Coral Reefs Optimization for Solving Parameter Identification in Permanent Magnet Synchronous Motor

Authors

Yawei Quan, 1. Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;;
Tian Na, 1. Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;;2. Department of Educational Technology, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, 1. Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;;
Wang Yan, 1. Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;;

Abstract

Abstract: High accuracy identification of parameters in permanent magnet synchronous motor (PMSM) is the basis of controller design. According to the drawbacks of slow speed, big error, and small number of parameters in classical particle swarm optimization (PSO) and least square method, Coral Reefs Optimization (CRO) was proposed to solve the parameter identification problem in PMSM. In order to improve the identification accuracy, the parameter setting in CRO was adjusted. The mathematical model of PMSM in coordinate system was established, CRO, PSO and RLS were applied to identify parameters in PMSM, and were verified in Matlab/Simulink for comparison. The simulation results indicate that CRO algorithm is able to improve the identification accuracy of stator resistance, d-axis inductance, q-axis inductance, rotor flux and guarantee the performance improvement in PMSM.

Recommended Citation

Quan, Yawei; Na, Tian; Ji, Zhicheng; and Yan, Wang (2020) "Coral Reefs Optimization for Solving Parameter Identification in Permanent Magnet Synchronous Motor," Journal of System Simulation: Vol. 28: Iss. 4, Article 21.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss4/21

First Page

927

Revised Date

2015-10-14

DOI Link

https://doi.org/

Last Page

933

CLC

TP391.9

Recommended Citation

Quan Yawei, Tian Na, Ji Zhicheng, Wang Yan. Coral Reefs Optimization for Solving Parameter Identification in Permanent Magnet Synchronous Motor[J]. Journal of System Simulation, 2016, 28(4): 927-933.