基于小波框架的智能电能表台区识别技术研究  

Research on recognition technology of smart electricity meter area based on wavelet frame

作　　者：雷少波 刘丰硕 李健 卢晓宇 Lei Shaobo;Liu Fengshuo;Li Jian;Lu Xiaoyu(Power Marketing Service&Operation Management Branch,Inner Mongolia Power(Group)Co.,Ltd.,Hohhot 010000,China)

机构地区：[1]内蒙古电力(集团)有限责任公司电力营销服务与运营管理分公司,呼和浩特010000

出　　处：《电测与仪表》2021年第10期193-200,共8页Electrical Measurement & Instrumentation

摘　　要：为解决台区识别问题,引入小波框架的基本理论为依据,使各智能电能表在使用相同的一个小波框架,分别对自身采集的电压数据进行塔式分解,并对分解子信号进行分帧处理后计算其能量的分布情况,通过差分计算来判断出采样信号中的突变位置。利用现有用电信息采集系统的宽带载波通信网络,使智能电能表之间交互判断结果,最后通过突变位置的相似性来进行自身台区识别。理论分析和实际应用表明,新技术充分利用了小波框架在信号分析上算法计算复杂度低、内存数据保存量低且节点之间的通信负荷小、识别结果正确率高的优点,在无需引入新的硬件设备情况下,为管理部门提供了一种新型的、可远程同时对大量智能电能表进行台区识别的解决方案。To solve the problem of station identification,the basic theory of wavelet frame is introduced to make the smart electricity meter use the same wavelet framework,respectively to conduct pyramidal decomposition for the voltage data gathered themselves,and to frame the signal decomposition after calculating the distribution of energy.The position of sampling signal mutation is determined by differential calculation,the broadband carrier communication network of the existing electricity information acquisition system is used to make the smart electricity meters judge the results interactively.Finally,they can identify their own stations through the similarity of the mutation positions.Theoretical analysis and practical application show that the novel technique makes full use of the advantages of wavelet framework on low computational complexity in signal analysis,low memory data retention and small communication load between nodes,and the high accuracy rate of identification results.It provides a new solution for the management department which can remotely identify a large number of smart electricity meters at the same time without introducing new hardware equipment.

关 键 词：小波框架 台区识别 智能电能表 电压采样数据 

分 类 号：TM933[电气工程—电力电子与电力传动]