Stator Flux Observer of SRM Based on Particle Swarm Optimized Recurrent Neural Network

doi:10.11985/2017.10.001

Abstract

Abstract:

For improving SRM stator flux observer,a particle swarm optimization (PSO) real-time recurrent neural networks (RNN) stator flux observer is presented under the SRM DTC control system．The network is trained off-line．In the off-line training process with the training data, the number and locations of the hidden units of neural network are obtained,and during the process of the learning,the neural network is built with a much simpler and tighter structure, and stronger generalization ability to form an efficient nonlinear map,and then it facilitates the elimination of the stator flux. The SRM DTC system are simulated with the new stator flux observer, and compared with the traditional RNN stator flux observer. It is proved the new stator flux observer improves the convergence speed and also has the merits of higher precision and strong generalization ability.

Key words: Switch reluctance machine, direct torque control, stator flux observer, particle swarm optimization, recurrent neural networks

CLC Number:

TM352

Wang Xuesong,Xu Aide,Zhao Zhonglin,Zhao Xianchao. Stator Flux Observer of SRM Based on Particle Swarm Optimized Recurrent Neural Network[J]. Journal of Electrical Engineering, 2017, 12(10): 1-8.

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