Deep Learning-Based Interval State Estimation of AC Smart Grids Against Sparse Cyber Attacks

Due to the aging of electric infrastructures, conventional power grid is being modernized toward smart grid that enables two-way communications between consumer and utility, and thus more vulnerable to cyber-attacks. However, due to the attacking cost, the attack strategy may vary a lot from one ope...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2018-11, Vol.14 (11), p.4766-4778
Main Authors: Wang, Huaizhi, Ruan, Jiaqi, Wang, Guibin, Zhou, Bin, Liu, Yitao, Fu, Xueqian, Peng, Jianchun
Format: Article
Language:English
Subjects:
Cyber physical power system
Cybersecurity
Deep learning
Defense mechanisms
Electronic equipment tests
false data injection attack
Feature extraction
Forecasting
Load modeling
Machine learning
Mathematical models
Modernization
Normal distribution
Security
Smart grid
smart grid security
Smart grids
State estimation
State variable
Uncertainty
Upper bounds
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Summary:Due to the aging of electric infrastructures, conventional power grid is being modernized toward smart grid that enables two-way communications between consumer and utility, and thus more vulnerable to cyber-attacks. However, due to the attacking cost, the attack strategy may vary a lot from one operation scenario to another from the perspective of adversary, which is not considered in previous studies. Therefore, in this paper, scenario-based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed. Then, in order to effectively detect the established cyber-attacks, an interval state estimation-based defense mechanism is developed innovatively. In this mechanism, the lower and upper bounds of each state variable are modeled as a dual optimization problem that aims to maximize the variation intervals of the system variable. At last, a typical deep learning, i.e., stacked auto-encoder, is designed to properly extract the nonlinear and nonstationary features in electric load data. These features are then applied to improve the accuracy for electric load forecasting, resulting in a more narrow width of state variables. The uncertainty with respect to forecasting errors is modeled as a parametric Gaussian distribution. The validation of the proposed cyber-attack models and defense mechanism have been demonstrated via comprehensive tests on various IEEE benchmarks.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2018.2804669