篡改电表数据是一种典型的窃电行为。针对此类窃电行为,现有的检测方法需要标记好的数据集或额外的电力系统状态信息,这在现实中很难获得或即使获得也与实际值存在较大误差。因此,利用较低维度的数据来实现对此类窃电行为进行检测的方...篡改电表数据是一种典型的窃电行为。针对此类窃电行为,现有的检测方法需要标记好的数据集或额外的电力系统状态信息,这在现实中很难获得或即使获得也与实际值存在较大误差。因此,利用较低维度的数据来实现对此类窃电行为进行检测的方法亟待深入研究。创新性地结合最大互信息系数(maximum information coefficient,MIC)技术和基于密度峰值的快速聚类算法提出了一种新的融合检测方法。该方法利用最大互信息系数度量管理线损与用户特定行为之间的相关性,采用CFSFDP定位异常用电用户,适用性强,能够检测多种不同类型的窃电行为。最后利用爱尔兰智能电表数据集进行了算法验证,结果证明了该方法的良好性能。展开更多
为解决现有的智能电网电力盗窃行为检测方法中准确性不足、检测效率低下等问题,提出了一种由卷积自编码器网络(convolutional auto-encoders,CAEs)和长短期记忆网络(long short term memory,LSTM)相结合的CAEs-LSTM检测模型。该模型通...为解决现有的智能电网电力盗窃行为检测方法中准确性不足、检测效率低下等问题,提出了一种由卷积自编码器网络(convolutional auto-encoders,CAEs)和长短期记忆网络(long short term memory,LSTM)相结合的CAEs-LSTM检测模型。该模型通过分析数据集的特点对电力数据进行二维转换,设计卷积自编码器结构,采用池化、下采样和上采样重构电力数据的二维空间特征,加入高斯噪声提高模型鲁棒性,并构建长短期记忆网络以学习全局时序特征。最后,对提取的时空特征进行融合从而检测能源窃贼,并进行了参数调优。在由国家电网公布的真实数据集上,通过将CAEs-LSTM模型与支持向量机、LSTM以及宽深度卷积神经网络进行对比,CAEs-LSTM模型的平均精度均值和曲线下面积值均最优。仿真实验表明,基于CAEs-LSTM模型的窃电检测方法具有更高的窃电检测效率和精度。展开更多
With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is vio...With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.展开更多
One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which make...One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.展开更多
文摘篡改电表数据是一种典型的窃电行为。针对此类窃电行为,现有的检测方法需要标记好的数据集或额外的电力系统状态信息,这在现实中很难获得或即使获得也与实际值存在较大误差。因此,利用较低维度的数据来实现对此类窃电行为进行检测的方法亟待深入研究。创新性地结合最大互信息系数(maximum information coefficient,MIC)技术和基于密度峰值的快速聚类算法提出了一种新的融合检测方法。该方法利用最大互信息系数度量管理线损与用户特定行为之间的相关性,采用CFSFDP定位异常用电用户,适用性强,能够检测多种不同类型的窃电行为。最后利用爱尔兰智能电表数据集进行了算法验证,结果证明了该方法的良好性能。
文摘为解决现有的智能电网电力盗窃行为检测方法中准确性不足、检测效率低下等问题,提出了一种由卷积自编码器网络(convolutional auto-encoders,CAEs)和长短期记忆网络(long short term memory,LSTM)相结合的CAEs-LSTM检测模型。该模型通过分析数据集的特点对电力数据进行二维转换,设计卷积自编码器结构,采用池化、下采样和上采样重构电力数据的二维空间特征,加入高斯噪声提高模型鲁棒性,并构建长短期记忆网络以学习全局时序特征。最后,对提取的时空特征进行融合从而检测能源窃贼,并进行了参数调优。在由国家电网公布的真实数据集上,通过将CAEs-LSTM模型与支持向量机、LSTM以及宽深度卷积神经网络进行对比,CAEs-LSTM模型的平均精度均值和曲线下面积值均最优。仿真实验表明,基于CAEs-LSTM模型的窃电检测方法具有更高的窃电检测效率和精度。
基金supported by National Natural Science Foundation of China(No.52277083).
文摘With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.
文摘One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.
