摘要
为了准确掌握配电网用户侧异常用电行为以及智能电表的故障情况,基于随机矩阵理论进行低压用户侧智能电表的数据分析与建模,以高维数据统计指标对用户侧的用电数据进行状态表征,在此基础上提出一种低压配电网用户侧异常用电及电表故障诊断分析方法。该方法通过对随机矩阵特征根平均谱半径(mean spectral radius,MSR)指标的分析,给出了随机矩阵原理应用于用户用电异常区域定位的具体步骤,同时也可以实现用户侧用电异常时刻点的特征发现。最后,以某用电台区智能电表历史与实时量测数据为实际算例,分别在不同采样时刻点与不同用户处设置用户窃电与电表损坏等异常用电类型进行计算分析,结果证明了所提方法的有效性与适用性。
In order to accurately grasp abnormal user-side electricity consumption behavior of the distribution network and fault situations of the smart meter,this paper makes analysis and modeling for the data from the low-voltage user-side smart meter based on the random matrix theory,and characterizes the state by high-dimensional statistical indicators.And then it proposes a diagnosis and analysis method for abnormal user-side electricity consumption and meter fault for the low-voltage distribution network.By analyzing the mean spectral radius(MSR)indicators of characteristic roots of the random matrix,this method gives specific steps for applying the random matrix principle in locating abnormal areas of users’electricity consumption and can discover features of abnormal time points.Finally,the paper takes historical and real-time measured data of the smart meter in one electricity consumption area for an example,and sets abnormal electricity consumption types such as electricity stealing and meter damages at different sampling time and users’locations for calculation and analysis.The results prove effectiveness and adaptability of the proposed method.
作者
郝方舟
孙奇珍
沈超
黄勇
吴雨沼
马国龙
HAO Fangzhou;SUN Qizhen;SHEN Chao;HUANG Yong;WU Yuzhao;MA Guolong(Guangzhou Power Supply Bureau Co.,Ltd.,Guangzhou,Guangdong 510620,China;Guangzhou Suihua Energy Technology Co.,Ltd.,Guangzhou,Guangdong 510530,China)
出处
《广东电力》
2019年第11期111-119,共9页
Guangdong Electric Power
基金
广州供电局有限公司科技项目(GZM2015-2-0005)
关键词
配电网
高维随机矩阵
特征根分布
智能电表
故障诊断
distribution network
high dimensional random matrix
characteristic root distribution
smart meter
fault diagnosis
