Anomaly Data Analysis of Electric Railway Facility using Data Mining Method

It is necessary to develop a novel technology for predicting and diagnosing anomaly conditions of the railway power system, such as voltage drop and distortion so as to prevent a fault due to a power outage of the railway power system during railroad operation. Therefore, for intelligent management and maintenance of electric power equipment for railway system, an algorithm for an anomaly condition monitoring system based on real time TEO information was developed, and field installation for performance verification of the developed module was completed. In this paper, we describe the data mining process of a real-time monitoring system for anomaly conditions of the railway power system and the process of verifying the operation of the monitoring system.