基于DSLPSO算法的超级电容参数辨识  

Parameter identification of super capacitor based on DSLPSO algorithm

作　　者：刘继超 王维庆 王海云 Liu Jichao;Wang Weiqing;Wang Haiyun(Renewable Energy Power Generation and Grid Technology,Engineering Research Center of Ministry of Education,Xinjiang University,Urumqi 830047,China)

机构地区：[1]新疆大学可再生能源发电与并网技术教育部工程研究中心,乌鲁木齐830047

出　　处：《电测与仪表》2021年第6期33-38,共6页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(51667020);新疆自治区万人计划后备人选项目—"天山雪松"人才培养计划(2017XS02);新疆自治区重点实验室开放课题(2018D04005);教育部创新团队项目(IRT_16R63)。

摘　　要：针对超级电容模型多参数辨识问题以及传统辨识算法收敛精度差,收敛速度慢问题,提出基于动态自学习粒子群算法的超级电容参数辨识方法。根据超级电容等效电路模型,采用双线性变换进行离散化获得辨识模型,使用动态自学习粒子群算法辨识各分支的参数。仿真结果分析表明,与基本粒子群、自适应惯性权重粒子群对比分析,基于动态自学习粒子群算法的超级电容参数辨识方法收敛速度快、收敛精度高、全局寻优能力强,可以更准确地反映出超级电容的动态特性。In order to solve the problem of multi-parameter identification of super capacitor model and the problems of poor convergence accuracy and slow convergence speed of traditional identification algorithm,a parameter identification method based on dynamic self-learning particle swarm optimization(PSO)is proposed in this paper.According to the three-branch equivalent circuit model of super capacitor,the identification model is discretized by bilinear transformation,and the parameters of each branch are identified by dynamic self-learning particle swarm optimization.The simulation results show that,compared with the basic particle swarm and the adaptive inertial weighted particle swarm,the parameter identification method based on dynamic self-learning particle swarm algorithm has fast convergence speed,high convergence accuracy and strong global optimization ability,which can more accurately reflect the dynamic characteristics of super capacitors.

关 键 词：超级电容 三分之等效电路模型 双线性变换 动态自学习粒子群 

分 类 号：TM93[电气工程—电力电子与电力传动]