摘要
智能电能表作为用电户与电网的信息链接点,能为电网提供用电户的用电习惯和负荷特征等信息,对指导电网合理安排电力负荷、提高电网运行效率具有重要价值。但实际电网中,智能电能表获得的大量测量数据中,不可避免地存在一部分由多种原因导致的异常数据,例如用电户或电网中的突发事件、传感器的暂时故障、数据传输或存储故障,甚至人为的网络攻击,等等。如何从智能电能表测量数据中辨别、提取、剔除这些异常数据,是准确获取用户负荷信息的关键。针对这一问题,在参考国外国内相关文献基础上,对智能电能表测量数据诊断方法进行了梳理、归纳和综述,并对不同方法的数学原理、适用范围等进行了比较和讨论。
As a bridge between the customer and the power grid,the smart meter can supply useful information of electricity consumption habit and load characteristics for load forecasting,it is of great significance to guide power grid to arrange power load reasonably and improve the efficiency of the power grid operation.However,in reality,a part of the large set of measurement data contains misleading outliers,which are caused by many reasons,e.g.an accident in either the user side or the power grid side,failure of the instrument or a sensor,fault during data transmission or storage,internet hack,etc.How to detect,indicate and delete these outliers among the smart meter data is of great importance towards an accurate load forecasting.In this paper,we summarize and review possible approaches in literatures for outlier detection that can be used for smart meter data analytic.The principle,mathematical realization and application of these techniques are discussed.
作者
裴茂林
黄洋界
赵伟
李世松
Pei Maolin;Huang Yangjie;Zhao Wei;Li Shisong(Electric Power Research Institute of State Grid Jiangxi Electric Power Company,Nanchang 330069,China;Department of Electrical Engineering,Tsinghua University,Beijing 100084,China)
出处
《电测与仪表》
北大核心
2018年第23期129-135,145,共8页
Electrical Measurement & Instrumentation
基金
国家电网公司科技项目(52182017001T)
