基于高维随机矩阵分析的窃电识别方法
摘要：窃电检查是用电检查的重点和难点。本文基于大数据理论,以电网运行采集参数为元素构建了高维随机矩阵,通过对矩阵的统计特性进行刻画,提出基于大数据分析的窃电识别方法,解决了传统窃电检查方法耗费人力大,时效性差,判断不精准的问题,从而实现了高效反窃电。文章以33节点电网运行模型为例,根据仿真采集到的电网随时间变化的电压电流等运行参数实现了对窃电发生判别、窃电发生时间确定、窃电地点的精确定位、窃电类型的判别。
Abstract:Electric larceny is hard to be checked and is important for power check. Based on big data theories, high dimensional random matrix can be built with parameters collected from the power grid as elements. Through characterizing statistical properties of the matrix, electric larceny recognition method based on big data analysis is proposed, which solves such problems existed in the traditional electric larceny recognition method as high cost of manpower, time lag and low accuracy. Therefore, an efficient anti-electric larceny is realized. Taking a 33-bus power grid as anexample, occurrence recognizing, the time determining, the precise locating and the type of electric larceny are realized based on such operation data collected from the grid as voltage and current that vary with time.
标题：基于高维随机矩阵分析的窃电识别方法
英文标题：Electric Larceny Recognition Method Based on High Dimensional Random Matrix Analysis
作者：王颖琛, 顾洁, 金之俭,
关键词：窃电识别, 高维随机矩阵, 协方差矩阵, 经验谱密度函数, MP律,
发表日期：2017-12-09
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