Adaptive Step-Size Predictive PLL Based Rotor Position Estimation Method for Sensorless IPMSM Drives

被引:12
|
作者
Zhang, Guoqiang [1 ]
Yan, Yunhao [1 ]
Wang, Qiwei [1 ]
Ding, Dawei [1 ]
Wang, Gaolin [1 ]
Ding, Li [1 ]
Li, Yunwei [2 ]
Xu, Dianguo [1 ]
机构
[1] Harbin Inst Technol, Sch Elect Engn & Automat, Harbin 150001, Peoples R China
[2] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 1H9, Canada
基金
中国国家自然科学基金;
关键词
Phase locked loops; Rotors; Estimation; Sensorless control; Iterative methods; Cost function; Observers; Adaptive step-size iterative search; high-frequency square-wave injection; interior permanent magnet synchronous motor (IPMSM); predictive phase-locked loop (PPLL); sensorless control; PHASE-LOCKED LOOP; INJECTION; MOTOR; BANDWIDTH; SIGNAL; IMPROVEMENT; MACHINES; STRATEGY;
D O I
10.1109/TPEL.2024.3365735
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
The fixed-gain position observer for sensorless interior permanent magnet synchronous motor (IPMSM) drives requires repeated trials for parameter tuning and has poor dynamic response capability. A novel adaptive step-size predictive phase-locked loop (ASS-PPLL) based rotor position estimation method is proposed to improve dynamic performance in this article. A cost function using position-tracking error decoupled from a high-frequency current response through a high-frequency square-wave injection is established. In addition, the step-size and direction are automatically adjusted by the predefined cost function to speed up the iterative search for an optimal rotor position estimate in a finite position set. Compared with the fixed-gain observers, the proposed ASS-PPLL effectively improves dynamic performance without a complex and time-consuming parameter tuning process. Compared with the conventional predictive PLL, the proposed method reduces the computational burden with fewer iterations, while ensuring the position estimation accuracy. Finally, the effectiveness of the proposed ASS-PPLL is comprehensively verified on a 2.2-kW IPMSM drive platform.
引用
收藏
页码:6136 / 6147
页数:12
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