首页|基于SE-CNN窃电行为模型的电力数据异常检测

基于SE-CNN窃电行为模型的电力数据异常检测

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为了有效防治用户窃电行为,在构建用电量趋势下降指标的基础上,设计一种基于通道注意力激励-卷积神经网络(SE-CNN)窃电行为模型的电力数据异常检测分析方法.基于CNN模型引入通道注意力网络(SENet)对特征通道重要程度进行调整,有效提升通道利用率.结果表明:SE-CNN模型的AUC值达到0.999,检测效率高,SE-CNN模型的可以适用于复杂电网环境;与支持向量机(SVM)、XGBoost和CNN进行比较,SE-CNN模型具有良好的评价指标,有效地减小了无效特征带来的实验影响,实现了局部区域上特征融合,使得实验数据快速达到拟合状态.
Abnormal Detection of Power Data Based on SE-CNN Model of Power Stealing Behavior
In order to prevent and control power stealing behavior effectively,an abnormal detection and analysis method of power data based on(SE-CNN)power stealing behavior model is designed on the basis of the construction of power consump-tion trend decline index.Based on the CNN model,SENet is introduced to adjust the importance of the feature channels,so as to effectively improve the utilization rate of the channels.The experiem ental results show that the AUC value of SE-CNN model reaches 0.999,the detection efficiency is high,and the SE-CNN model can be applied to complex power grid environ-ment.Compared with support vector machine(SVM),XGBoost and CNN,SE-CNN model has good evaluation indexes,effec-tively reduces the experimental influence brought by invalid features,realizes feature fusion in local areas,and makes experi-mental data quickly reach the fitting state.

power stealing beheaviorimproved convolutional neural networkabnormal detectionevaluation index

谈叶月、李莉、袁佳琰、孔陈祥

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江苏电力信息技术有限公司,江苏,南京 210000

窃电行为 改进卷积神经网络 异常检测 评价指标

2024

微型电脑应用
上海市微型电脑应用学会

微型电脑应用

CSTPCD
影响因子:0.359
ISSN:1007-757X
年,卷(期):2024.40(12)