直接转矩控制下开关磁阻电机的参数辨识研究

发布时间：2018-06-27 15:00

本文选题：开关磁阻电机 + 直接转矩控制　；参考：《大连海事大学》2017年硕士论文

【摘要】：由于开关磁阻电机具有结构简单、成本低、可靠性高、性能优越等特点,受到人们广泛关注。近年来,许多学者将直接转矩控制方法应用于开关磁阻电机,而直接转矩控系统准确性与电机实时反馈量的准确性息息相关。实际上,标准直接转矩控制系统只有电压、电流传感器,在只有电压、电流反馈的控制过程中,由二者计算得到磁链是控制准确与否的关键因素,而传统的积分计算磁链存在许多问题,故围绕磁链计算的相关参数的辨识是准确判别磁链的保证。首先由于在积分模型中,定子电阻是计算磁链的关键,特别是低速时,影响更大。定子电阻主要受到温度变化的影响,而为了避免控制系统的复杂性,温度传感器是不被允许的。本文通过优化BP神经网络,研究两个不同定子电阻模型间的电流与定子电阻的关系,在传统BP神经网络的基础上,优化选择各层级神经元个数,优化选择学习速率,提出一种基于电流误差的定子电阻辨识方法。仿真结果表明,该定子电阻辨识器算法简单、实时性好,能够满足控制需求。其次针对于积分模型的误差随着积分累加的问题,Torrey提出另外一种磁链的模型,该模型具有高精度拟合特性,能够准确的描述磁链特性,但其参数辨识比较困难。针对该问题,提出一种改进的遗传算法用于辨识开关磁阻电机的模型参数,改进遗传算法能够避免过早成熟的问题。实验结果显示,该种算法比基本遗传算法对解决本问题有更好的寻优能力。在Matlab2014a/Simulink仿真环境下,本文建立了加入定子电阻辨识模块和Torrey磁链模型的开关磁阻电机直接转矩控制系统仿真模型,仿真结果表明,两种算法对于传统直接转矩控制有很好的优化。同时验证了两种算法的性能,为两种算法的应用做好推广的基础。
[Abstract]:Because of its simple structure, low cost, high reliability and superior performance, switched reluctance motor (SRM) has attracted wide attention. In recent years, many scholars have applied direct torque control method to switched reluctance motor, and the accuracy of direct torque control system is closely related to the accuracy of real-time feedback. In fact, there are only voltage and current sensors in the standard direct torque control system. In the control process with only voltage and current feedback, the flux chain is the key factor for the accuracy of the control. However, there are many problems in the traditional integral calculation of flux chain, so the identification of the related parameters around the flux chain calculation is the guarantee of the accurate identification of the flux chain. Firstly, in the integral model, stator resistance is the key to the calculation of flux, especially at low speed. Stator resistance is mainly affected by temperature change, but temperature sensor is not allowed to avoid the complexity of control system. By optimizing BP neural network, the relationship between current and stator resistance between two different stator resistance models is studied. On the basis of traditional BP neural network, the number of neurons in each level is optimized and the learning rate is optimized. A stator resistance identification method based on current error is proposed. The simulation results show that the stator resistance identifier is simple and real-time, and can meet the control requirements. Secondly, Torrey puts forward another kind of flux model aiming at the problem that the error of the integral model accumulates with the integration. The model has the characteristic of high precision fitting and can describe the flux characteristic accurately, but its parameter identification is difficult. To solve this problem, an improved genetic algorithm is proposed to identify the model parameters of switched reluctance motor. The improved genetic algorithm can avoid the problem of premature maturity. Experimental results show that this algorithm has better ability to solve this problem than the basic genetic algorithm. In the Matlab 2014a / Simulink simulation environment, the simulation model of switched reluctance motor direct torque control system with stator resistance identification module and Torrey flux model is established. The simulation results show that the two algorithms are very good for traditional direct torque control. At the same time, the performance of the two algorithms is verified, which is the basis for the application of the two algorithms.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM352;TP18

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