接触网高压隔离开关的机械故障诊断新策略

New Strategy of Mechanical Fault Diagnosis for High Voltage Isolation Switch of OCS

随着铁路线路的快速发展,高压隔离开关成为使用量最大的高压设备。接触网隔离开关的运行可靠性对保障牵引供电的稳定运行有着重要意义。K-means作为一种十分有效的聚类分析工具,通过处理电机电流采集系统监测得到的隔离开关电机电流信号,计算获得簇中心及其位置的变化并以此作为判断隔离开关故障情况的依据,分类后的高压隔离开关机械故障为后续的监测和诊断提供了直观的表述。但是由于高压隔离开关机械故障的复杂性,以及K-means算法自身的局限性,本文提出一种改进的K-means算法,通过结合自适应权重和遗传算法(geneticalgorithm,GA),避免了K-means算法在依赖初始值和局部收敛等弊端,通过实际数据进行仿真,验证了该方法的有效性。据此可对高压隔离开关的机械故障进行分类并且作为接触网隔离开关故障分类和检测的依据。

With the rapid development of railway lines, high voltage disconnectors have become the most frequently used high voltage equipment. The reliability of OCS isolating switch is of great significance for ensuring the stable operation of traction power supply. As a very effective tool for clustering analysis, the K-means algorithm processes the isolated current motor's current signals monitored by the motor current acquisition system, obtaining the cluster centers and the change of their positions, thus determining the fault isolation switch with mechanical fault classification for further processing. Because of the complexity of mechanical failure of high voltage isolation switch, and the limitations of the K-means algorithm itself, this paper proposed an improved K-means algorithm by combining the adaptive weights and GA algorithm (genetic algorithm). K-means algorithm avoided such defects as dependence on initial value and local convergence. The validity of the methods was verified by simulating the actual data. It is found that the mechanical fault of the high-voltage isolating switch can be classified and used as the basis for fault classification and detection of OCS isolating switch.