Global Identification of Electrical and Mechanical Parameters in PMSM Drive Based on Dynamic Self-Learning PSO

被引:157
作者
Liu, Zhao-Hua [1 ]
Wei, Hua-Liang [2 ]
Li, Xiao-Hua [1 ]
Liu, Kan [3 ]
Zhong, Qing-Chang [4 ]
机构
[1] Hunan Univ Sci & Technol, Sch Informat & Elect Engn, Xiangtan 411201, Peoples R China
[2] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England
[3] Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Hunan, Peoples R China
[4] IIT, Dept Elect & Comp Engn, Chicago, IL 60616 USA
基金
中国国家自然科学基金;
关键词
Drive; Dynamic self learning; electrical parameters; interactive learning; mechanical parameters; parameter estimation; particle swarm optimization (PSO); permanent magnet synchronous machines (PMSMs); voltage-source-inverter (VSI); PARTICLE SWARM OPTIMIZATION; MAGNET SYNCHRONOUS MOTORS; TORQUE CONTROL; KALMAN-FILTER; ALGORITHM; MACHINES; NONLINEARITY; SYSTEMS; VSI;
D O I
10.1109/TPEL.2018.2801331
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
A global parameter estimation method for a permanent magnet synchronous machines (PMSM) drive system is proposed, where the electrical parameters, mechanical parameters, and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters, and VSI of PMSM drive by using dynamic self-learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multiscale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously.
引用
收藏
页码:10858 / 10871
页数:14
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