电子测量与仪器学报

李斌,舒嘉辉.基于端口阻抗法的弓网电弧识别方法[J].电子测量与仪器学报,2026,40(3):155-163

基于端口阻抗法的弓网电弧识别方法

Pantograph-catenary arc detection method based on port impedance

DOI：

英文关键词:port impedance pantograph-catenary system dream optimization algorithm support vector machine fault detection

基金项目:国家自然科学基金(51674136)、2024年辽宁省教育厅基本科研项目（LJ232410147055）资助

作者	单位
李斌	辽宁工程技术大学电气与控制工程学院葫芦岛125105
舒嘉辉	辽宁工程技术大学电气与控制工程学院葫芦岛125105

Author	Institution
Li Bin	Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China
Shu Jiahui	Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China

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中文摘要:

针对高速列车在实际运行过程中，通过电流，电压等信号来识别电弧时对算法要求较高，算法实现难度较大以及利用高频摄像机采集电弧图像识别电弧成本较高等问题，提出了一种基于端口阻抗的方法对弓网电弧进行识别。首先，考虑实际运行条件下气流速度对电弧电压梯度的影响对电压梯度进行优化建立了更加符合列车实际运行条件下的电弧电压模型。然后，为更好地模拟实验进行，考虑在负荷波动较小情况下将弓网系统等效成含有系统阻抗和电弧阻抗的二端口网络并建立考虑气流速度影响的端口阻抗模型。其次，利用PSCAD/EMTDC仿真平台和搭建弓网电弧实验模拟平台对优化的电弧模型进行可行性分析，并且采用梦想优化算法优化的支持向量机电弧识别模型对弓网电弧进行识别，检验算法对端口阻抗的识别可行性。最后，将端口阻抗法识别电弧与电流信号，电压信号以及电弧图像3种方法从6个方面进行综合比较，验证端口阻抗法的实用性和经济性。

英文摘要:

To address the challenges of high algorithmic complexity and implementation difficulty when identifying arcing in high-speed trains using current and voltage signals, as well as the high cost of arc recognition through high-speed camera imaging, a novel method based on port impedance is proposed for pantograph-catenary arc detection. First, the influence of airflow velocity under actual operating conditions on the arc voltage gradient is considered, and the voltage gradient is optimized to establish an arc voltage model that more accurately reflects real train operation conditions. Then, to facilitate more accurate simulation, the pantograph-catenary system is equivalently modeled as a two-port network consisting of system impedance and arc impedance under conditions of minimal load fluctuation, and a port impedance model incorporating airflow effects is developed. Subsequently, the feasibility of the optimized arc model is analyzed using the PSCAD/EMTDC simulation platform and a constructed experimental pantograph-catenary arc simulation setup. A support vector machine (SVM) arc recognition model optimized by the dream optimization algorithm is employed to identify the arc, thereby validating the feasibility of port impedance-based arc recognition. Finally, the port impedance-based arc recognition method is comprehensively compared with three traditional methods—current signal, voltage signal, and arc image recognition—across six evaluation criteria to verify its practicality and cost-effectiveness.

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