Research on reliability analysis of catenary model based on the fusion particle swarm least square support vector machine algorithm. (22nd March 2023)

• Record Type: Journal Article
• Authors: Zhang, Haigang; Chen, Xuan; Liu, Piao; Zhao, Decheng; Wang, Bulai; Zou, Jinbai; Yang, Minglai
• Is Part Of: International journal of information and communication technology. Volume 22:Number 3(2023)
• Page Start: 294
• Page End: 308
• Publication Date: 2023-03-22
• Subjects: Weibull distribution -- least squares support vector machine -- parameter fitting -- bootstrap; Information technology -- Periodicals; Computer science -- Periodicals; Telecommunication -- Periodicals; 004.05
• Languages: English
• ISSNs: 1466-6642

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.