电气化铁路车网电气耦合异常辨识

Identification of Abnormal Phenomenon of Vehicle-network Coupling in Electrified Railway

准确迅速地辨识电气化铁路中的车网电气耦合异常是分析和避免电气事故发生的重要前提。目前,车网电气耦合异常主要包括谐波谐振、低频振荡、励磁涌流以及接地短路4种异常电气现象,现有的辨识方法中,每种算法仅对应识别一种异常电气现象类型,无法应对多种类型频发的情况,实际中异常电气现象的辨识仍以人工分析为主。为此,提出一种基于卷积神经网络(convolutional neural network,CNN)的异常电气现象辨识方法,在区别正常数据的同时监测以上4种典型异常电气现象。首先,分析异常电气现象的特征,计算得到各异常电气现象间牵引网电压电流的波形结构相似度,根据异常电气现象的波形特征及分布情况提出适用于异常电气现象辨识的CNN网络模型。模型采用2个并行的特征提取子模型同时分析电压及电流的波形特征,并在Flatten层融合所提取的特征;此外,对原始信号进行下采样处理并在网络模型中加入批量归一化层以加快网络收敛速度,避免过拟合现象。实验结果表明,所提算法对异常电气现象的识别精确率达到98.17%,对机车电气耦合状态的综合识别准确率达到95.75%,初步实现了电气化铁路异常电气现象识别的自动化。

It is an important prerequisite for analyzing and avoiding electrical accidents to accurately identify the electrical coupling anomalies of vehicle network in electrified railways.At present,the research on electric coupling anomaly of vehicle network focuses on harmonic resonance,low frequency oscillation,inrush current and short circuit.In the existing identification methods,each algorithm only identifies one type of exception,which cannot cope with the frequent occurrence of multiple exception types in practice.This paper proposed a method for identifying abnormal electrical phenomena(AEP)based on convolutional neural network(CNN),which can monitor the above four typical AEP while distinguishing normal data.Firstly,the characteristics of AEP were analyzed.The waveform structure similarity between different AEP was calculated.According to the characteristics and distribution of the waveform of abnormal phenomena,the CNN suitable for the identification of AEP was proposed.Two parallel feature extraction submodels were used to capture waveform features of voltage and current at the same time,and the extracted features were fused in the Flatten layer.In addition,the original signal was subsampled and the Batch normalization layer was added into the network model to accelerate the convergence speed of the network and avoid overfitting.The experimental results show that precision accuracy of the proposed algorithm for abnormal phenomena reaches 98.17%.And the comprehensive recognition accuracy for the locomotive electrical coupling state reaches 95.75%.The algorithm realizes the automatic identification of AEP in electrified railway.