Detectors on Edge Nodes against False Data Injection on Transmission Lines of Smart Grid

Xun, Peng; Zhu, Peidong; Zhang, Zhenyu; Cui, Pengshuai; Xiong, Yinqiao

doi:10.3390/electronics7060089

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…Due to the limitation of wireless bandwidth, it is difficult to meet the real-time response requirements. In view of the potential hazardous points that are subject to the damage by the fleeting external forces, the lag in the response speed can lead to serious consequences [8].…”

Section: Introductionmentioning

confidence: 99%

Application of AI Image Recognition in the Omnidirectional Monitoring System for Power Transmission Lines

Zeng

Jia

et al. 2021

Advances in Multimedia

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With the continuous advancement of social economy, electric power, as an essential resource, plays an important supporting role for the development of industry and the lives of residents. As electric power itself is dangerous to a certain level, it is crucial to use the electric power rationally and ensure the safe application of electrical equipment. In this paper, AI image recognition technology is introduced to establish a comprehensive monitoring system for power transmission lines. Through the control of the potential risks of the transmission lines, emergency treatment of power transmission lines is implemented, which has provided an application support for the safe use of power equipment. Simulation experiments have verified that the image recognition technology based on AI can support omnidirectional monitoring of power transmission lines effectively.

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Section: Introductionmentioning

confidence: 99%

Application of AI Image Recognition in the Omnidirectional Monitoring System for Power Transmission Lines

Zeng

Jia

et al. 2021

Advances in Multimedia

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“…Vellaithurai et al [7] propose a security-oriented stochastic risk management technique for calculation of cyber-physical security indices. Wadhawan et al [8] consider false data injection detection on transmission lines of a GCPS and present a detection framework. Xun et al [9] provide a comprehensive study of smart grid security against cyber-physical attacks and describe a risk assessment methodology.…”

Section: Discussionmentioning

confidence: 99%

“…Rahnamay-Naeini and Hayat [4] model and analyze the cascading failures and effects of interdependence on system reliability. The robustness [2,3,5,6], reliability [4] and security [7][8][9] of GCPSs have been analyzed in numerous papers. It necessitates to perform safety assessment for power grids [10,11].…”

Section: Introductionmentioning

confidence: 99%

Safety Analysis of AADL Models for Grid Cyber-Physical Systems via Model Checking of Stochastic Games

et al. 2019

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As safety-critical systems, grid cyber-physical systems (GCPSs) are required to ensure the safety of power-related systems. However, in many cases, GCPSs may be subject to uncertain and nondeterministic environmental hazards, as well as the variable quality of devices. They can cause failures and hazards in the whole system and may jeopardize system safety. Thus, it necessitates safety analysis for system safety assurance. This paper proposes an architecture-level safety analysis approach for GCPSs applying the probabilistic model-checking of stochastic games. GCPSs are modeled using Architecture Analysis and Design Language (AADL). Random errors and failures of a GCPS and nondeterministic environment behaviors are explicitly described with AADL annexes. A GCPS AADL model including the environment can be regarded as a game. To transform AADL models to stochastic multi-player games (SMGs) models, model transformation rules are proposed and the completeness and consistency of rules are proved. Property formulae are formulated for formal verification of GCPS SMG models, so that occurrence probabilities of failed states and hazards can be obtained for system-level safety analysis. Finally, a modified IEEE 9-bus system with grid elements that are power management systems is modeled and analyzed using the proposed approach.

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“…In [18], a data-driven and low-sparsity false data injection attack strategy against smart grid was investigated. In [19], a machine learning solution was proposed to identify the false data injection attacks on transmission lines of smart grids. Existing data-driven approaches, however, have not yet been used to detect cyber and physical attacks in the device level (power electronics converters).…”

Section: Introductionmentioning

confidence: 99%

Detection and Identification of Cyber and Physical Attacks on Distribution Power Grids With PVs: An Online High-Dimensional Data-Driven Approach

Xie

Yang

et al. 2022

IEEE J. Emerg. Sel. Topics Power Electron.

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Cyber and physical attacks threaten the security of distribution power grids. The emerging renewable energy sources such as photovoltaics (PVs) introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the distribution power networks, in this paper, we propose a novel high-dimensional data-driven cyber physical attack detection and identification approach (HCADI). Firstly, we analyze the cyber and physical attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we construct a high dimensional streaming data feature matrix based on signal analysis of multiple sensors in the network. Next, we propose a novel mechanism including leverage score based attack detection and binary matrix factorization based attack diagnosis. By leveraging the data structure and binary coding, our HCADI approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. To the best of our knowledge, it is the first attempt to use raw electrical waveform data to detect and identify the power electronics cyber/physical attacks in distribution power grids with PVs.

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Detectors on Edge Nodes against False Data Injection on Transmission Lines of Smart Grid

Cited by 13 publications

References 16 publications

Application of AI Image Recognition in the Omnidirectional Monitoring System for Power Transmission Lines

Application of AI Image Recognition in the Omnidirectional Monitoring System for Power Transmission Lines

Safety Analysis of AADL Models for Grid Cyber-Physical Systems via Model Checking of Stochastic Games

Detection and Identification of Cyber and Physical Attacks on Distribution Power Grids With PVs: An Online High-Dimensional Data-Driven Approach

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