Permanent Magnet Synchronous Motor Multi-parameter Identification Based on Improved Salp Swarm Algorithm

Authors

Mengqiu Wang, 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: Since the multi-parameter identification of permanent magnet synchronous motor (PMSM) has slow speed and low accuracy, a parameter identification method based on the improved salp swarm algorithm was proposed in this paper. The algorithm firstly adopted the self-adaptive evaluation-move strategy and neighborhood optimum guide strategy based Von Neumann topology to update the position of followers twice, which strengthened information cooperation in the population and accelerated the convergence rate of parameter identification. Secondly, the algorithm used the opposition-based learning strategy to perturb the population position with a certain mutation probability, that avoided local optimum and misconvergence of the parameters. The simulation results show that this algorithm can identify PMSM parameter quickly and accurately.

Recommended Citation

Wang, Mengqiu; Yan, Wang; and Ji, Zhicheng (2019) "Permanent Magnet Synchronous Motor Multi-parameter Identification Based on Improved Salp Swarm Algorithm," Journal of System Simulation: Vol. 30: Iss. 11, Article 29.
DOI: 10.16182/j.issn1004731x.joss.201811029
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss11/29

First Page

4284

Revised Date

2018-07-01

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201811029

Last Page

4292

CLC

TP391.9

Recommended Citation

Wang Mengqiu, Wang Yan, Ji Zhicheng. Permanent Magnet Synchronous Motor Multi-parameter Identification Based on Improved Salp Swarm Algorithm[J]. Journal of System Simulation, 2018, 30(11): 4284-4292.

DOI

10.16182/j.issn1004731x.joss.201811029