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文章摘要
智能电表异常测量数据诊断方法综述
A Review on Outlier Detection Techniques for Smart Meter Data Analytic
Received:July 04, 2018  Revised:July 04, 2018
DOI：
中文关键词: 数据诊断  大数据  智能电表  负荷预测
英文关键词: Data diagnosis, big data, smart meter, load forecasting
基金项目:
Author NameAffiliationE-mail
Peimaolin* State Grid Jiangxi Electric Power Research Institute 29589219@qq.com 
2 State Grid Jiangxi Electric Power company 809351543@qq.com 
3 Tsinghua University zhaowei@mail.tsinghua.edu.cn 
Li Shisong Tsinghua University zhaowei@mail.tsinghua.edu.cn 
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中文摘要:
      智能电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，本文在参考国外国内相关文献基础上，对智能电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。
英文摘要:
      
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