摘要
为了能够准确反映超级电容器的动态特性,需要建立其等效电路模型。考虑超级电容器双电层中扩散层对器件动态特性的影响,建立了包含能够反映电荷再分配过程分支的等效电路模型。采用改进的单亲遗传算法对模型中的各个元件参数进行了辨识。为了验证所建立的超级电容器等效电路模型和元件参数辨识方法的有效性,在Simulink环境下搭建了仿真模型,并对模型进行了充电、静置以及放电实验,同时对所选用超级电容器进行了相同工况下的实测实验。实验结果显示仿真模型输出电压与实测值的最大相对误差为8.41%,出现在放电阶段。在静置阶段,仿真输出的误差要明显低于充电阶段和放电阶段。3个阶段的平均相对误差大约在3%。
In order to accurately reflect the dynamic characteristics of the supercapacitor,it is necessary to set up its equivalent circuit model.The influence of the diffusion layer in the double layer of the supercapac⁃itor on the dynamic characteristics of the device is considered,and an equivalent circuit model containing branches that reflect the charge redistribution process is set up.The modified partheno genetic algorithm is used to identify the parameters of each component in the model.For verifying the effectiveness of the estab⁃lished equivalent circuit model and the identification method of the component parameters,a simulation model is set up in Simulink environment,and the charging,standing and discharging experiments are car⁃ried out on the model.At the same time,the selected supercapacitor is tested under the same condition.The experimental results show that the maximum relative error between the output voltage of the simulated mod⁃el and the measured value is 8.41%,which occurs in the discharge stage.In the resting stage,the error of the simulated output is significantly lower than that in the charging and discharging stages.The average rel⁃ative error of the three stages is about 3%.
作者
李龙
锁军
张钰声
胡红利
闫新育
LI Long;SUO Jun;ZHANG Yusheng;HU Hongli;YAN Xinyu(State Grid Shaanxi Electric Power Co.,Ltd.,Electric Power Research Institute,Xi’an 710100,China;State Key Laboratory of Electrical Insulation and Power Equipment,Xi’an Jiaotong University,Xi’an 710049,China;Xi’an XD Power Capacitor Co.,Ltd,Xi’an 710082,China)
出处
《电力电容器与无功补偿》
2023年第3期58-64,共7页
Power Capacitor & Reactive Power Compensation
基金
西安交大电气技术研究院基金项目(XDRD16001)。
关键词
超级电容器
等效电路模型
动态特性
单亲遗传算法
supercapacitor
equivalent circuit model
dynamic characteristics
partheno genetic algorithm
