Application of QGA-SVM to Load Optimization for Pantograph-catenary System Sliding Electrical Contact

REN Zhiling; GUO Chao; XU Lixia

doi:10.13296/j.1001-1609.hva.2015.04.025

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Application of QGA-SVM to Load Optimization for Pantograph-catenary System Sliding Electrical Contact

更新时间：2025-04-26

- Application of QGA-SVM to Load Optimization for Pantograph-catenary System Sliding Electrical Contact
- High VoltageApparatus Vol. 51, Issue 4, Pages: 133-138(2015)
- 作者机构：
  
  1. 辽宁工程技术大学电气与控制工程学院
  2. 大连宝原核设备有限公司质保部
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2015.04.025
  CLC： U264.34;U225
- Published：2015
- 稿件说明：
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[1]任志玲,郭超,徐丽霞.QGA-SVM在弓网滑动电接触下的最优载荷研究[J].高压电器,2015,51(04):133-138.

REN Zhiling, GUO Chao, XU Lixia. Application of QGA-SVM to Load Optimization for Pantograph-catenary System Sliding Electrical Contact[J]. High voltageapparatus, 2015, 51(4): 133-138.
[1]任志玲,郭超,徐丽霞.QGA-SVM在弓网滑动电接触下的最优载荷研究[J].高压电器,2015,51(04):133-138. DOI： 10.13296/j.1001-1609.hva.2015.04.025.

REN Zhiling, GUO Chao, XU Lixia. Application of QGA-SVM to Load Optimization for Pantograph-catenary System Sliding Electrical Contact[J]. High voltageapparatus, 2015, 51(4): 133-138. DOI： 10.13296/j.1001-1609.hva.2015.04.025.

摘要

弓网滑动电接触过程中

电、力、速多个物理域的复杂耦合影响列车的高速、重载、稳定运行。为了提高载荷最优控制

使摩擦副摩擦磨损与受流稳定性达到相对最佳

利用量子遗传算法优化支持向量机的相关参数

建立了受电弓滑板磨损率的预测模型。经过MATLAB仿真结果表明

量子遗传算法比遗传算法有更好的优化性能

建立的模型能够稳定预测滑板磨损率

对选取最优载荷、研究载流摩擦副材料具有重要意义。

Abstract

Complex coupling of electrical

power

speed and other physical domains in pantograph-catenary system sliding electrical contact process impacts the stable operation of a train with high-speed and heavy load. In order to improve the optimal control of load and optimize the friction and wear of the friction pair and the current collection stability

the quantum genetic algorithm（QGA） is adopted to optimize the parameters of support vector machine（SVM）

then a wear rate prediction model of pantograph slider is established. Simulation on MATLAB shows that quantum genetic algorithm has better optimization performance than genetic algorithm

and the proposed model can steadily predict the wear rate of the slider for selecting the optimal load and developing the material of current-carrying friction pair.

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