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考虑多扰动因子的含光伏电源低压台区漏电故障检测

慕静茹 喻锟 曾祥君 仝海昕 罗晨 谢志成

南方电网技术2024,Vol.18Issue(10):130-141,12.
南方电网技术2024,Vol.18Issue(10):130-141,12.DOI:10.13648/j.cnki.issn1674-0629.2024.10.013

考虑多扰动因子的含光伏电源低压台区漏电故障检测

Leakage Fault Detection in Low-Voltage Station Area with Photovoltaic Power Supply Considering Multi-Disturbance Factors

慕静茹 1喻锟 1曾祥君 1仝海昕 1罗晨 1谢志成2

作者信息

  • 1. 电网防灾减灾全国重点实验室(长沙理工大学电气与信息工程学院),长沙 410114
  • 2. 中国南方电网超高压输电公司,广州 510663
  • 折叠

摘要

Abstract

Aiming at the problem that residual current detection in low-voltage distribution areas containing photovoltaic power sup-ply is easily affected by multiple factors and difficult to achieve accurate detection of leakage faults,a leakage fault detection method for low-voltage distribution areas containing photovoltaic power supply is proposed based on the random forest algorithm,taking into account the residual current disturbance factors.By mining and analyzing residual current disturbance factors from multiple perspec-tives,the residual current deviation method is used to quantitatively analyze the impact of residual current disturbance factors on residual current.The frequency domain characteristics of leakage faults considering residual current disturbance factors are analyzed,and multidimensional fault feature vectors and feature datasets are constructed.A leakage fault detection model based on random for-est algorithm is established.Through simulation analysis and verification using a simulation model,the results show that the proposed method can detect leakage faults with high accuracy.Compared with commonly used methods,the fault detection accuracy and stabil-ity of the proposed method are higher,and the anti-interference ability is stronger.

关键词

光伏电源/低压配电系统/剩余电流扰动因子/随机森林算法/漏电故障检测

Key words

photovoltaic power supply/low-voltage distribution system/residual current disturbance factor/random forest algorithm/leakage fault detection

分类

信息技术与安全科学

引用本文复制引用

慕静茹,喻锟,曾祥君,仝海昕,罗晨,谢志成..考虑多扰动因子的含光伏电源低压台区漏电故障检测[J].南方电网技术,2024,18(10):130-141,12.

基金项目

国家自然科学基金资助项目(52177070) (52177070)

湖南省自然科学基金资助项目(2021JJ30729) (2021JJ30729)

湖南省教育厅资助项目(22A0231). Supported by the National Natural Science Foundation of China(52177070) (22A0231)

the Natural Science Foundation of Hunan Province(2021JJ30729) (2021JJ30729)

Hunan Provincial Department of Education Project(22A0231). (22A0231)

南方电网技术

OA北大核心CSTPCD

1674-0629

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