基于UAS和BP神经网络的列车运行段停车困难点确定

Hongyu Zhou, Jiahui Feng, Jun Shen, Yang Chai, Qingyuan Wang
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引用次数: 1

摘要

动车组的列车都是电力机车。动车组在运行过程中,恶劣天气、高压电缆脱落、接触网故障、供电系统故障等多种原因都会造成供电网络停电。列车失去动力,只能被动停车等待救援,或者利用列车自身的车载储能进行自救,到达最近的车站。一旦列车停在“V”字形地形中间或困难的救援地点,使用柴油拖车救援会消耗大量能源,造成大量的碳排放。针对这一问题,在UAS仿真平台上,提出了一种基于Levenberg-Marquardt算法的BP神经网络方法来确定列车运行路段的停车难点。通过与无人机仿真数据的比较,验证了该方法的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determination of Difficult Parking Points in Train Running Section Based on UAS and BP Neural Network
The trains of EMU are all electric locomotives. During the operation of EMU, many reasons such as bad weather, high voltage cable falling off, catenary failure, power supply system failure and so on will cause power outage of power supply network. The power of the train is lost, so it has to be passively parked for rescue or use its own on-board energy storage to carry out self-rescue to the nearest station. Once the train stops in the middle of the "V" terrain or in difficult rescue locations, the use of diesel Trailer rescue will consume a lot of energy and cause a lot of carbon emissions. To solve this problem, a BP neural network method based on Levenberg-Marquardt algorithm is proposed to determine the parking difficulties in train operation section using UAS simulation platform. Compared with UAS simulation data, the reliability of this method is verified.
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