scholarly journals Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids

2021 ◽  
Vol 11 (12) ◽  
pp. 5706
Author(s):  
Moslem Dehghani ◽  
Taher Niknam ◽  
Mohammad Ghiasi ◽  
Pierluigi Siano ◽  
Hassan Haes Alhelou ◽  
...  
Keyword(s):  
Smart Grids ◽  
Sliding Mode ◽  
Renewable Energy Sources ◽  
Singular Values ◽  
Attack Detection ◽  
Operating Conditions ◽  
Cyber Attack ◽  
Switching Surface ◽  
Detection Model ◽  
False Data Injection

Cyber-physical threats as false data injection attacks (FDIAs) in islanded smart microgrids (ISMGs) are typical accretion attacks, which need urgent consideration. In this regard, this paper proposes a novel cyber-attack detection model to detect FDIAs based on singular value decomposition (SVD) and fast Fourier transform (FFT). Since new research are mostly focusing on FDIAs detection in DC systems, paying attention to AC systems attack detection is also necessary; hence, AC state estimation (SE) have been used in SI analysis and in considering renewable energy sources effect. Whenever malicious data are added into the system state vectors, vectors’ temporal and spatial datum relations might drift from usual operating conditions. In this approach, switching surface based on sliding mode controllers is dialyzed to regulate detailed FFT’s coefficients to calculate singular values. Indexes are determined according to the composition of FFT and SVD in voltage/current switching surface to distinguish the potential cyber-attack. This protection layout is presented for cyber-attack detection and is studied in various types of FDIA forms like amplitude and vector derivation of signals, which exchanged between agents such as smart sensor, control units, smart loads, etc. The prominent advantage of the proposed detection layout is to reduce the time (less than 10 milliseconds from the attack outset) in several kinds of case studies. The proposed method can detect more than 96% accuracy from 2967 sample tests. The performances of the method are carried out on AC-ISMG in MATLAB/Simulink environment.

scholarly journals Cyber Attack Detection based on Wavelet Singular Entropy in AC Smart Islands: False Data Injection Attack

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Moslem Dehghani ◽  
Mohammad Ghiasi ◽  
Taher Niknam ◽  
Abdollah Kavousi-Fard ◽  
Elham Tajik ◽  
...  
Keyword(s):  
Attack Detection ◽  
Cyber Attack ◽  
False Data Injection ◽  
False Data Injection Attack

Load frequency control in the presence of simultaneous cyber-attack and participation of demand response program

2022 ◽  
pp. 014233122110686
Author(s):  
Seyed Hossein Rouhani ◽  
Hamed Mojallali ◽  
Alfred Baghramian
Keyword(s):  
Power System ◽  
Demand Response ◽  
Sliding Mode ◽  
Load Frequency Control ◽  
Frequency Regulation ◽  
Cyber Attack ◽  
Smart Power ◽  
False Data Injection ◽  
Load Disturbance ◽  
Communication Time

Simultaneous investigation of demand response programs and false data injection cyber-attack are critical issues for the smart power system frequency regulation. To this purpose, in this paper, the output of the studied system is simultaneously divided into two subsystems: one part including false data injection cyder-attack and another part without cyder-attack. Then, false data injection cyber-attack and load disturbance are estimated by a non-linear sliding mode observer, simultaneously and separately. After that, demand response is incorporated in the uncertain power system to compensate the whole or a part of the load disturbance based on the available electrical power in the aggregators considering communication time delay. Finally, active disturbance rejection control is modified and introduced to remove the false data injection cyber-attack and control the uncompensated load disturbance. The salp swarm algorithm is used to design the parameters. The results of several simulation scenarios indicate the efficient performance of the proposed method.

scholarly journals A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80778-80788 ◽  
Author(s):  
Hadis Karimipour ◽  
Ali Dehghantanha ◽  
Reza M. Parizi ◽  
Kim-Kwang Raymond Choo ◽  
Henry Leung
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Large Scale ◽  
Attack Detection ◽  
Learning System ◽  
Cyber Attack ◽  
Unsupervised Machine Learning

scholarly journals Deep Learning Enabled Data Offloading With Cyber Attack Detection Model in Mobile Edge Computing Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185938-185949
Author(s):  
T. Gopalakrishnan ◽  
D. Ruby ◽  
Fadi Al-Turjman ◽  
Deepak Gupta ◽  
Irina V. Pustokhina ◽  
...  
Keyword(s):  
Deep Learning ◽  
Attack Detection ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Cyber Attack ◽  
Computing Systems ◽  
Data Offloading ◽  
Detection Model

scholarly journals A Novel False Data Injection Attack Detection Model of the Cyber-Physical Power System

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 95109-95125 ◽  
Author(s):  
Jie Cao ◽  
Da Wang ◽  
Zhaoyang Qu ◽  
Mingshi Cui ◽  
Pengcheng Xu ◽  
...  
Keyword(s):  
Power System ◽  
Attack Detection ◽  
Detection Model ◽  
False Data Injection ◽  
False Data Injection Attack

scholarly journals A Robust Electric Spring Model and Modified Backward Forward Solution Method for Microgrids with Distributed Generation

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1326
Author(s):  
Guillermo Tapia-Tinoco ◽  
David Granados-Lieberman ◽  
David A. Rodriguez-Alejandro ◽  
Martin Valtierra-Rodriguez ◽  
Arturo Garcia-Perez
Keyword(s):  
Steady State ◽  
Distributed Generation ◽  
Smart Grids ◽  
Model Simulation ◽  
Renewable Energy Sources ◽  
Electrical Circuit ◽  
Simulation Software ◽  
Operating Conditions ◽  
Electrical Network ◽  
Forward Solution

The electric spring (ES) is a contemporary device that has emerged as a viable alternative for solving problems associated with voltage and power stability in distributed generation-based smart grids (SG). In order to study the integration of ESs into the electrical network, the steady-state simulation models have been developed as an essential tool. Typically, these models require an equivalent electrical circuit of the in-test networks, which implies adding restrictions for its implementation in simulation software. These restrictions generate simplified models, limiting their application to specific scenarios, which, in some cases, do not fully apply to the needs of modern power systems. Therefore, a robust steady-state model for the ES is proposed in this work to adequately represent the power exchange of multiples ESs in radial micro-grids (µGs) and renewable energy sources regardless of their physical location and without the need of additional restrictions. For solving and controlling the model simulation, a modified backward–forward sweep method (MBFSM) is implemented. In contrast, the voltage control determines the operating conditions of the ESs from the steady-state solution and the reference voltages established for each ES. The model and algorithms of the solution and the control are validated with dynamic simulations. For the quasi-stationary case with distributed renewable generation, the results show an improvement higher than 95% when the ESs are installed. On the other hand, the MBFSM reduces the number of iterations by 34% on average compared to the BFSM.

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