Accurate modeling of switched reluctance machine based on hybrid trained WNN | Zendy

Shoujun Song | Zendy; Lefei Ge | Zendy; Shaojie Ma | Zendy; Man Zhang | Zendy

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Accurate modeling of switched reluctance machine based on hybrid trained WNN

Author(s) -

Shoujun Song,

Lefei Ge,

Shaojie Ma,

Man Zhang

Publication year - 2014

Publication title -

aip advances

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.421

H-Index - 58

ISSN - 2158-3226

DOI - 10.1063/1.4873535

Subject(s) - matlab , computer science , artificial neural network , nonlinear system , convergence (economics) , switched reluctance motor , stability (learning theory) , genetic algorithm , gradient descent , backpropagation , control theory (sociology) , algorithm , artificial intelligence , machine learning , engineering , mechanical engineering , quantum mechanics , rotor (electric) , economics , economic growth , operating system , physics , control (management)

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

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