一种基于实际工况的数字化电能表校验方法及其误差分析  

A Calibration Method and Error Analysis of Digital Energy Meter Based on Actual Working Condition

作　　者：寇英刚 范洁 杨世海[2] 陈刚[2] 胡琛[3] 穆小星 徐敏锐[2] KOU Yinggang;FAN Jie;YANG Shihai;CHEN Gang;HU Chen;MU Xiaoxin;XU Mingrui(State Grid Jiangsu Electric Power Company, Nanjing 210024,China;State Grid Key Laboratory of Energy Metering(State Power Research Jiangsu Electric Power Company Institute), Nanjing 211103,China;SEEE of Huazhong University of Science and Technology,Wuhan 430074, China)

机构地区：[1]国网江苏省电力公司,江苏南京210024 [2]国家电网公司电能计量重点实验室(国网江苏省电力公司电力科学研究院),江苏南京211103 [3]华中科技大学电气与电子工程学院,湖北武汉430074

出　　处：《电力工程技术》2017年第6期53-57,共5页Electric Power Engineering Technology

基　　金：国家重点研发计划资助项目(2016YFB0901104);国家电网公司科技项目(5210EF17001M)

摘　　要：数字化电能表在实际工况下受到频率波动、谐波、输入噪声等因素影响,常出现误差超差的现象。为了研究现场实际工况下数字化电能表的计量性能,提出了一种基于实际工况的校验方法,研制了工况复现装置,推出了Blackman离散傅里叶变换(DFT)+自适应线性(Adaline)神经网络算法,实现了标准电能的计算,并将该数字化电能表校验方法和瓦秒法进行了比较分析。误差分析结果表明基于实际工况的数字化电能表校验方法和瓦秒法均能用于校验数字化电能表,但是前者的测试结果波动更小,更加稳定,且能够为现场复杂工况下电能表性能评估提供参考。Digital energy meters often appear error phenomenon in the actual working conditions such as frequency fluctuations,harmonics,input noise and other factors.In order to study the measurement performance of the digital energy meter under the actual working conditions,a calibration method based on the actual working condition is presented.A condition recovery device is developed.A Blackman discrete Fourier transform(DFT)+adaptive linear(Adaline)neural network algorithm is proposed.The calculation of standard electrical energy is achieved.The digital energy meter calibration method and the wattage-second method are compared and analyzed.The results of error analysis show that the digital energy meter calibration method based on the actual working conditions and the wattage-second method can be used to verify the digital energy meter.But the test results of the former are smaller and more stable,and can provide reference for the performance evaluation of the energy meter under complex conditions.

关 键 词：数字化电能表 实际工况 校验 傅里叶变换 自适应线性神经网络 

分 类 号：TM933[电气工程—电力电子与电力传动]