摘要
开关磁阻电机(SRM)的双凸极结构和工作原理引发了电机的转矩脉动与径向力脉动问题,进而导致电机振动。而传统的振动抑制策略对两者的综合抑制能力较弱,极大地限制了SRM在高精度需求等领域的应用。为实现对两者的同时抑制,该文提出一种基于磁链波形规划的径向力与转矩分配函数(TSF),并改进电压矢量表,为径向力的降低预留充足的时间,进而抑制径向力脉动;在此基础上,构建磁阻预测值与实际值间的差值模型,利用磁阻与电流的关系提出一种基于磁阻预测的电流误差补偿方法提高模型预测控制(MPC)的精度,进一步提升对转矩脉动和径向力脉动的抑制效果。经不同工况下的实验得出,与传统电流斩波控制(CCC)、脉冲宽度调制(PWM)、模型预测转矩控制(MPTC)法相比,采用新的控制策略可同时使转矩脉动和径向力脉动至少分别降低10.10%和23.47%,电机的振动加速度频谱也验证了具有误差补偿的振动抑制策略的有效性。
With its simple and robust structure and wide speed regulation range,switched reluctance machine(SRM)has a wide range of application scenarios in the fields of multi-all-electric airplanes,electric vehicles,and new energy power generation.However,due to its double-convex pole structure and pulse-powered operation mode,SRM has the shortcomings of large torque ripple and strong vibration during operation,which seriously affects its practical application in the industrial field.In view of the above problems,this paper firstly explores the generation mechanism of vibration noise of SRM,and identifies torque ripple and radial force ripple as the two major causes of motor vibration noise.Based on the idea of model predictive control(MPC),a multi-objective control method of SRM torque ripple and radial force ripple based on the MPC method is proposed along with the control logic of torque predictive control.Based on the magnetic chain waveform planning,the radial force and torque share function(TSF)is constructed,and the torque and radial force waveforms of each phase are planned to realize the simultaneous control of the torque and radial force;considering the changing characteristics of the radial force,the voltage vector table of single-phase conduction area under the MPC is optimized,so that the radial force ripple caused by demagnetization correlation breaks can be reduced;the predicted torque and radial force are taken as the parameters to the cost function,so that the on-line optimization of the switching status of the minimum torque ripple and radial force ripple can be performed.In MPC,the accuracy of the motor electromagnetic characteristic model seriously affects the stability of the control algorithm,in order to improve the stability of the model predictive torque and radial force control(MPTFC)algorithm,and reduce the vibration of the motor,propose a current error compensation method based on the prediction of reluctance.Combined with the operating characteristics of the SRM variable reluctance,from the nonlinear inductance characteristics,according to the parameters of each phase current and voltage,to obtain the relationship between the predicted reluctance and the actual reluctance;combined with the motor state equations,the inverse deduction to obtain a more accurate value of current prediction,to improve the control performance of the MPC algorithm.Finally,a semi-physical experimental verification platform based on a 600 W three-phase 12/8-pole SRM is constructed to experimentally validate the proposed error compensation-based vibration suppression control strategy.Comparisons are made with current chopper control(CCC),PWM control,MPC,model predictive torque control(MPTC),and MPTFC methods at 500 r/min,1000 r/min,and 1500 r/min with a load of 1 N·m and 2 N·m.The experimental results show that,compared with the traditional CCC,PWM and MPTC,the MPTFC strategy has significant suppression effects on torque ripple and radial force ripple,and the vibration acceleration spectrum shows that this method effectively reduces the vibration of the whole machine;the model predictive torque and force control based on current error compensation(CEC-MPTFC)further strengthens the suppression effect,and reduces the torque ripple and radial force ripple by 10.10%and 23.47%,which provides a reference to improve the reliability of the SRM.
作者
葛乐飞
宋佳赫
黄佳乐
樊子祯
宋受俊
Ge Lefei;Song Jiahe;Huang Jiale;Fan Zizhen;Song Shoujun(School of Automation,Northwestern Polytechnical University,Xi’an,710072,China)
出处
《电工技术学报》
北大核心
2025年第17期5434-5447,共14页
Transactions of China Electrotechnical Society
基金
国家自然科学基金(52107055)
航空科学基金(20220040051002)资助项目。
关键词
开关磁阻电机
模型预测控制
振动
误差补偿
Switched reluctance machine
model predictive control
vibration
error compensation
