电气化铁路牵引负荷预测研究

刘福; 廖启术

doi:10.13890/j.issn.1000-128X.2023.02.017

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电气化铁路牵引负荷预测研究

铁道电气化 | 更新时间：2024-08-02

- 电气化铁路牵引负荷预测研究
- Research on traction load forecasting of electrified railway
- 机车电传动 2023年第2期页码：142-150
- 作者机构：
  
  1.国家能源投资集团朔黄铁路发展有限责任公司，河北肃宁 062350
  2.株洲中车时代电气股份有限公司，湖南株洲;412001
- 作者简介：
  
  廖启术（1998—），男，硕士，主要从事电力机车PHM方面的研究；E-mail: lqslqsuestc@163.com
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2023.02.017
  中图分类号： U223
- 纸质出版日期：2023-03-10，
  
  收稿日期：2022-11-07，
  
  修回日期：2023-03-01，
- 稿件说明：
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刘福, 廖启术. 电气化铁路牵引负荷预测研究[J]. 机车电传动, 2023(2): 142-150.

LIU Fu, LIAO Qishu. Research on traction load forecasting of electrified railway[J]. Electric Drive for Locomotives,2023(2): 142-150.
刘福, 廖启术. 电气化铁路牵引负荷预测研究[J]. 机车电传动, 2023(2): 142-150. DOI： 10.13890/j.issn.1000-128X.2023.02.017.

LIU Fu, LIAO Qishu. Research on traction load forecasting of electrified railway[J]. Electric Drive for Locomotives,2023(2): 142-150. DOI： 10.13890/j.issn.1000-128X.2023.02.017.

摘要

为保证牵引供电系统电能质量并减少牵引变电所购电成本，文章提出了一种基于稀疏高斯过程（sparse Gaussian process

SGP）的牵引负荷概率预测模型。该模型首先构建以牵引负荷历史特征和时间信息为基础的输入特征向量；然后建立输入特征到牵引负荷之间的映射关系，并用SGP来拟合该映射关系；最后使用滚动预测的形式来实现对牵引负荷的预测。在朔黄铁路某牵引变电所实际运行数据上进行的对比试验验证了所提方法的优越性，其中，点预测可以得到误差在7%左右的预测值，概率预测可以得到不同置信度下可靠的预测区间。

Abstract

In order to ensure the power quality of traction power supply system and reduce the costs for purchasing electricity by traction substation

a probabilistic forecasting model of traction load based on sparse Gaussian process (SGP) is proposed. Firstly

the model constructed the input feature vector based on the historical characteristics of traction load and time information. Then

the mapping relationship between input characteristics and traction load was established

and SGP was used to fit the mapping relationship. Finally

a rolling forecasting methodology was used to predict the traction load. The comparative experiments carried out on the real operation data of a traction substation on Shuohuang railway verify the advantage of the proposed method

where the point forecasting can obtain the prediction value with an error of about 7%

and the probabilistic forecasting can obtain the reliable prediction interval under different confidence coefficients.

关键词

牵引负荷负荷预测高斯过程点预测概率预测电力机车

Keywords

ttraction loadload forecastingGaussian processpoint forecastingprobabilistic forecastingelectric locomotive

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