Pre-charging model based capacitance estimation method for DC-link capacitors with low sampling frequency

被引：2
|
作者：
Wu X. [1 ]
Tian R. [2 ]
Li K. [3 ]
Yu T. [1 ]
Cheng S. [1 ]
Chen C. [1 ]
机构：
[1] School of Traffic & Transportation Engineering, Central South University, Changsha
[2] State Grid Hunan Extra High Voltage Substation Company, Changsha
[3] Shenzhen Metro Operation Group Co. Ltd., Shenzhen
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2023年 / 54卷 / 08期
基金：
中国国家自然科学基金;
关键词：
capacitance estimation; DC-link capacitor; noise; pre-charging; RELS algorithm;
D O I：
10.11817/j.issn.1672-7207.2023.08.033
中图分类号：
O441.1 [电学]; TM12 [];
学科分类号：
摘要：
DC-link capacitor is a significant part of traction converter in railway applications. It mainly filters the output voltage of rectifier and ensures the efficient operation of inverter. The capacitance is an important indicator for the residual service life of the capacitors, and it tends to degrade fast in harsh working environment. Accurate capacitance estimation is necessary for the condition monitoring of DC-link capacitors. However, the noise fluctuation of voltage sensor signals is nearly the same as that of the ripple components in railway applications, which leads to the increase of errors in existing methods. Therefore, a pre-charging model based capacitance estimation method for DC-link capacitors was proposed. The pre-charging process was analyzed and a discrete model was built to reduce the impact of noise. Based on the model, RELS algorithm with noise evaluation was utilized to further lessen the noise effect and obtain accurate capacitance estimation results. The accuracy and robustness of the proposed method were verified on a dSPACE platform. The results show that the accurate calcutation can be achieved with a low sampling frequency. The comparison results with existing estimation methods show the superiority of the proposed method. © 2023 Central South University of Technology. All rights reserved.
引用
收藏
页码：3325 / 3334
页数：9
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