基于电流信号多频带特征的列车弓网燃弧检测方法

罗茵蓓; 葛婷; 孙泽勇

doi:10.13890/j.issn.1000-128X.2024.04.022

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基于电流信号多频带特征的列车弓网燃弧检测方法

铁道电气化 | 更新时间：2024-09-12

- 基于电流信号多频带特征的列车弓网燃弧检测方法
- Detection method on pantograph-catenary arcing of electric locomotives based on multi-frequency-band characteristics of current signals
- 机车电传动 2024年第4期页码：181-189
- 作者机构：
  
  1.中国铁路广州局集团有限公司机务部，广东广州 510088
  2.国网湖北省电力有限公司武汉供电公司，湖北武汉 430050
  3.湖南中车时代通信信号有限公司，湖南长沙 410100
- 作者简介：
  
  罗茵蓓，男，高级工程师，主要从事机车运行管理方面的研究；E-mail: zzlyb1520@163.com
- 基金信息：
  
  中国铁路广州局集团有限公司科研项目(2018K068-J)
- DOI：10.13890/j.issn.1000-128X.2024.04.022
  中图分类号： U225;U264.3⁺4
- 纸质出版日期：2024-07-10，
  
  收稿日期：2023-07-25，
  
  修回日期：2024-06-20，
- 稿件说明：
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罗茵蓓, 葛婷, 孙泽勇. 基于电流信号多频带特征的列车弓网燃弧检测方法[J]. 机车电传动, 2024(4): 181-189.

LUO Yinbei, GE Ting, SUN Zeyong. Detection method on pantograph-catenary arcing of electric locomotives based on multi-frequency-band characteristics of current signals[J]. Electric drive for locomotives,2024(4): 181-189.
罗茵蓓, 葛婷, 孙泽勇. 基于电流信号多频带特征的列车弓网燃弧检测方法[J]. 机车电传动, 2024(4): 181-189. DOI：10.13890/j.issn.1000-128X.2024.04.022.

LUO Yinbei, GE Ting, SUN Zeyong. Detection method on pantograph-catenary arcing of electric locomotives based on multi-frequency-band characteristics of current signals[J]. Electric drive for locomotives,2024(4): 181-189. DOI：10.13890/j.issn.1000-128X.2024.04.022.

摘要

列车弓网电弧检测是铁路安全运维的重要方面。现有检测方案多以光学仪器拍摄弓网图像，并分析所拍图像是否含有电弧的光谱，以此判断列车弓网是否发生燃弧，然而，该方法受限于列车外部环境的能见度，具有不易维护的特点。因此文章提出了一种基于电流信号多频带特征的识别方法。首先，从电弧的时域和频域物理特征出发，通过理论推导、仿真和现场实测波形，论证弓网电弧的特征分量包含了瞬间电离导致的极低频分量、LC振荡引起的谐波分量和高频分量；然后以此为依据，设计测量方案和数据预处理算法，结合历史数据形成特征集，并建立以特征向量为输入、以检测结果为输出的随机森林模型；最后将3C设备提供的燃弧标签和特征集代入训练，获得能够实时检测的分类器。通过现场随车试验论证其可行性，其中检测准确率达100%，回报率约98. 9%。文章提及的方法具有一定扩展能力，可根据用户提供不同事件标签进行训练，扩展模式识别的用途。

Abstract

Detecting pantograph-catenary arcing on trains is crucial for ensuring safety in railway operation and maintenance. Most existing detection methods rely on optical instruments to capture pantograph-catenary images

followed by the analysis of these images to identify arc spectra as evidence of arcing occurrences. However

these methods are limited by inadequate visibility in the external environments of trains

and maintenance access can be challenging. To address these issues

this paper proposed a detection method based on multi-frequency-band characteristics of current signals. First

based on the time-domain and frequency-domain characteristics of arcs

leveraging theoretically derived

simulated and measured waveforms

the following characteristic components of pantograph-catenary arcs were demonstrated: extremely low-frequency components caused by instantaneous ionization

harmonic components resulting from LC oscillation

and high-frequency components. These characteristic components were then utilized to devise a measurement scheme and data preprocessing algorithm

and historical data were incorporated

leading to the establishment of feature sets. Additionally

a random forest model was established

with feature vectors as inputs and detection results as outputs. The arcing labels and feature sets provided by 3C equipment were incorporated for training

to develop a classifier enabling real-time arcing detection. Its efficacy was demonstrated through on-board experiments

showcasing a detection precision up to 100% and a recall approximating 98.9%. In addition

the proposed method supports certain extensions for more application scenarios

after training using different event labels provided by users.

关键词

电力机车电弧暂态特征传感器应用傅里叶变换随机森林

Keywords

electrical locomotivearc transient characteristicstransducer applicationFourier transformrandom forest

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