基于混合量子粒子群算法的DFIG参数辨识

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

为了确保双馈感应发电机(Doubly Fed Induction Generator,DFIG)参数的准确性,提高发电机的控制性能,提出了基于混合量子粒子群算法的参数辨识方法。在dq坐标系下建立了DFIG参数辨识的模型。对量子粒子群算法进行改进并与模拟退火算法进行混合,得到混合量子粒子群辨识算法。在Matlab/Simulink中将所提出的混合算法用于DFIG的参数辨识,并与粒子群算法、量子粒子群算法和改进量子粒子群算法进行了对比验证。仿真结果表明所提出的算法能提高定子电阻、定子电感、转子电阻、转子电感以及定转子互感五个参数的辨识精度。

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 dq 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.