Switched Reluctance Machine Modeling through Multilayer Neural Networks

Abstract

The work deals with the application of artificial neural networks (ANNs) in the modeling of switched reluctance machines (SRMs). The performance of a SRM is determined by its geometry, materials used and levels of excitation. In this way, this work investigates the influence of the stator and rotor back iron thickness in the performance of SRM. A multilayer neural network is proposed to learn the nonlinear characteristics of the motor. Data of flux linkages and torque are obtained through simulations of finite elements and used for ANN training. The algorithm developed in Octave allows the user to adjust the network parameters. The results presented confirm the feasibility of using ANN to establish a predictive model of SRM performance, thus enabling further investigation in the future.

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

“…Recently, intelligent approximation algorithms such as fuzzy logic and artificial neural networks (ANNs) that are suitable for modeling non-linear systems have been used to estimate or model the characteristics of SRM (Mamede et al, 2018;Bajec et al, 2011;Lu et al, 2003;Kucuk et al, 2009;Evangeline et al, 2016). The accuracy of these models depends strongly on the quality of the data used for their training, which are mostly obtained through finite element simulations or direct measurement.…”