Identification Algorithm of Abnormal Metering Data of Indirect Access DC Watt-hour Meter
In the abnormal identification of electric energy meter,there are few types of abnormal data that can be distinguished,and the error in abnormal data identification is large.Therefore,a new indirect access abnormal metering data identification al-gorithm of DC electric energy meter is designed.This paper normalizes the metering data of electric energy meter,introduce k-means clustering,calculate Euclidean distance,complete the clustering of all metering data of indirect access DC electric energy meter,optimize wavelet transform,obtain discrete wavelet,process the residual sequence of metering data,extract abnormal data characteristics,and identify abnormal metering data.The example test results show that the maximum error of the algo-rithm is 0.103,and the fluctuation is small.The output of normal measurement data and abnormal measurement data can a-chieve the purpose of optimizing the identification effect of abnormal measurement data of indirect access DC electric energy me-ter.
indirect accesselectricity meterabnormal data identificationk-means clusteringwavelet transformparameter solving
万方数据
维普
