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 locomotivesbased on multi-frequency-band characteristics of current signals

作　　者：罗茵蓓 葛婷 孙泽勇 LUO Yinbei;GE Ting;SUN Zeyong(Locomotive Department,China Railway Guangzhou Group Co.,Ltd.,Guangzhou,Guangdong 510088,China;Wuhan Power Supply Company,State Grid Hubei Electric Power Co.,Ltd.,Wuhan,Hubei 430050,China;Hunan CRRC Times Signal and Communication Co.,Ltd.,Changsha,Hunan 410100,China)

机构地区：[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

出　　处：《机车电传动》2024年第4期181-189,共9页Electric Drive for Locomotives

基　　金：中国铁路广州局集团有限公司科研项目(2018K068-J)。

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。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 

分 类 号：U225[交通运输工程—道路与铁道工程] U264.34[机械工程—车辆工程]