基于深度信念网络与数据聚合模型的智能电表数据异常检测方法

针对智能电网中广泛应用的智能电表(smart meters,SM)可能在测量和监视电能消耗的过程中遭受的多种网络攻击的问题,提出了一种新的异常模式检测框架,以防止智能电表的能源欺诈。所提方法首先基于智能电表向智能配变终端发送用户的用电特征数据,采用分布式数据模型对数据进行聚合,以更好地解决用户隐私保护问题;然后利用深度信念网络(deep belief network,DBN)将得到的数据与期望数据进行对比,以更好地获取数据特征,并对训练结果进行自上而下的特征优化;最后,通过智能配变终端将集群中的智能电表从1到N进行标记,并将执行数据经过深度信念网络提取特征传送至电表数据计量管理系统(meter data management system,MDMS),检查并更换故障或受损的智能电表,以获得更精确的非专业技术损失检测分析。实验结果表明,所提方法相对于传统智能电表数据异常检测具有更高的检测率和适用性。

Anomaly Detection Method of Smart Meter Based on Deep Belief Network and Data Aggregation Model

Aiming at a variety of network attacks on smart meters(SM),which are widely used in smart grid,may be subjected to in the process of measuring and monitoring power consumption,a new abnormal pattern detection framework is proposed to prevent energy fraud in smart meters.The proposed method first sends the user s power characteristic data to the aggregator based on smart meter.And the distributed data model is used to aggregate the data to better solve the problem of user privacy protection.Then deep belief network(DBN)is used to compare the obtained data with the expected data to better obtain the data features and optimize the top-down features of the training results.Finally,the aggregator marks the SMs in clusters from 1 to N,and transmits the execution data to the meter data management system(MDMS),check through the deep belief network extraction features and replaces the faulty or damaged SM,for more accurate non-technical losses detection analysis.The experimental results show that the proposed method has higher detection rate and applicability than the traditional smart meter data anomaly detection.