Deep semi-supervised electricity theft detection in AMI for sustainable and secure smart grids

Abstract

Electricity theft is a major issue that affects the sustainability and security of smart grids. This paper proposes a deep semi-supervised method for electricity theft detection in the Advanced Metering Infrastructure (AMI) of smart grids. By only using normal samples to train the detection model, the proposed method has the capability of detecting unknown attacks in a short time frame. The method utilizes the ratio profile generated from the readings of the observer meter and the user’s smart meter as the input, to reduce false positives, which is then transformed into a 2D image with the continuous wavelet transform (CWT) to capture the time–frequency information. Discriminative features are extracted from the CWT image with a deep convolutional autoencoder (CAE) and principal component analysis (PCA), which are fed into a semi-supervised autoencoder for classification. The performance of the proposed method was evaluated and compared with a set of baselines and four supervised machine learning and deep learning methods under 11 different false data injection (FDI) attacks using smart meter data from both business and residential users. The results show that the proposed method significantly outperforms the baselines and is more capable of detecting unknown attacks than supervised methods.