Abstract
Due to the strong concealment of electricity theft and the limitation of inspection resources, the number of power theft samples mastered by the power department is insufficient, which limits the accuracy of power theft detection. Therefore, a data augmentation method for electricity theft detection based on the conditional variational auto-encoder (CVAE) is proposed. Firstly, the stealing power curves are mapped into low dimensional latent variables by using the encoder composed of convolutional layers, and the new stealing power curves are reconstructed by the decoder composed of deconvolutional layers. Then, five typical attack models are proposed, and the convolutional neural network is constructed as a classifier according to the data characteristics of stealing power curves. Finally, the effectiveness and adaptability of the proposed method is verified by a smart meters’ data set from London. The simulation results show that the CVAE can take into account the shapes and distribution characteristics of samples at the same time, and the generated stealing power curves have the best effect on the performance improvement of the classifier than the traditional augmentation methods such as the random oversampling method, synthetic minority over-sampling technique, and conditional generative adversarial network. Moreover, it is suitable for different classifiers.
Highlights
The electrical loss includes non-technical loss and technical loss
The accuracy, Macro F1, and G-mean of convolutional neural network (CNN) are improved by 7.00%, 6.65%, and 7.01%, respectively after data augmentation
Due to the strong concealment of electricity theft and the limitation of inspection resources, the number of power theft samples mastered by the power department is insufficient, which limits the accuracy of power theft detection
Summary
The electrical loss includes non-technical loss and technical loss. Technical loss is an unavoidable loss in the process of power transmission, which is determined by power loads and parameters of power supply equipment. The detection of electricity theft is of great significance to reduce non-technical loss. The existing methods for electricity theft detection can be divided into supervised classification and unsupervised regression. The unsupervised regression method is to determine the electricity theft by comparing the deviation between the actual value and the predicted value of the power load [3]. This kind of method does not need a labeled data set to train the model, but it is difficult to set the threshold and the detection accuracy is low [4,5]. Supervised classification methods mainly include traditional data mining models such as support vector machine (SVM), multi-layer perceptron (MLP), Bayesian network, extreme gradient boosting tree (XGBoost) [6,7,8,9,10], and new deep learning technologies such as the deep belief network and convolutional neural network (CNN) [11,12,13]
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