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

Wang, Huaizhi; Ruan, Jiaqi; Wang, Guibin; Zhou, Bin; Liu, Yitao; Fu, Xueqian; Peng, Jianchun

doi:10.1109/tii.2018.2804669

Cited by 203 publications

(71 citation statements)

References 50 publications

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“…Since smart grids have the ability to establish bidirectional digital communication, and for that use information technologies, they are susceptible to cyber attacks. Because of this, many studies review cybersecurity issues, focusing on preventing attacks and network breaches that may be favored to potential vulnerabilities of control and communication systems [32][33][34]. However, few studies on assessing the dependability of smart grids considering their physical infrastructure and different supplies of power generation can be found in the literature.…”

Section: Related Workmentioning

confidence: 99%

A Methodology for Dependability Evaluation of Smart Grids

et al. 2019

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Smart grids are a new trend in electric power distribution, which has been guiding the digitization of electric ecosystems. These smart networks are continually being introduced in order to improve the dependability (reliability, availability) and efficiency of power grid systems. However, smart grids are often complex, composed of heterogeneous components (intelligent automation systems, Information and Communication Technologies (ICT) control systems, power systems, smart metering systems, and others). Additionally, they are organized under a hierarchical topology infrastructure demanded by priority-based services, resulting in a costly modeling and evaluation of their dependability requirements. This work explores smart grid modeling as a graph in order to propose a methodology for dependability evaluation. The methodology is based on Fault Tree formalism, where the top event is generated automatically and encompasses the hierarchical infrastructure, redundant features, load priorities, and failure and repair distribution rates of all components of a smart grid. The methodology is suitable to be applied in early design stages, making possible to evaluate instantaneous and average measurements of reliability and availability, as well as to identify eventual critical regions and components of smart grid. The study of a specific use-case of low-voltage distribution network is used for validation purposes.

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Section: Related Workmentioning

confidence: 99%

A Methodology for Dependability Evaluation of Smart Grids

et al. 2019

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“…An end-to-end case study of how to instantiate real FDI attacks to the AC state estimation process presented in [23]. Authors in [24] use two-stage sparse cyber-attack models for smart grid with complete and incomplete network information and then propose a novel detection mechanism based on dual optimization problem and stacked auto-encoder (SAE) typical deep learning method.…”

Section: Fdi Attacks In Smart Gridsmentioning

confidence: 99%

Graph theoretical defense mechanisms against false data injection attacks in smart grids

Ansari

Vakili

Tavassoli

2018

J. Mod. Power Syst. Clean Energy

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This paper addresses false data injection, which is one of the most significant security challenges in smart grids. Having an accurately estimated state is of great importance for maintaining a stable running condition of smart grids. To preserve the accuracy of the estimated state, bad data detection (BDD) mechanisms are utilized to remove erroneous measurements due to meter failures or outsider attacks. In this paper we use a graph-theoretical formulation for false data injection attacks in smart grids and propose defense mechanisms to mitigating this type of attacks. To this end we discuss characteristics of a typical smart grid graph such as planarity. Then we propose three different approaches for finding optimal protected meters set: a fast and efficient heuristic algorithm that works well in practice, an approximation algorithm that provides guarantee for the quality of the protected set, and an exact algorithm that find the optimal solution. Our extensive simulation results show that our algorithms outperform similar existing solutions in terms of different performance metrics.

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“…Consequently, two general cases are considered in this paper: The current recursive component e S is on the main network or not. This is because the service to the downstream subarea of e S can be restored via closing one available AS if e S sits on the main network, otherwise the service cannot be restored [30].…”

Section: Top-down Recursive Processmentioning

confidence: 99%

Recursive Method for Distribution System Reliability Evaluation

Wang

Zhang

Li³

et al. 2018

Energies

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This paper proposes a novel hybrid recursive method for distribution system reliability evaluation to deal with the computational limit and low-efficiency problem which exist in previously developed techniques as the system becomes larger. This method includes a bottom-up process and a top-down process, which are developed on the basis of a recursive principle, and the synthesis of both processes yield the reliability performance of each bus of the system. The bottom-up process considers the effects of downstream failures on upstream customers, and the top-down process considers the effects of upstream failures on downstream customers. In addition, a novel switch zone concept is defined and introduced into the bottom-up recursive process to save the computation cost. Besides, section technique (ST) and shortest path method (SPM) are employed to effectively simplify the recursive path and thus, the computation efficiency can be improved. The most significant feature of the proposed method over ST, SPM, failure mode and effect analysis (FMEA) is that it provides a more generalized equivalent approach to maximally simplify the network for reliable evaluation irrespective of the network topology. The effectiveness of the proposed method has been validated through comprehensive tests on Roy Billinton test system (RBTS) bus 6 and a practical-sized distribution system in China.

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

Cited by 203 publications

References 50 publications

A Methodology for Dependability Evaluation of Smart Grids

A Methodology for Dependability Evaluation of Smart Grids

Graph theoretical defense mechanisms against false data injection attacks in smart grids

Recursive Method for Distribution System Reliability Evaluation

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