DOI: 10.3724/SP.J.1004.2013.02121

Acta Automatica Sinica (自动化学报) 2013/39:12 PP.2121-2130

Permanent Magnet Synchronous Motor Multiple Parameter Identification and Temperature Monitoring Based on Binary-modal Adaptive Wavelet Particle

A novel parameter identi¯cation and temperature monitoring approach to permanent magnet synchronous motor (PMSM) based on binary-modal adaptive wavelet particle swarm optimization (BAWPSO) is proposed. In order to enhance the dynamic optimal performance of the swarm, the population is split into two states involving positive learning state and opposition learning state during the search process. The positive learning strategy and the opposition learning strategy are applied to different state swarms respectively to exhibit a wide range exploration. An adaptive wavelet learning mechanism is employed for accelerating the convergence accuracy of pbest. The experimental results show that the proposed method can estimate the machine dq-axis inductances, stator winding resistance and rotor flux linkage effectively, as well as track the varied parameter. Once the stator winding resistance is identified, the temperature can be calculated according to the principle that the metal resistance linearly depends on its temperature. The proposed method can realize on-line monitoring of the permanent magnet synchronous motor temperature effectively.

Key words:Particle swarm optimization (PSO), permanent magnet synchronous motor (PMSM), parameter identification, temperature monitoring, adaptive

ReleaseDate:2014-07-21 17:04:35

Funds:National Science and Technology Pillar Program (2012BAH09B02), National Natural Science Foundation of China (61174140, 61203309), Doctoral Fund of Ministry of Education of China (20110161110035), China Postdoctoral Science Foundation Funded Project (2013M540628), and National Natural Science Foundation of Hunan Province (13JJ8014, 14JJ3107)