基于多层次非负稀疏编码和SVM的窃电检测方法

Electricity theft detection based on multi-level non-negative sparse coding and electricity theft scenario analysis

针对现有方法对新型窃电方式检测准确率不高的问题,文中提出了一种基于多层次非负稀疏编码和支持向量机(support vector machines,SVM)的窃电检测新方法。该方法以月度用电曲线为检测对象,基于多层次非负稀疏编码提取样本的多层次用电模式特征,以及窃电情景分析提取样本的数值统计特征,将二者的融合检测特征输入SVM分类器进行窃电检测。以爱尔兰智能电表数据集构造的算例验证了所提方法能够提高窃电检测的精确率和召回率。

Existing detection methods of electricity theft have low detection accuracy for new means of electricity theft.This paper proposes a new theft detection method based on multi-level non-negative sparse coding and SVM.Using the monthly electricity consumption curve as the detection object,firstly,the multi-level electricity consumption pattern characteristics of the sample are extracted based on the multi-level non-negative sparse coding;next,the numerical statistical characteristics of the sample are extracted based on the electricity theft scenario analysis;then,the fusion detection features of the above two characteristics are input into the SV M classifier for electricity theft detection.Finally,the Irish smart meter.data set is used as the example to verify the effectiveness of the proposed method,showing the improved accuracy and recall rate of the detection.