Cluster Analysis Based Arc Detection in Pantograph-Catenary System

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.

Application of GWO-SVM Algorithm in Arc Detection of Pantograph

“…In this paper, the five eigenvalues of the mean, variance, standard deviation, first-order difference mean, and second-order difference mean are extracted from the current receiving waveform for each 100 sampling points to form a vector group matrix in the form of a vector. The feature vector group can be written as (6)…”

“…Yu analyzed the arc spectral distribution, and proposed a method for arc detection in urban rail transit based on the photoelectric conversion mechanism, the photoelectric conversion module converts the arc signal into the current signal, and then carries out comparative screening to detect the arc of the pantograph [5]. Huang proposed a method to segment pantograph video into continuous frame-by-frame images, analyze time series in the same environment through clustering analysis (CAT), find outliers and identify emergencies [6]. However, the abnormal image features are not obvious, the training samples are limited, and the image has to be checked in the scheduling data center.…”

The Electrical Behaviour of Railway Pantograph Arcs

“…Detection, finally, is an important part for what entails verification of current collection quality and recognition of defects in the pantograph and catenary: it may involve phenomena that are not strictly electric (that form the core of this review), and, indeed, methods based on waveform distortion [37] and electromagnetic emissions [38,39] are sided by very promising techniques based on various forms of luminous emissions [40,41], including image scanning, pattern recognition and various artificial intelligence algorithms [42][43][44], that are discussed in Section 6.…”

Pantograph Arc Location Estimation Using Resonant Frequencies in DC Railway Power Systems

“…By means of a camera installed on the roof of a train, a recorded pantograph video can be analysed by signal and image processing methods to detect the pantograph arc occurrence [16]- [19]. For example, continuous frame-by-frame images decomposed from a pantograph video could be converted into binary images from which the ratio of white pixels to black pixels is evaluated as an arc parameter for the frame [17]. In addition, some arc detection techniques install sensors to monitor physical quantities emitted from pantograph arcing such as (i) significant temperature changes at the contact point recorded by thermal cameras combined with image processing algorithms [20]; (ii) ultraviolet emissions detected by phototubes [7], [21] or predefined light wavelengths for particular metal materials monitored by light detectors [22]; and (iii) the electromagnetic field radiation captured by an antenna [23], [24] and processed to determine the possible characteristic radiated frequencies [23].…”