-14 DC motor. The switched reluctance motor parameters are obtained based on numerical calculation of the magnetic field by QuickField program and are embedded in MATLAB/Simulink model of a switched reluctance motor created for 8/6 magnetic system configuration. SRM-14-615 has the mechanical characteristics as DC motor DRT-14 and meets the required operating modes as part of the AM8D mine battery electric locomotive. Also two types of models using methods and techniques of the artificial intelligence theory are presented: a model with a fuzzy control system in the Fuzzy Logic Toolbox package and a model with a neural network control system in the Neural Network Toolbox package.

Position Sensorless Control System of SRM Based on Improved BP Neural Network

2012 ◽  
Vol 468-471 ◽  
pp. 2187-2192 ◽  
Author(s):  
Li Xiao ◽  
He Xu Sun ◽  
Feng Gao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Computing Time ◽  
Switched Reluctance Motor ◽  
Detection Accuracy ◽  
Training Time ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Position Sensorless

Due to the shortcomings of long training time and slow convergence of BP neural network, this paper presents a new improved method that weight is no longer a constant but turned into a function of adjustable parameters. After the training of the improved BP neural network is completed, the network can map the nonlinear relationship between motor current, flux and rotor position. Based on the analysis of the unique structural properties of switched reluctance motor, this paper also proposes a method of greatly reducing the sample data to save computing time. Simulation results show that this method simplifies the complexity of the control system and improve detection accuracy, thus realize position sensorless detection of the switched reluctance motor.

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