考虑台风影响的电动汽车充电桩故障改进决策树预测方法

doi:10.3969/j.issn.1008-0198.2025.04.002

湖南电力 ›› 2025, Vol. 45 ›› Issue (4): 10-14.doi: 10.3969/j.issn.1008-0198.2025.04.002

• 专家专栏：电力防灾减灾 • 上一篇下一篇

考虑台风影响的电动汽车充电桩故障改进决策树预测方法

曾伟杰^1,2, 崔泽政¹, 尹华平¹, 申丽曼^1,2, 刘谋海^1,2, 郑闻麟³

1.国网湖南省电力有限公司,湖南长沙 410004;
2.智能电气量测与应用技术湖南省重点实验室,湖南长沙 410007;
3.长沙理工大学电气与信息工程学院,湖南长沙 410114

收稿日期:2025-05-16 修回日期:2025-06-12 出版日期:2025-08-25 发布日期:2025-09-05
通信作者: 刘谋海(1990),男,高级工程师,主要从事用电信息获取及互动技术研究工作。
作者简介:曾伟杰(1996),女,工程师,主要从事电能计量新技术研究工作。
基金资助:
国网湖南省电力有限公司科技项目（5216AG240007）

Improved Decision Tree Prediction Method for Electric Vehicle Charging Pile Failure Considering Typhoon Impact

ZENG Weijie^1,2, CUI Zezheng¹, YIN Huaping¹, SHEN Liman^1,2, LIU Mouhai^1,2, ZHENG Wenlin³

1. State Grid Hunan Electric Power Company Limited, Changsha 410004, China;
2. Hunan Province Key Laboratory of Intelligent Electrical Measurement and Application Technology, Changsha 410007, China;
3. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China

Received:2025-05-16 Revised:2025-06-12 Online:2025-08-25 Published:2025-09-05

摘要/Abstract

摘要： 为提升电动汽车充电桩在台风天气下的运行可靠性,提出一种融合台风影响因子的改进充电桩故障预测方法。首先对充电桩数据进行预处理操作。然后针对传统C4.5决策树算法在特征耦合效应下的预测性能不足问题,引入气象属性,并基于信息增益比优化特征选择策略,降低属性间相关性对分类准确性的干扰。算例结果分析表明,相比于传统算法,该算法的准确率提升了21%个百分点（相对提升30.1%）证实了台风影响因子的引入可优化预测模型性能。

关键词: 电动汽车充电桩, 决策树, 台风影响, 故障预测

Abstract: In order to improve the operational reliability of electric vehicle charging piles under typhoon weather, an improved charging pile fault prediction method integrating typhoon influence factors is proposed. First, the charging pile data is preprocessed. Secondly, in view of the insufficient prediction performance of the traditional C4.5 decision tree algorithm under the feature coupling effect, meteorological attributes are introduced and the feature selection strategy is optimized based on the information gain ratio to reduce the interference of inter-attribute correlation on classification accuracy. Finally, the analysis of the example results shows that the accuracy of this algorithm is improved by 21% compared with the traditional algorithm, which verifies the optimization effect of the introduction of typhoon influence factors on the performance of the prediction model.

Key words: electric vehicle charging pile, decision tree, typhoon impact, fault prediction

中图分类号:

TM925.06

曾伟杰, 崔泽政, 尹华平, 申丽曼, 刘谋海, 郑闻麟. 考虑台风影响的电动汽车充电桩故障改进决策树预测方法[J]. 湖南电力, 2025, 45(4): 10-14.

ZENG Weijie, CUI Zezheng, YIN Huaping, SHEN Liman, LIU Mouhai, ZHENG Wenlin. Improved Decision Tree Prediction Method for Electric Vehicle Charging Pile Failure Considering Typhoon Impact[J]. Hunan Electric Power, 2025, 45(4): 10-14.

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考虑台风影响的电动汽车充电桩故障改进决策树预测方法

Improved Decision Tree Prediction Method for Electric Vehicle Charging Pile Failure Considering Typhoon Impact

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