基于混合量子粒子群算法的DFIG参数辨识

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (5): 1054-1062.

基于混合量子粒子群算法的DFIG参数辨识

蒋莹莹, 纪志成

江南大学轻工过程先进控制教育部重点实验室,江苏无锡 214122

收稿日期:2015-07-10 修回日期:2015-09-29 发布日期:2020-07-03
作者简介:蒋莹莹(1992-),女,江苏南通,硕士,研究方向为电力电子及电力传动;纪志成(1959-),男,浙江杭州,教授,博士,博导,研究方向为电力电子及电力传动、绿色制造物联应用技术等。
基金资助:
国家高技术研究发展计划(2013AA040405)

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

Jiang Yingying, Ji Zhicheng

Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China

Received:2015-07-10 Revised:2015-09-29 Published:2020-07-03

摘要/Abstract

摘要： 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。

关键词: 双馈风力发电机, 参数辨识, 量子粒子群算法, 模拟退火算法, 混合算法

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

中图分类号:

TP391.9

蒋莹莹, 纪志成. 基于混合量子粒子群算法的DFIG参数辨识[J]. 系统仿真学报, 2016, 28(5): 1054-1062.

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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