Title: Research on reliability analysis of catenary model based on the fusion particle swarm least square support vector machine algorithm

Authors: Haigang Zhang; Xuan Chen; Piao Liu; Decheng Zhao; Bulai Wang; Jinbai Zou; Minglai Yang

Addresses: School of Rail Transit of Shanghai Institute of Technology, Shanghai, 201418, China ' School of Rail Transit of Shanghai Institute of Technology, Shanghai, 201418, China ' School of Rail Transit of Shanghai Institute of Technology, Shanghai, 201418, China ' School of Rail Transit of Shanghai Institute of Technology, Shanghai, 201418, China ' School of Rail Transit of Shanghai Institute of Technology, Shanghai, 201418, China ' School of Rail Transit of Shanghai Institute of Technology, Shanghai, 201418, China ' School of Rail Transit of Shanghai Institute of Technology, Shanghai, 201418, China

Abstract: The reliability of contact network is always an important part of the reliability analysis of traction power supply system. In this paper, combined with the failure rate data of the main parts of the contact network, the small sample data is expanded by bootstrap non-parametric regeneration sampling method using the fused particle swarm least squares support vector machine (PSOLSSVM) algorithm to provide training set data for particle swarm optimisation. Mann, Schuer and Fertig fit test and the two-parameter Weibull distribution of the model based on MATLAB were used. The key components such as load cable and insulator were selected, and the data was combined to establish a model to characterise the overall reliability of the contact network. Based on the established model, the relevant parameters of each main part are estimated separately, which accords with the actual situation.

Keywords: Weibull distribution; least squares support vector machine; parameter fitting; bootstrap.

DOI: 10.1504/IJICT.2023.129869

International Journal of Information and Communication Technology, 2023 Vol.22 No.3, pp.294 - 308

Received: 16 Nov 2020
Accepted: 06 Apr 2021
Published online: 03 Apr 2023 *

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