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基于改进局部离群因子的低压用户用电隐患检测方法
作者:
作者单位:

1.浙江大学电气工程学院,浙江省杭州市 310027;2.国网浙江余姚市供电有限公司,浙江省宁波市 315400

摘要:

基于用电信息采集系统的量测数据,提出了一种基于改进局部离群因子算法的用户用电隐患检测方法。首先,提出基于信息熵的电压信息重构方法,扩大电压数据差异性。其次,提出基于K-奇异值分解的电压数据稀疏编码方法,解决台区用户原始负荷特征维度过高带来的冗余性问题。然后,提出基于改进局部离群因子算法的用户用电隐患检测方法,通过多局部离群因子模型组合优化,提高低压用户用电隐患检测泛化能力与准确率。最后,以中国浙江省某台区为例进行验证,算例分析的结果表明所提算法相对于传统局部离群因子算法具有更高的隐患检测准确率。

关键词:

基金项目:

国家自然科学基金资助项目(52077195);国家重点研发计划资助项目(2016YFB0901100)。

通信作者:

作者简介:

林之岸(1997—),男,硕士研究生,主要研究方向:电力系统数据分析。E-mail:linzhian@zju.edu.cn
刘晟源(1995—),男,博士研究生,主要研究方向:电力系统态势感知、数据分析技术和数学优化方法在电力系统中的应用。E-mail:eelsy@zju.edu.cn
金伟超(1996—),男,硕士研究生,主要研究方向:电力应急与电力大数据。E-mail:eejwc@zju.edu.cn
林振智(1979—),男,通信作者,博士,教授,博士生导师,主要研究方向:电力大数据挖掘、电力系统恢复。E-mail:linzhenzhi@zju.edu.cn


Detection Method of Hidden Danger for Power Utilization of Low-voltage Users Based on Local Outlier Factor-Boosting
Author:
Affiliation:

1.School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;2.State Grid Zhejiang Yuyao Power Supply Co., Ltd., Ningbo 315400, China

Abstract:

A detection method of hidden danger for power utilization of low-voltage users based on local outlier factor (LOF)-Boosting is proposed by using voltage measurement data of the electricity information acquisition system. First, a method of voltage information reconstruction based on information entropy is used to expand the difference of voltage information. Second, a voltage data sparse coding method based on K-singular value decomposition (K-SVD) is presented to deal with the redundancy problem caused by the excessively high dimension of the original load characteristics. Then, the accuracy and the generalization ability of hidden danger detection for power utilization of low-voltage users are improved by optimizing the multiple LOF combination models. Finally, a case study of a certain low-voltage distribution area in Zhejiang Province of China demonstrates that the proposed method can achieve a higher detection accuracy than the traditional LOF algorithm.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 52077195) and National Key R&D Program of China (No. 2016YFB0901100).
引用本文
[1]林之岸,刘晟源,金伟超,等.基于改进局部离群因子的低压用户用电隐患检测方法[J].电力系统自动化,2022,46(1):130-138. DOI:10.7500/AEPS20210115006.
LIN Zhian, LIU Shengyuan, JIN Weichao, et al. Detection Method of Hidden Danger for Power Utilization of Low-voltage Users Based on Local Outlier Factor-Boosting[J]. Automation of Electric Power Systems, 2022, 46(1):130-138. DOI:10.7500/AEPS20210115006.
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  • 收稿日期:2021-01-15
  • 最后修改日期:2021-04-28
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  • 在线发布日期: 2022-01-05
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