微粒群优化相关向量机的开关磁阻电机转子位置自检测  

Rotor Position Self-Detection of Switched Reluctance Motor Using Relevance Vector Machine with Particle Swarm Optimization

作　　者：项倩雯[1] 袁野[1] 于焰均[1] XIANG Qianwen;YUAN Ye;YU Yanjun(School of Electrical and Information Engineering,Jiangsu Univesity,Zhenjiang 212013,China)

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013

出　　处：《电机与控制应用》2018年第10期100-105,共6页Electric machines & control application

基　　金：国家自然科学基金项目(51707082);江苏省自然科学基金青年基金项目(BK20150510;BK20170546);江苏大学科研启动基金项目(14JDG075)

摘　　要：将电机在有位置传感器运行条件下采样获得的磁链、电流和转角作为样本数据,基于相关向量机回归理论,通过对样本数据的训练与学习,构建开关磁阻电机转子位置的非线性相关向量机预测模型。为提高预测模型的拟合精度和泛化能力,训练过程中采用微粒群算法优化相关向量机的核函数参数。以1台三相12/8极样机为例,开展仿真研究,结果表明:该预测模型能够正确地检测出开关磁阻电机的转子位置,并且检测精度较高。The motors’flux-linkage,current and angle obtained from the system with sensors were chosen as the sample data,and the predictive model of rotor position based on relevance vector machine was built by training these sample data.In order to improve the fitting precision and generalization ability of the predictive model,the kernel function parameter in relevance vector machine was optimized by the particle swarm algorithm.By simulation on the test motor,it was verified that the proposed predictive model could estimate the rotor position accurately in the simulation condition and had satisfactory estimation precision.

关 键 词：相关向量机 微粒群 开关磁阻电机 预测建模 自检测 

分 类 号：TM352[电气工程—电机]