Establishment of offline disturbance model of pantograph and catenary based on machine learning algorithm

Yi-fei Dai; Ying-hong Wen; Jin-bao Zhang

doi:10.1117/12.2684673

21 July 2023 Establishment of offline disturbance model of pantograph and catenary based on machine learning algorithm

Yi-fei Dai, Ying-hong Wen, Jin-bao Zhang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 1271717 (2023) https://doi.org/10.1117/12.2684673
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Due to the complexity of the electromagnetic environment of high-speed EMU, electromagnetic interference will be affected by many factors and malfunction, among which disturbance source in high-speed railway is one of the most important factors. As an important disturbance source in high-speed railway, the off-line discharge disturbance of pantograph and catenary is studied based on the generation mechanism of the off-line discharge of pantograph and catenary, and the radiation characteristics of the off-line discharge disturbance source of high-speed railway pantograph and catenary are studied by means of deep learning platform prediction and time-frequency analysis. Based on the generation mechanism of pantograph-catenary offline discharge, the time-frequency characteristics of pantograph-catenary offline discharge under normal train operation and iced catenary are analyzed through the equivalent circuit model of pantograph-catenary offline discharge mechanism. Based on the time-frequency signals of the whole process for the pantograph/catenary offline discharge during the experiment, the deep learning model and machine learning for the pantograph/catenary offline discharge are established, and the signal characteristics in the two-dimensional spectrum of the discharge frequency and time are mainly studied.

Citation Download Citation

Yi-fei Dai, Ying-hong Wen, and Jin-bao Zhang "Establishment of offline disturbance model of pantograph and catenary based on machine learning algorithm", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 1271717 (21 July 2023); https://doi.org/10.1117/12.2684673

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KEYWORDS

Artificial neural networks

Feature extraction

Machine learning

Signal processing

Electromagnetism

Wavelets

Time-frequency analysis

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