基于支持向量机的多回路关口电能表在线监测方法研究

陈龙瑾; 邢菁; 吴达雷; 戚斌; 陈育培

陈龙瑾,邢菁,吴达雷,戚斌,陈育培.基于支持向量机的多回路关口电能表在线监测方法研究[J].电测与仪表,2023,60(10):194-200.
Chen Longjin,Xing Jing,Wu Dalei,Qi Bin,Chen Yupei.Research on on-line monitoring method of multi-loop gateway watt-hour meter based on SVM[J].Electrical Measurement & Instrumentation,2023,60(10):194-200.

基于支持向量机的多回路关口电能表在线监测方法研究

Research on on-line monitoring method of multi-loop gateway watt-hour meter based on SVM

DOI：10.19753/j.issn1001-1390.2023.10.032

中文关键词: 小波包能量谱支持向量机多回路关口电能表监测系统

英文关键词:wavelet packet energy spectrum, SVM, Multi-loop gateway watt hour meter, monitoring system

基金项目:中国南方电网有限责任公司科技项目（070000KK52200006）

作者中文名	作者英文名	单位
陈龙瑾	Chen Longjin	海南电网有限责任公司电能计量中心
邢菁	Xing Jing	海南电网有限责任公司电能计量中心
吴达雷	Wu Dalei	海南电网有限责任公司电能计量中心
戚斌	Qi Bin	海南电网有限责任公司电能计量中心
陈育培	Chen Yupei	海南电网有限责任公司电能计量中心

摘要点击次数: 1492

中文摘要:

根据多回路关口电能表监测的多通道采样连续性、采样信号的多特征及耦合性，提出一种基于支持向量机的多回路关口电能表在线监测方法，该方法分别设计了基于监测系统的信号采集单元、数据通信单元、数据处理单元及上位机显示单元；文中首先搭建了电能表的多个故障缺陷模型，通过三相电流及电压传感器组成的信号采集单元将缺陷模型的三相电流及电压实时数据传送给数据处理单元，数据处理单元对收集的三相电流数据进行基础运算后形成对应的有功功率样本数据并采用小波包能量谱进行离线分析后形成故障特征池，基于此，构建了基于支持向量机算法的多分类支持向量机模型并将得到的模型内嵌于上位机显示单元；监测系统运行时上位机显示单元直接基于模型的关联规则，和转换后的采样信息进行特征匹配，实现对电能表运行工况的实时监测。经实例验证，结果表明该方法能够实时、准确的对多回路关口电能表进行在线监测，具有较强的应用价值及前景。

英文摘要:

According to the multi-channel sampling continuity of multi-loop gateway electric energy meter monitoring, the multi-features and coupling of sampled signals, an online monitoring method of multi-loop gateway electric energy meter based on support vector machine is proposed. The online monitoring method of the meter is designed based on The signal acquisition unit, data communication unit, data processing unit and host computer display unit of the monitoring system; the article first builds multiple fault defect models of the electric energy meter, and uses the signal acquisition unit composed of three-phase current and voltage sensors to divide the three fault models. The real-time data of phase current and voltage are transmitted to the data processing unit. The data processing unit performs basic calculations on the collected data to form the corresponding model active power sample data and uses the wavelet packet energy spectrum for offline analysis to form a fault feature pool. Based on this, construct A multi-class support vector machine model based on the support vector machine algorithm is built and the obtained model is embedded in the upper computer display unit; the upper computer display unit directly matches the converted sampling information based on the association rules of the model when the monitoring system is running. Realize the real-time monitoring of the operating conditions of the electric energy meter. The results show that the method can quickly and accurately monitor the multi-circuit gateway electric energy meter online, and it has strong application value and prospects.

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