Hybrid Quantum-Behaved Particle Swarm Optimization for Parameter Identification of DFIG

Authors

Yingying Jiang, Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: In order to ensure the accuracy of the doubly-fed wind power generator (DFIG) and improve the control performance of the generator, a hybrid quantum-behaved particle swarm optimization for parameter identification was proposed. A parameter identification model of DFIG at coordinate was established. Quantum-behaved particle swarm optimization (QPSO) was improved and then mixed with simulated annealing (SA) algorithm. The proposed algorithm was compared with particle swarm optimization (PSO), QPSO and improved QPSO, which were applied to parameter identification of DFIG in Matlab/Simulink. Simulation results show that the proposed algorithm can improve the identification accuracy of the five parameters including stator resistance, stator inductance, rotor resistance, rotor inductance, and mutual inductance of stator and rotor.

Recommended Citation

Jiang, Yingying and Ji, Zhicheng (2020) "Hybrid Quantum-Behaved Particle Swarm Optimization for Parameter Identification of DFIG," Journal of System Simulation: Vol. 28: Iss. 5, Article 8.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss5/8

First Page

1054

Revised Date

2015-09-29

DOI Link

https://doi.org/

Last Page

1062

CLC

TP391.9

Recommended Citation

Jiang Yingying, Ji Zhicheng. Hybrid Quantum-Behaved Particle Swarm Optimization for Parameter Identification of DFIG[J]. Journal of System Simulation, 2016, 28(5): 1054-1062.