finite element analysis;
rotors;
permanent magnet machines;
losses;
laminations;
eddy current losses;
synchronous motors;
permanent magnet motors;
synchronous machines;
torque;
magnetic flux;
radial flux permanent magnet synchronous machine;
wind generation application;
real-time rotational iron-loss computation;
bulk conductivity;
magnetic induction vector locus;
iron component;
multiple magnetic antennae;
torque-frequency-loss computation;
total iron losses;
iron losses prediction;
time-domain computation;
rotational iron losses;
finite-element technique;
iron-loss characteristics;
INDUCTION-MOTOR;
POWER LOSSES;
D O I:
10.1049/iet-epa.2018.5395
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
This study investigates an advanced finite-element (FE) technique for the evaluation of rotational iron losses based on a time-domain computation, where the bulk conductivity of the core materials is considered. The iron-loss characteristics are discussed for a radial flux permanent magnet synchronous machine (PMSM) for a wind generation application, with closed slots and outer rotor topology. The following factors are taken into account: (i) a real-time rotational iron-loss computation; (ii) the bulk conductivity of the steel laminations; and (iii) the influence of the controller harmonics on the system during transient conditions. The magnetic induction vector locus of each iron component is also discussed, where the magnetic induction is numerically modelled [three-dimensional (3D) finite element analysis (FEA)], computed using multiple magnetic antennae and is also experimentally verified. This comparative study shows a torque-frequency-loss computation that is presented from low to high frequencies (50-800) Hz. The FE model of the total iron losses for the PMSM using both pure sinusoidal and proportional-integral pulse-width modulation currents is studied and experimentally verified on a surface-mounted PMSM. The proposed method of iron losses prediction significantly reduced the rate of error between 3D FEA and experimental data to 1.7%.