基于BP神经网络和遗传算法的简单链型悬挂接触网结构优化

doi:10.3901/JME.2024.12.313

机械工程学报 ›› 2024, Vol. 60 ›› Issue (12): 313-320.doi: 10.3901/JME.2024.12.313

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基于BP神经网络和遗传算法的简单链型悬挂接触网结构优化

卢琪¹, 苏凯新¹, 张继旺¹, 闫涛², 杨冰¹, 张浩楠¹

1. 西南交通大学牵引动力国家重点实验室成都 610031;
2. 保德利电气设备有限责任公司宝鸡 721000

收稿日期:2023-07-17 修回日期:2023-10-25 出版日期:2024-06-20 发布日期:2024-08-23
作者简介:卢琪,男,1996年出生。主要研究方向为接触网结构服役可靠性。E-mail:luqi9668@foxmail.com;张继旺(通信作者),男,1983年出生,博士,研究员,博士研究生导师。主要研究方向为机械结构强度与可靠性。E-mail:zhangjiwang@swjtu.cn
基金资助:
国家自然科学基金资助项目(52075457)。

Structural Optimization of Simple Catenary Based on BP Neural Network and Genetic Algorithm

LU Qi¹, SU Kaixin¹, ZHANG Jiwang¹, YAN Tao², YANG Bing¹, ZHANG Haonan¹

1. State-key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031;
2. Bj-baodeli Electrical Equipment Co., Ltd., Baoji 721000

Received:2023-07-17 Revised:2023-10-25 Online:2024-06-20 Published:2024-08-23

摘要/Abstract

摘要： 受电弓和接触网之间的接触力标准差是评价弓网受流质量的重要指标。为了优化弓网受流质量，建立经EN 50318标准和已有文献仿真结果验证的弓网耦合仿真模型。采用中心复合设计方法设计输入-吊弦间距和接触线预弛度参数试验表，然后根据该试验表调整弓网耦合仿真模型，并进行仿真，提取输出-接触力标准差。采用BP神经网络(Back propagation neural network，BPNN)建立输入和输出之间的关系模型，模型预测精度达95.50%。然后，采用遗传算法(Genetic algorithm，GA)搜寻该BP神经网络模型的最小接触力标准差及所对应的最优输入参数组合。优化结果表明，BPNN-GA优化方法能显著降低弓网接触力标准差，从而提高弓网受流质量。最后，通过将优化后的接触网与原始接触网不同速度下接触力标准差进行比较，验证了优化接触网的弓网受流改善效果。经优化后的接触网可为实际接触网设计、施工、维修提供指导。

关键词: 弓网仿真, 受流质量, BP神经网络, 遗传算法

Abstract: The standard deviation of contact force(CFSD) between pantograph and catenary is an important index to evaluate the current collection quality of pantograph and catenary. In order to optimize the pantograph catenary current collection quality, a pantograph-catenary coupling simulation model verified by EN 50318 standard and the simulation results in the existing literature is established. The central composite design method is used to design the test table of input parameters (dropper spacing and contact wire pre-sag). The pantograph catenary coupling simulation model is carried out after modifying the model according to the test table. Based on this, the output parameter(CFSD) is obtained. Back propagation neural network(BPNN) is used to establish the relationship model between input and output parameter, and the prediction accuracy of the model is proved to be 95.50%. Then, genetic algorithm (GA) is used to search the minimum standard deviation of contact force and the corresponding optimal combination of input parameters of the BP neural network model. The optimization results show that BPNN-GA optimization method can significantly reduce the standard deviation of pantograph catenary contact force and improve the current collection quality of pantograph-catenary. Finally, by comparing the standard deviation of contact force between the optimized catenary and the original catenary at different speeds, the improvement effect of pantograph catenary current collection of the optimized catenary is verified. The optimized catenary can provide guidance for the design, construction and maintenance of the actual catenary.

Key words: pantograph-catenary simulation, current collection quality, BP neural network, genetic algorithm

中图分类号:

U225

卢琪, 苏凯新, 张继旺, 闫涛, 杨冰, 张浩楠. 基于BP神经网络和遗传算法的简单链型悬挂接触网结构优化[J]. 机械工程学报, 2024, 60(12): 313-320.

LU Qi, SU Kaixin, ZHANG Jiwang, YAN Tao, YANG Bing, ZHANG Haonan. Structural Optimization of Simple Catenary Based on BP Neural Network and Genetic Algorithm[J]. Journal of Mechanical Engineering, 2024, 60(12): 313-320.

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基于BP神经网络和遗传算法的简单链型悬挂接触网结构优化

Structural Optimization of Simple Catenary Based on BP Neural Network and Genetic Algorithm

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