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
投稿时间:2022-03-29  修订日期:2022-03-29
DOI:
中文关键词: 计量失准  神经网络  电能表  电压采样电阻  核密度估计法
英文关键词: Measurement inaccuracy  Neural networks  Electric energy meter  Voltage sampling resistance  Kernel density estimation
基金项目:
作者单位
朱铮 国网上海市电力公司电力科学研究院 
陈海宾 国网上海市电力公司电力科学研究院 
蒋超 国网上海市电力公司电力科学研究院 
甄昊涵 国网上海市电力公司电力科学研究院 
许堉坤 国网上海市电力公司电力科学研究院 
童涛 国网上海市电力公司电力科学研究院 
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中文摘要:
      针对一类电压采样电阻异常的电能表计量失准问题,本文提出了一种基于神经网络模型的计量失准判定方法。首先,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 的控制限 。然后通过仿真验证所提方法的有效性,并获得以下判定规则:当 时,电能表计量失准;当 时,电能表计量正常。此外,本文所提出的方法可以提前预判电能表的计量异常状态,提早排除用电安全隐患。
英文摘要:
      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 in this paper. Firstly, 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 limit of the residual Mahalanobis distance is obtained by using the kernel density estimation method. Then the effectiveness of the proposed method is verified by simulation, and the following judgment rules are obtained: when , the metering of electric energy meter is inaccurate; When , the metering of electric energy meter is normal. In addition, the method proposed in this paper can predict the abnormal state of electric energy meter in advance and eliminate the hidden dangers of power consumption safety in advance.
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