摘要
为了准确地辨识风力发电机变桨系统后备电源中超级电容模组等效模型的参数,解决由于“数据饱和”现象所产生增益下降过快的缺点,建立超级电容模组三分支等效电路模型,提出一种带可变遗忘因子的递推最小二乘法(RLS)的超级电容模组等效电路模型参数辨识方法,然后建立超级电容模组多方法参数辨识的Simulink仿真模型,并进行仿真与分析。结果表明:该方法充电后静态阶段的综合误差为0.19%,比电路分析法的综合误差降低了6.92%,比分段优化法的综合误差降低了0.09%。整个充放电过程的综合误差为1.22%,比电路分析法降低了9.5%,比分段优化法降低了1.6%。带可变遗忘因子的RLS法比电路分析法和分段优化法拥有更高的辨识精度。
In order to accurately identify the parameters of the equivalent model of supercapacitor cell module in the backup power supply of the pitch system of megawatt wind turbine and to solve the problem that the gain decreases too fast due to the data saturation phenomenon,the three-branch equivalent circuit model for the supercapacitor cell module was established,and a parameter identification method of the equivalent circuit model of supercapacitor cell module based on variable forgetting factor recursive least squares(RLS)was proposed in this paper.Then,the Simulink simulation model was also established for the multi-method parameter identification of supercapacitor cell module,and the simulation and analysis were performed.The comprehensive error in the static self-discharge phase of this new method is 0.19%,which is 6.92%and 0.09%lower than circuit analysis method and segmentation optimization method,respectively.Its comprehensive error in the whole process is 1.22%,which is reduced by 9.5%and 1.6%compared with circuit analysis method and segmentation optimization method,respectively.The results show that the new method has higher identification accuracy than circuit analysis method and segmentation optimization method.
作者
谢文超
赵延明
方紫微
刘树立
Xie Wenchao;Zhao Yanming;Fang Ziwei;Liu Shuli(School of Information and Electrical Engineering Hunan University of Science and Technology,Xiangtan 411201 China;School of Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data Hunan University of Science and Technology,Xiangtan 411201 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2021年第5期996-1005,共10页
Transactions of China Electrotechnical Society
基金
国家重点研发计划(2016YFF0203400)
湖南省研究生创新项目(CX2018B670)资助。
关键词
超级电容模组
等效模型
参数辨识
可变遗忘因子
Supercapacitor cell module
equivalent circuit model
parameter identification
variable forgetting factor
