期刊
IEEE INTERNET OF THINGS JOURNAL
卷 7, 期 9, 页码 8218-8227出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.2983911
关键词
Power systems; State estimation; Detectors; Power measurement; Meters; Deep learning; Computer architecture; Convolutional neural network (CNN); false data injection attack (FDIA); multilabel classification; power system; security; state estimation
资金
- National Key Research and Development Program [2019YFB1803305]
- National Natural Science Foundation of China [61871271]
- General Research Funding by Hong Kong Research Grant Council [14200315]
- Guangdong Province Pearl River Scholar Funding Scheme 2018
- Foundation of Shenzhen City [JCYJ20170818101824392, JCYJ20190808120415286]
- Science and Technology Innovation Commission of Shenzhen [827/000212]
State estimation is critical to the monitoring and control of smart grids. Recently, the false data injection attack (FDIA) is emerging as a severe threat to state estimation. Conventional FDIA detection approaches are limited by their strong statistical knowledge assumptions, complexity, and hardware cost. Moreover, most of the current FDIA detection approaches focus on detecting the presence of FDIA, while the important information of the exact injection locations is not attainable. Inspired by the recent advances in deep learning, we propose a deep-learning-based locational detection architecture (DLLD) to detect the exact locations of FDIA in real time. The DLLD architecture concatenates a convolutional neural network (CNN) with a standard bad data detector (BDD). The BDD is used to remove the low-quality data. The followed CNN, as a multilabel classifier, is employed to capture the inconsistency and co-occurrence dependency in the power flow measurements due to the potential attacks. The proposed DLLD is model-free in the sense that it does not leverage any prior statistical assumptions. It is also cost-friendly in the sense that it does not alter the current BDD system and the runtime of the detection process is only hundreds of microseconds on a household computer. Through extensive experiments in the IEEE bus systems, we show that DLLD can perform locational detection precisely under various noise and attack conditions. In addition, we also demonstrate that the employed multilabel classification approach effectively enhances the presence-detection accuracy.
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