Permanent Magnet Synchronous Motor Parameter Identification Based on Improved Teaching-Learning-Based Optimization

Authors

Li Jie, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Wang Yan, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: High accuracy identification of parameters in permanent magnet synchronous motor (PMSM) is the basis of controller design. In order to overcome the shortages of traditional identification methods such as slow speed and low identification accuracy, an improved teaching-learning-based optimization algorithm (ITLBO) was proposed to identify the permanent magnet synchronous motor parameters. In the teaching phrase, tutorial teaching mechanism was introduced to strengthen teacher's capacity and improved the convergence rate of algorithm, in the learning phrase, the course stepwise learning was used to improve learners' learning efficiency. Besides, opposition-based-learning was introduced for small probability mutation, which enhanced the possibility out of local optima. The simulation result shows that the proposed algorithm has better convergence and reliability in simultaneous identification of the stator resistance, d-axis and q-axis inductance and the rotor linkage compared with teaching-learning-based optimization and particle swarm optimization.

Recommended Citation

Jie, Li; Yan, Wang; and Ji, Zhicheng (2020) "Permanent Magnet Synchronous Motor Parameter Identification Based on Improved Teaching-Learning-Based Optimization," Journal of System Simulation: Vol. 29: Iss. 2, Article 22.
DOI: 10.16182/j.issn1004731x.joss.201702022
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss2/22

First Page

393

Revised Date

2016-08-22

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201702022

Last Page

401

CLC

TP391.9

Recommended Citation

Li Jie, Wang Yan, Ji Zhicheng. Permanent Magnet Synchronous Motor Parameter Identification Based on Improved Teaching-Learning-Based Optimization[J]. Journal of System Simulation, 2017, 29(2): 393-401.

DOI

10.16182/j.issn1004731x.joss.201702022