基于数字孪生的辅助逆变器<italic style="font-style: italic">LC</italic>滤波参数辨识

向超群; 杜京润; 李佳怡; 付欣钰; 成庶

doi:10.19713/j.cnki.43-1423/u.T20251550

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基于数字孪生的辅助逆变器LC滤波参数辨识

智能制造与装备 | 更新时间：2026-07-24

- 基于数字孪生的辅助逆变器LC滤波参数辨识
- LC filter parameter identification of auxiliary inverter based on digital twin
- 铁道科学与工程学报 2026年23卷第7期页码：3473-3485
- 作者机构：
  
  中南大学交通运输工程学院，湖南长沙 410075
- 作者简介：
  
  成庶(1981—)，男，湖南长沙人，教授，博士，从事电力牵引传动及控制研究；E-mail：6409020@qq.com
- 基金信息：
  
  湖南省教育厅优秀青年基金资助项目(24B0025);湖南省区域联合基金资助项目(2025JJ70036)
- DOI：10.19713/j.cnki.43-1423/u.T20251550
  中图分类号： TM614
- 收稿：2025-10-10，
  
  网络首发：2026-07-24，
  
  纸质出版：2026-07-28
- 稿件说明：
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向超群,杜京润,李佳怡等.基于数字孪生的辅助逆变器LC滤波参数辨识[J].铁道科学与工程学报,2026,23(07):3473-3485.

XIANG Chaoqun,DU Jingrun,LI Jiayi,et al.LC filter parameter identification of auxiliary inverter based on digital twin[J].Journal of Railway Science and Engineering,2026,23(07):3473-3485.
向超群,杜京润,李佳怡等.基于数字孪生的辅助逆变器LC滤波参数辨识[J].铁道科学与工程学报,2026,23(07):3473-3485. DOI： 10.19713/j.cnki.43-1423/u.T20251550.

XIANG Chaoqun,DU Jingrun,LI Jiayi,et al.LC filter parameter identification of auxiliary inverter based on digital twin[J].Journal of Railway Science and Engineering,2026,23(07):3473-3485. DOI： 10.19713/j.cnki.43-1423/u.T20251550.

摘要

辅助逆变器为列车控制电源、空调、散热风机等辅助设备提供电能，是列车安全舒适运行的关键。逆变器滤波参数与控制参数受制造公差、运行环境、器件老化等因素影响，存在动态漂移和退化，若不能及时准确辨识，将降低输出电能品质，进而影响负载设备安全运行。为提升轨道交通辅助逆变器的健康监测能力，保障列车辅助供电系统的安全稳定运行，实现设备全生命周期的智能运维，亟需构建能够准确反映装备健康状态与关键参数变化的高精度数字孪生模型。当前主流建模与监测方法往往依赖于离线测试或局部特性分析，难以满足复杂工况下多参数同步辨识与非侵入式健康监测的需求。因此，研究高效、智能的参数在线辨识方法，对推动轨道交通装备的智能健康管理和低碳运维具有重要工程意义。本文提出了一种融合数字孪生高保真建模与鸽群优化算法(pigeon-inspired optimization

PIO)的

滤波参数协同辨识方法。首先，建立了基于四阶龙格-库塔离散化的高精度数学模型；其次，针对控制器参数与滤波参数之间的耦合关系，设计了两阶段协同优化框架和循环辨识方法，并依据逆变器输出波形特性，设计了包含相关系数和误差的适应度函数。最后，开展了涵盖多组参数退化与老化场景的系统实验。实验表明，数字孪生模型在实际物理系统下展现出优异的辨识精度与鲁棒性。

Abstract

The auxiliary inverter supplies electrical energy to auxiliary equipment of the train

such as the train control power supply

air conditioning

and cooling fans

and is crucial for the safe and comfortable operation of the train. The filtering parameters and control parameters of the inverter are affected by factors like manufacturing tolerances

operating environment

and component aging

leading to dynamic drift and degradation. If timely and accurate identification cannot be achieved

the quality of the output electrical energy will be reduced

which in turn affects the safe operation of the load equipment. To enhance the health monitoring capability of auxiliary inverters in rail transit

ensure the safe and stable operation of the train auxiliary power supply system

and realize intelligent operation and maintenance th

roughout the equipment's full life cycle

there is an urgent need to construct a high-precision digital twin model that can accurately reflect the equipment's health status and changes in key parameters. Currently

mainstream modeling and monitoring methods often rely on offline testing or local characteristic analysis. This can make it difficult to meet the requirements of multi-parameter synchronous identification and non-intrusive health monitoring under complex working conditions. Therefore

researching efficient and intelligent online parameter identification methods holds significant engineering significance for promoting intelligent health management and low-carbon operation and maintenance of rail transit equipment. This paper proposed a collaborative identification method for

filtering parameters that integrated high-fidelity digital twin modeling and the pigeon-inspired optimization (PIO) algorithm. Firstly

a high-precision mathematical model based on fourth-order runge-kutta discretization was established. Secondly

in response to the coupling relationship between controller parameters and filtering parameters

a two-stage collaborative optimization framework and a cyclic identification method were designed. Additionally

a fitness function including correlation coefficients and errors was developed based on the characteristics of the inverter's output waveform. Finally

systematic experiments covering multiple sets of parameter degradation and aging scenarios were conducted. The experimental results show that the digital twin model can exhibit excellent identification accuracy and robustness in the actual physical system.

关键词

Keywords

references

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基于数字孪生的辅助逆变器LC滤波参数辨识

LC filter parameter identification of auxiliary inverter based on digital twin

DOI：10.19713/j.cnki.43-1423/u.T20251550

摘要

Abstract

关键词

Keywords

references