Benchmark of machine learning algorithms on transient stability prediction in renewable rich power grids under cyber-attacks

K Aygul, M Mohammadpourfard, M Kesici… - Internet of Things, 2024 - Elsevier
This study addresses the problem of ensuring accurate online transient stability prediction in
modern power systems that are increasingly dependent on smart grid technology and are …

Cyber attacks on power grids: Causes and propagation of cascading failures

VS Rajkumar, A Ştefanov, A Presekal, P Palensky… - IEEE …, 2023 - ieeexplore.ieee.org
Cascading effects in the power grid involve an uncontrolled, successive failure of elements.
The root cause of such failures is the combined occurrence of multiple, statistically rare …

[HTML][HTML] Electric load forecasting under False Data Injection Attacks using deep learning

A Moradzadeh, M Mohammadpourfard, C Konstantinou… - Energy Reports, 2022 - Elsevier
Precise electric load forecasting at different time horizons is an essential aspect for electricity
producers and consumers who participate in energy markets in order to maximize their …

Detection of cyber attacks in grid-tied PV systems using dynamic watermarking

HAJ Ibrahim, J Kim, JA Ramos-Ruiz… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
In this article an active defense mechanism for detecting cyber-attacks on a grid-tied PV
system is discussed. The proposed defense mechanism introduces a unique random signal …

Anomaly detection and state correction in smart grid using ekf and data compensation techniques

P Hu, W Gao, Y Li, X Guo, F Hua… - IEEE Sensors Journal, 2024 - ieeexplore.ieee.org
The smart grid is built on an integrated and high-speed communication network, which
renders it more vulnerable to cyber-attacks. False data injection attacks (FDIAs) and sudden …

Real-time detection of cyber-attacks in modern power grids with uncertainty using deep learning

M Mohammadpourfard, F Ghanaatpishe… - … on Smart Energy …, 2022 - ieeexplore.ieee.org
The smart grid, which is critical for developing smart cities, has a tool called state estimation
(SE), which enables operators to monitor the system's stability. While the SE result is …

A Method to Detect Man in the Middle Attack (MiTM) on a Grid Following PV Inverter

FH Alotaibi, H Ibrahim, J Kim… - 2023 IEEE 24th Workshop …, 2023 - ieeexplore.ieee.org
In this paper, a method to detect a man-in-the-middle attack (MiTM) on a grid following PV
inverter is discussed. The control objective of the grid following inverter is to utilize the …

Anomaly Detection in Load Forecasting for Electric Vehicles Using Image Processing Techniques

S Marandi, A Moradzadeh, H Moayyed… - … on Environment and …, 2024 - ieeexplore.ieee.org
The incorporation of electric vehicles (EVs) into the power grid presents significant
challenges, particularly in meeting the electricity requirements of EVs through effective …

Employing Machine Learning Algorithms to Identify False Data Injection in Smart Grid

M Tajdinian, M Mohammadpourfard… - 2024 9th International …, 2024 - ieeexplore.ieee.org
Enhancement in demand of electrical energy from industrial or residential consumers has
resulted in encouraging policy makers to utilize of new generation of power network named …

A Review of Cybersecurity Attacks on Bulk Power System: Assessing Risks

MFN Khan, F de Leon - Authorea Preprints, 2024 - techrxiv.org
Cyber security for power systems is becoming a concern for many researchers. However,
the impact of cyberattacks on actual power systems is unclear. This paper discusses …