基于优化BP神经网络的开关磁阻电机定子电阻辨识方法

为解决直接转矩控制下的开关磁阻电机低速运行时磁链计算受电阻变化影响比较大的问题,详细观察分析了电阻对于相电流的影响,通过比对电阻可调的电机模型与实际的电机模型的输出电流,提出了一种基于优化BP神经网络的电阻辨识器。优化BP网络数学理论,结构简单,学习算法清晰明白,基于该网络的算法能够对变化的定子电阻进行辨识。将该方法置于Simulink控制系统上进行仿真,同时比较有无电阻辨识器前后仿真波形。试验表明,该电阻辨识方法可以提高开关磁阻电机低速运行时系统性能。

Stator Resistance Identification Method of Switched ReluctanceMotor Based on Optimized BP Neural Network

When switched reluctance motor was in the status of slow running under direct torque control, calculation of flux was greatly influenced by resistance. In order to solve the issue above. The study observed and analyzed carefully about the relation between resistance and phase current, through comparing the output current between resistance variable motor model and actual motor model, proposed a solution of resistance estimation based on optimized BP neural networks. Optimized BP neural networks had sufficient mathematical theory, with simple structure and clear algorithm. The algorithm based on BP neural networks could recognize variable stator resistance. Put this algorithm into action in the Simulink control system, then comparing the test results between with resistance estimation and without resistance estimation. Experimental results showed that this resistance estimation method could improve system performance when the switched reluctance motor was in the status of slow running.