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Journal of Advanced Transportation
Volume 2018, Article ID 1329265, 12 pages
https://doi.org/10.1155/2018/1329265
Research Article

Cluster Analysis Based Arc Detection in Pantograph-Catenary System

1Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
2College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China

Correspondence should be addressed to Shize Huang; nc.ude.ijgnot@zsh

Received 24 November 2017; Revised 3 March 2018; Accepted 28 March 2018; Published 7 May 2018

Academic Editor: Taku Fujiyama

Copyright © 2018 Shize Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The pantograph-catenary system, which ensures the transmission of electrical energy, is a critical component of a high-speed electric multiple unit (EMU) train. The pantograph-catenary arc directly affects the power supply quality. The Chinese Railway High-speed (CRH) is equipped with a 6C system to obtain pantograph videos. However, it is difficult to automatically identify the arc image information from the vast amount of videos. This paper proposes an effective approach with which pantograph video can be separated into continuous frame-by-frame images. Because of the interference from the complex operating environment, it is unreasonable to directly use the arc parameters to detect the arc. An environmental segmentation algorithm is developed to eliminate the interference. Time series in the same environment is analyzed via cluster analysis technique (CAT) to find the abnormal points and simplified arc model to find arc events accurately. The proposed approach is tested with real pantograph video and performs well.