基于图转换和混合卷积神经网络的窃电检测方法

Electricity Theft Detection Method Based on Graph Transformation and Hybrid Convolutional Neural Network

传统的窃电检测方法大多基于一维用电序列数据构建模型,同时单一的分类模型往往限制了电力用户行为规律的深层挖掘。为进一步提高窃电行为的检出率,提出一种基于图转换和混合卷积神经网络的窃电检测方法。首先,为了更好地捕捉用户窃电前后的用电周期性特征差异,引入基于格拉姆角和场的图转换方法,实现用电数据的二维化。然后,针对不同维度的输入形式,利用混合卷积神经网络同步提取原始一维序列数据的全局特征以及二维图像数据保留的时序相关性特征。实际算例结果表明,所提模型的受试者工作特性曲线下面积、查全率以及F1分数3项指标相比于随机森林、宽而深卷积神经网络等模型取得了有效的提升。

Traditional electricity theft detection methods mostly build models based on one-dimensional power consumption data sequence,and a single classification model usually limits the deep mining of the law of electricity users’behavior.In order to further improve the detection rate of electricity theft behavior,a novel electricity theft detection method based on the graph transformation and hybrid convolutional neural network is proposed.First,in order to better capture the difference in the periodic characteristics of electricity consumption before and after electricity theft behavior of users,the Gramian angular summation field image transformation method is introduced to realize the two-dimensional transformation of the electricity consumption data.Then,according to different dimensional forms of input,the hybrid convolutional neural network is applied to synchronously extract the global features of the original one-dimensional sequence data and the temporal correlation features retained by the two-dimensional image data.Actual case results show that the three performance indicators of the proposed model,i.e.area under the receiver operation characteristic curve,recall,and F1-score,have been effectively improved compared with the models such as random forest and wide&deep convolutional neural network.