吉首大学学报(自然科学版) ›› 2024, Vol. 45 ›› Issue (4): 32-40.DOI: 10.13438/j.cnki.jdzk.2024.04.006

• 计算机与自动化 • 上一篇    下一篇

基于小波包变换和XGBoost的高压交流输电线路故障诊断

潘敏,汪旭明,孙龙飞,雷可君,杨喜   

  1. (吉首大学通信与电子工程学院,湖南 吉首 416000)
  • 出版日期:2024-07-25 发布日期:2024-07-23
  • 作者简介:潘敏(1997—),女,贵州黔东南人,吉首大学通信与电子工程学院硕士研究生,主要从事信号与信息处理技术研究
  • 基金资助:
    国家自然科学基金资助项目(61861019,62161012);湖南省教育厅科学研究项目(21A0335);湖南省环保科研项目(HBKT-2022017);国家大学生创新创业训练项目(S202010531009,202110531029);吉首大学校级科研创新项目(Jdy22021)

Fault Diagnosis of High-Voltage AC Transmission Lines Based on Wavelet Packet Transform and XGBoost

PAN Min,WANG Xuming,SUN Longfei,LEI Kejun,YANG Xi   

  1. (School of Communication and Electronic Engineering,Jishou University,Jishou 416000,Hunan China)
  • Online:2024-07-25 Published:2024-07-23

摘要:针对高压交流输电线路短路故障情况,将三相交流电流和电压信号进行小波包变换以提取故障特征,然后将提取到的特征输入XGBoost模型中进行故障诊断.结果表明,与传统故障检测方法相比,基于小波包变换和XGBoost的故障诊断方法不受故障类型影响,对10种高压交流输电线路故障的识别准确率高达99.6%,是一种准确、可靠的高压交流输电线路故障诊断方法.

关键词: 输电线路, 故障分类, 小波包变换, XGBoost

Abstract: In view of the short-circuit fault situation of high-voltage AC transmission line,the three-phase AC voltage and current signals are transformed into wavelet packets to extract the fault features,and then the extracted features are input into the XGBoost model for fault diagnosis.The experimental results show that the fault identification accuracy of the fault diagnosis method based on wavelet packet transform and XGBoost is 99.6% for the fault of high-voltage AC transmission line under different operating conditions.Compared with other machine learning models,this method has high accuracy.

Key words: transmission lines, fault classification, wavelet packet transform, XGBoost

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