ETD-ConvLSTM: A Deep Learning Approach for Electricity Theft Detection in Smart Grids
Xiaofang Xia, Jian Lin, Qiannan Jia, Xiaoluan Wang, Chaofan Ma, Jiangtao Cui, Wei Liang
Abstract
In smart grids, various Internet-of-Things-based (IoT-based) components are massively deployed across the power systems. However, most of these IoT-based components have their own vulnerabilities, leveraging which malicious users can launch different cyber/physical attacks to steal electricity. Economic losses caused by electricity theft amount to $96 billion in 2017. Most existing electricity theft detection techniques suffer from either a high deployment cost or a low detection accuracy. To address these concerns, we propose a novel Electricity Theft Detector based upon Convolutional Long Short Term Memory neural networks, called ETD-ConvLSTM. By installing a central observer meter in each community, we can know which communities have malicious users. For these communities, users’ time series of electricity consumptions with temporal correlations are transformed into spatio-temporal sequence data, mainly by constructing a two-dimensional matrix containing both consumptions and consumption differences among several adjacent days. This matrix is then divided into a sequence of sub-matrices, which are then fed into a ConvLSTM network consisting of multiple stacked ConvLSTM layers, with each layer formed by several temporarily concatenated ConvLSTM nodes. When capturing the periodicity in users’ consumption patterns, the ETD-ConvLSTM method considers both global and local knowledge, and hence the detection accuracy improves significantly. Simulations results show that compared with existing state-of-the-art detectors, the proposed ETD-ConvLSTM method can obtain better or comparable performance in terms of detection accuracy, false negative rates and false positive rates within much shorter detection time.