TY - GEN
T1 - Design of IE4 Level Synchronous Reluctance Machines with Different Number of Poles
AU - Jia, Shaofeng
AU - Zhang, Ping
AU - Liang, Deliang
AU - Dai, Maocun
AU - Liu, And Jinjun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - With the continuous requirement for improvement of international motor efficiency standards, the research and exploitation of motor with higher efficiency standards has become an important development trend in the motor industry. Synchronous reluctance machines (SynRMs) with simple rotor construction, no need of permanent magnets and similar price compared to the induction motors are considered to be another promising type of motor in addition to permanent magnet motors that can achieve IE4 efficiency standards. This paper describes the design and optimization of SynRM with different number of poles to achieve IE4 efficiency level. First, the sample data was obtained by the finite-element analysis (FEA), then in order to establish the relationship between the design parameters, saturation effect and the motor performance, such as torque, torque ripple, etc., the optimization model of the SynRMs was established by using extreme learning machine (ELM). Finally, a genetic algorithm (GA) was used to search the best optimization performances and geometrical parameters. FEA proves the effectiveness and validity of the proposed method.
AB - With the continuous requirement for improvement of international motor efficiency standards, the research and exploitation of motor with higher efficiency standards has become an important development trend in the motor industry. Synchronous reluctance machines (SynRMs) with simple rotor construction, no need of permanent magnets and similar price compared to the induction motors are considered to be another promising type of motor in addition to permanent magnet motors that can achieve IE4 efficiency standards. This paper describes the design and optimization of SynRM with different number of poles to achieve IE4 efficiency level. First, the sample data was obtained by the finite-element analysis (FEA), then in order to establish the relationship between the design parameters, saturation effect and the motor performance, such as torque, torque ripple, etc., the optimization model of the SynRMs was established by using extreme learning machine (ELM). Finally, a genetic algorithm (GA) was used to search the best optimization performances and geometrical parameters. FEA proves the effectiveness and validity of the proposed method.
KW - Extreme learning machine (ELM)
KW - IE4 efficiency standards
KW - optimization
KW - synchronous reluctance machine (SynRM)
KW - torque ripple
UR - https://www.scopus.com/pages/publications/85077121054
U2 - 10.1109/ICEMS.2019.8922537
DO - 10.1109/ICEMS.2019.8922537
M3 - 会议稿件
AN - SCOPUS:85077121054
T3 - 2019 22nd International Conference on Electrical Machines and Systems, ICEMS 2019
BT - 2019 22nd International Conference on Electrical Machines and Systems, ICEMS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference on Electrical Machines and Systems, ICEMS 2019
Y2 - 11 August 2019 through 14 August 2019
ER -