基于多智能体近端策略优化算法的电力机车受电弓随网压力研究

doi:10.16628/j.cnki.2095-8188.2026.04.004

电器与能效管理技术 ›› 2026, Vol. 0 ›› Issue (4): 32-40.doi: 10.16628/j.cnki.2095-8188.2026.04.004

基于多智能体近端策略优化算法的电力机车受电弓随网压力研究

马振豪, 郭凤仪, 韩聪信

温州大学电气与电子工程学院, 浙江温州 325035

收稿日期:2025-12-21 出版日期:2026-04-30 发布日期:2026-05-18
作者简介:马振豪(1999—),男,硕士研究生,研究方向为电器基础理论及应用。|郭凤仪(1964—),男,教授,博士生导师,研究方向为电器基础理论及应用。|韩聪信(1994—),男,博士研究生,研究方向为电器基础理论及应用。
基金资助:
国家自然科学基金(52477153)

Investigation of Pantograph Wire-Following Contact Force in Electric Locomotives Based on Multi-Agent Proximal Policy Optimization

MA Zhenhao, GUO Fengyi, HAN Congxin

College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China

Received:2025-12-21 Online:2026-04-30 Published:2026-05-18

摘要/Abstract

摘要：

弓网系统作为电力机车电能传输的关键环节,其配合协同效果直接决定了受流质量与运行的可靠性。在高速运行条件下,受电弓沿接触线的滑动易引发弓网系统的耦合振动,进而影响接触稳定性,降低受流效率。对受电弓进行随网压力(WFCF)控制是解决该问题的有效途径。提出一种基于多智能体近端策略优化(MAPPO)算法,并结合长短期记忆(LSTM)神经网络的控制方法,用于实现对受电弓随网压力的动态控制。在训练过程中,所提出的算法通过多个智能体分别学习不同速度条件下的受电弓随网压力控制策略,并采用集中式训练、分布式执行(CTDE)的框架,实现对随网压力的精确调控。实验结果表明,该算法在降低随网压力波动和提升随网性能(CWFP)方面均表现出显著的改进效果。

关键词: 随网压力, 多智能体近端策略优化, 受电弓, 随网性能

Abstract:

The pantograph-catenary system, as the key interface for traction power transmission in electric locomotives, determines current-collection quality and operational reliability through its coordination performance. At high speeds, the sliding of pantograph along the contact wire excites coupled vibrations in the pantograph-catenary system, undermining contact stability and reducing current-collection efficiency. Regulating the wire-following contact force(WFCF) is an effective remedy. A multi-agent proximal policy optimization(MAPPO) method augmented with a long short-term memory(LSTM) network is proposed to achieve dynamic WFCF control. Within a centralized-training, decentralized-execution(CTDE) framework, multiple agents are trained to learn speed-specific control policies, enabling precise force regulation across operating conditions. Experimental results show that the proposed approach substantially suppresses WFCF fluctuations and enhances contact wire-following performance(CWFP).

Key words: wire-following contact force, MAPPO, pantograph, contact wire-following performance

中图分类号:

TM501

马振豪, 郭凤仪, 韩聪信. 基于多智能体近端策略优化算法的电力机车受电弓随网压力研究[J]. 电器与能效管理技术, 2026, 0(4): 32-40.

MA Zhenhao, GUO Fengyi, HAN Congxin. Investigation of Pantograph Wire-Following Contact Force in Electric Locomotives Based on Multi-Agent Proximal Policy Optimization[J]. LOW VOLTAGE APPARATUS, 2026, 0(4): 32-40.

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参考文献 17

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算法	参数	数值
LSTM参数	隐藏层大小	256
LSTM参数	层数	2
PPO参数	学习率	0.000 4
	折扣因数γ	0.98
	GAE_lambda	0.95
	策略截断范围	0.3
	熵正则系数	0.02
	值函数损失系数	0.18
	批量更新样本数	512
	每回合步数	4 096
	总训练回合	10 000

参数	DSA200
弓头质量/kg	6.52
上部框架质量/kg	4.3
下部框架质量/kg	10.72
弓头刚度/(N·m^-1)	6 350
上部框架刚度/(N·m^-1)	10 900
下部框架刚度/(N·m^-1)	80
弓头阻尼/(N·s·m^-1)	0
上部框架阻尼/(N·s·m^-1)	0
下部框架阻尼/(N·s·m^-1)	70
设计时速/(km·h^-1)	200
静态接触压力/N	70±10

速度/(km·h^-1)	平均绝对误差		标准差
	未控制	MAPPO	未控制	MAPPO
	30	1.86	1.54	2.21	1.77
50	2.03	1.65	2.38	1.86
70	2.22	1.72	2.57	2.12

基于多智能体近端策略优化算法的电力机车受电弓随网压力研究

Investigation of Pantograph Wire-Following Contact Force in Electric Locomotives Based on Multi-Agent Proximal Policy Optimization

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