基于SE-CNN模型的窃电检测方法研究

针对传统电网现有窃电检测仅用单一电量且实际数据集下检测准确度低的问题，提出一种基于通道注意力网络改进卷积神经网络模型的窃电行为检测方法。首先建立一种包含用电量趋势、线损增长率、终端告警多源数据融合的窃电评价指标体系，以此构建用户用电特征集。然后，基于通道注意力挤压激励网络(squeeze and excitation networks, SENet)优化卷积神经网络(convolutional neural network, CNN)模型，据此构建自适应通道注意力网络改进卷积神经网络模型的窃电检测方法。最后，利用南方电网数据集对提出方法的有效性与准确性进行验证。实测结果表明，所提方法能有效实现实际电网情况下各类窃电行为准确检测，建立的评价指标体系可更明显表征窃电行为规律。构建的窃电检测模型可自适应对特征通道重要程度调整，提升通道利用率。相比现有检测方法，其具有更高准确度和更优泛化性能。

A detection method of electricity theft behavior based on an SE-CNN model

There is a problem that the existing electricity theft detection of traditional power grids only uses a single amount of electricity and the detection accuracy is low under the actual dataset. Thus a method of electricity theft behavior detection based on a channel attention network improved convolutional neural network model is proposed. First, a multi-source data fusion electricity theft evaluation index system is established. This includes the trend of electricity consumption, the growth rate of line loss, and the terminal alarm, so as to construct the user''s electricity consumption feature set. Then, the convolutional neural network (CNN) model is optimized based on the channel attention squeeze and excitation networks (SENet). Thus an electricity theft detection method based on an improved CNN model of the adaptive channel attention network is constructed. Finally, the dataset of the China Southern Power Grid is used to verify the validity and accuracy of the proposed method. Actual measurement results show that the proposed method can effectively realize the accurate detection of various electricity theft behaviors in the actual power grid situation, and the established evaluation index system can more clearly characterize the electricity theft behavior rules. The constructed electricity theft detection model can adaptively adjust the importance of feature channels to improve channel utilization. Compared with existing detection methods, it has higher accuracy and better generalization ability.This work is supported by the National Natural Science Foundation of China (No. 51777061).