一类电压采样电阻异常的电能表计量失准研究  

Study of measurement inaccuracy for an electric energy meter with abnormal voltage sampling resistance

作　　者：朱铮 陈海宾 蒋超 甄昊涵 许堉坤 童涛 ZHU Zheng;CHEN Haibin;JIANG Chao;ZHEN Haohan;XU Yukun;TONG Tao(State Grid Shanghai Electric Power Company Electric Power Research Institute,Shanghai 200437,China)

机构地区：[1]国网上海市电力公司电力科学研究院,上海200437

出　　处：《中南民族大学学报（自然科学版）》2022年第6期682-688,共7页Journal of South-Central University for Nationalities：Natural Science Edition

基　　金：国家电网专项资助项目(52094020005S)。

摘　　要：针对一类电压采样电阻异常的电能表计量失准问题,提出了一种基于神经网络模型的计量失准判定方法.分析电能表计量失准原因,构建神经网络模型预测电压采样值的残差变化,采用核密度估计法获得残差的马氏距离d的控制限λ.通过仿真验证了所提方法的有效性,并获得了以下判定规则:当d>λ时,电能表计量失准;当d≤λ时,电能表计量正常.所提出的方法可以提前预判电能表的计量异常状态,排除用电安全隐患.Aiming at the measurement inaccuracy of a kind of electric energy meter with abnormal voltage sampling resistance,a measurement inaccuracy judgment method based on neural network model is proposed.The reason of the meter's measurement inaccuracy is analyzed,the neural network model is constructed to predict the residual change of the voltage sampling value,and the control limitλof the residual Mahalanobis distance d is obtained by using the kernel density estimation method.The effectiveness of the proposed method is verified by simulation,and the following judgment rules are obtained:when d>λ,the metering of electric energy meter is inaccurate;When d≤λ,the metering of electric energy meter is normal.The method proposed can predict the abnormal state of electric energy meter in advance and eliminate the hidden dangers of power consumption safety.

关 键 词：计量失准 神经网络 电能表 电压采样电阻 核密度估计法 

分 类 号：TM933.4[电气工程—电力电子与电力传动]