Real‐time stability assessment in smart cyber‐physical grids: a deep learning approach

Darbandi, Farzad; Jafari, Amirreza; Karimipour, Hadis; Dehghantanha, Ali; Derakhshan, Farnaz; Choo, Kim‐Kwang Raymond

doi:10.1049/iet-stg.2019.0191

Cited by 21 publications

(6 citation statements)

References 28 publications

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“…The extracted features are scale‐invariant and can also prevent overfitting. One should generally use mean pooling, such as downsampling [38]. Fully connected and discarded layer.…”

Section: Active Detection Methods For Power Cps Fdias With Gru‐cnn Hy...mentioning

confidence: 99%

Active and passive hybrid detection method for power CPS false data injection attacks with improved AKF and GRU‐CNN

et al. 2022

IET Renewable Power Gen

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With the deep penetration of the new generation of information technology in the power system, the power system has gradually evolved into a highly coupled cyber-physical systems (CPS), and false data injection attacks (FDIAs) is the most threatening attack among many power CPS network attacks. Aiming at the problem that the existing knowledge-driven detection process of FDIAs has been in a passive detection state for a long time and ignores the advantages of data-driven active capture of features, an active and passive hybrid detection method for power CPS FDIAs with improved adaptive Kalman filter (AKF) and convolutional neural networks (CNN) is proposed in this paper. First, it analyzes the shortcomings of the traditional AKF algorithm in terms of filtering divergence and calculation speed. The state estimation algorithm based on non-negative positive definite adaptive Kalman filter (NDAKF) is improved, and FDIAs passive detection method with similarity Euclidean distance detection and residual detection as the core is constructed. Then, combined with the advantages of gate recurrent unit (GRU) and CNN in terms of temporal memory and feature expression ability, an active detection method of FDIAs based on GRU-CNN hybrid neural network is designed. Finally, the results of joint knowledge-driven and data-driven parallel detection define a mixed fixed calculation formula, and the active and passive hybrid detection method of FDIAs is established considering the characteristic constraints of the parallel mode. The simulation system example of power CPS FDIAs verifies the effectiveness and accuracy of the method proposed in this paper. INDEX TERMSPower cyber-physical systems, false data injection attacks, adaptive Kalman filter, gate recurrent unit, convolutional neural networks, active and passive hybrid detection. YANG LI received his Ph.D. degree in Electrical Engineering from

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“…The extracted features are scale‐invariant and can also prevent overfitting. One should generally use mean pooling, such as downsampling [38]. Fully connected and discarded layer.…”

Section: Active Detection Methods For Power Cps Fdias With Gru‐cnn Hy...mentioning

confidence: 99%

Active and passive hybrid detection method for power CPS false data injection attacks with improved AKF and GRU‐CNN

et al. 2022

IET Renewable Power Gen

View full text Add to dashboard Cite

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“…It is noted that the first IMF (IMF1) 1 ( )consists of the highest signal processing frequency and is often utilized as the input for subsequent processing with HT [31]. B) Hilbert transform (Conversion): by considering IMFs which is derived using the EMD approach, HT can be used for any component of the IMF which is defined in equation (2).…”

Section: A Hilbert-huang Transform (Hht)mentioning

confidence: 99%

“…Cyber Physical Systems (CPSs) usually concentrate on connecting the physical globe to the cyber and digital world; also they are greatly utilized in the control of various industrial systems until several individuals can be able to grasp numerous kinds of required information in the real time [1][2][3]. The usage of CPS has a prominent potential of using in some fields including power distribution systems and sewage treatment plants.…”

Section: Introductionmentioning

confidence: 99%

False Data Injection Attack Detection based on Hilbert-Huang Transform in AC Smart Islands

et al. 2020

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In Smart Island (SI) systems, the operators of power distribution system usually utilize actual-time measurement information as the Advanced Metering Infrastructure (AMI) to have accurate, efficient, advanced control and monitoring. SI system can be vulnerable to complicated information integrity attacks such as False Data Injection Attack (FDIA) on some equipment including sensors and controllers, which can generate misleading operational decision in the system. Today, lack of detailed research in the evaluation of power system that links the FDIAs with system stability is felt, and it will be important for both assessment of the effect of cyber-attack and taking preventive protection measures. In this regards, time-frequency-based differential approach is proposed for SI cyber-attack detection according to non-stationary signal assessment. In this paper, non-stationary signal processing approach of Hilbert-Huang Transform (HHT) is performed for the FDIA detection in several case studies. Since various critical case studies with a small FDIA in data where accurate and efficient detection can be a challenge, the simulation results confirm the efficiency of HHT approach. In this research, the configuration of the SI test case is developed in the MATLAB software with several Distributed Generations (DGs). As a result, it is found that the HHT approach is completely efficient and reliable for FDIA detection target in AC-SI. The simulation results verify that the proposed model is able to achieve accuracy rate of 93.17%.

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“…In relation to this, it is of utmost importance to develop models to address the afore-mentioned setbacks [10], [11]. In energy informatics, it is worth noting that numerous aspects ranging from grid control and management present an interdisciplinary platform that exploits machine learning algorithms formulated on mathematical principles to handle big data collected for forecasting and assessment of the smart grid system [12], [13], [14]. In this paper, a mathematical model for a smart grid cyber physical power system is developed.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Modelling and Behaviour Simulation of a Smart Grid Cyber-Physical System

Tamakloe,

Griffith,

Kommey

2024

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The significant contributions of information and communication technology (ICT) and other operational technologies (OTs) or cyber networks have had a tremendous impact on the real-time monitoring, management, and control of the power or energy system facilities. Thus, the integration of these technologies into the energy grid system created a smart, complex, and interdependent system. This system is established and referred to as a smart grid cyber physical power system (SGCPPS). The performances of cyber physical systems are achieved via computation and communication and are imperatively based on a real-time feedback mechanism. In reference to the energy system, monitoring and control of the grid systems is extremely essential in ensuring efficient power supply, quality, reliability, stability and resilience among other determinants. However, their interdependence and integrated nature exposes the grid to disturbances subsequently leading to faults in the grid. Hence, failure to know the grid conditions at a particular period subjugates it to complete system collapse. This paper focused on the development of a mathematical model for a smart gird cyber physical system. Additionally, simulations were performed to study the behaviour of the Smart grid cyber-physical power system (SGCPPS) with regards to monitoring and controlling the physical systems using MATLAB Simulink tool to facilitate system awareness.

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Real‐time stability assessment in smart cyber‐physical grids: a deep learning approach

Cited by 21 publications

References 28 publications

Active and passive hybrid detection method for power CPS false data injection attacks with improved AKF and GRU‐CNN

Active and passive hybrid detection method for power CPS false data injection attacks with improved AKF and GRU‐CNN

False Data Injection Attack Detection based on Hilbert-Huang Transform in AC Smart Islands

A Mathematical Modelling and Behaviour Simulation of a Smart Grid Cyber-Physical System

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