Framework of Big Data Monitoring System for Locomotive-network’s Electrical Interaction and Its Data Mining Application

Abstract

Electric locomotive (EL) and EMUs acquire current from traction power supply system (TPSS) through the pantograph on board. The process of acquiring the current from TPSS contain abundant electrical transient and steady state phenomena which can reflect the TPSS, EL, and EMUs’ health status. For example, the parameter identification, the fault early warning and the electrical coupling performance between TPSS and EMUs could be calculated and evaluated with data mining method from the current and voltage. This paper proposed the framework of big data monitoring system for electrical interaction of locomotive-network (LN). And based on the electrical data obtained from the monitoring system, the application cases of harmonic resonance frequency identification, harmonic impedance calculation and spectrum’s time-varying characteristic of electric locomotive in TPSS were presented in this paper.