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

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.

Key words: DFIG, parameter identification, QPSO, SA, hybrid algorithm

CLC Number:

TP391.9

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.

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