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个人信息Personal Information
教授 博士生导师
招生学科专业:
电气工程 -- 【招收博士、硕士研究生】 -- 自动化学院
能源动力 -- 【招收博士、硕士研究生】 -- 自动化学院
性别:男
毕业院校:南京航空航天大学
学历:南京航空航天大学
学位:工学博士学位
所在单位:自动化学院
联系方式:apsc-zzr@nuaa.edu.cn
电子邮箱:
Indirect measurement and extreme learning machine based modelling for flux linkage of doubly salient electromagnetic machine
点击次数:
所属单位:自动化学院
发表刊物:IET ELECTRIC POWER APPLICATIONS
关键字:WIND POWER-GENERATION DESIGN
摘要:Doubly salient electromagnetic machines (DSEMs), which are characterised with fault tolerance, low cost, high reliability, and de-excitation ability, are gaining more and more attention in safety-critical and hash environment applications, such as the aircraft generation systems. Nevertheless, the non-linear and strong coupled characteristics of the flux linkage is the obstruct crux in DSEM modelling. The DSEM model is the critical part of the system model, which is the foundation of theoretical analysis, control strategy developing, and stability analysis. This study is aimed to demonstrate the feasibility of indirect flux linkage measurement method, as well as the effectiveness of the extreme learning machine (ELM)-based flux linkage modelling method. The basic principles of the indirect measurement are analysed and the measurement processes excluding rotor-clamping devices are proposed. The ELM is employed to high-precision flux linkage modelling with high efficiency. A three-phase 12/8-pole DSEM is tested to confirm the validity of the proposed modelling method. Both finite element analysis and experimental results are presented, verifying the effectiveness of the indirect flux linkage measurement and the ELM-based modelling method.
ISSN号:1751-8660
是否译文:否
发表时间:2018-05-01
合写作者:许彦武,于立,卞张铭
通讯作者:张卓然