Accurate modeling of switched reluctance machine based on hybrid trained WNN

Abstract

According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, the nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.

Artificial Neural Network and Data Dimensionality Reduction Based on Machine Learning Methods for PMSM Model Order Reduction

“…The implementation of an ANN consists in defining the model's inputs and outputs together with their connection given by the network's architecture and training algorithm. In the field of electrical machines, ANNs are implemented for machine control, anomaly detection and fault diagnosis, or for electromagnetic torque estimation, as presented in references [2]- [4]. A switched reluctance motor's (SRM) accurate model based on hybrid trained wavelet neural network (WNN) that combines genetic algorithm (GA) with gradient descent (GD) methods to train the network is proposed in [2].…”

“…In the field of electrical machines, ANNs are implemented for machine control, anomaly detection and fault diagnosis, or for electromagnetic torque estimation, as presented in references [2]- [4]. A switched reluctance motor's (SRM) accurate model based on hybrid trained wavelet neural network (WNN) that combines genetic algorithm (GA) with gradient descent (GD) methods to train the network is proposed in [2]. In [3] and [4], torque estimation methods and machine control techniques based on neural network are detailed, reaching highly accurate results.…”

Indirect measurement and extreme learning machine based modelling for flux linkage of doubly salient electromagnetic machine

“…With the development of artificial neural network (ANN), learning machines such as support vector machine (SVM), wavelet neural network (WNN), and extreme learning machine (ELM) are introduced to model electrical machines with non-linear characteristics. Least square SVM (LSSVM) and hybrid trained WNN are utilised in SRM modelling [13,14]. ELM is introduced to solve the non-linear relationships among angle, torque, and speed in the advanced angle control scheme of the DSEM [15].…”

Accurate model of switched reluctance motor based on indirect measurement method and least square support vector machine

“…However, they all need great number of sample data and have poor generalisation ability. Artificial intelligence methods [17][18][19][20] are also developed by researchers owing to the good non-linear approximation ability. However, almost all of these artificial intelligence methods need some prior knowledge and depend on personal experience.…”