Results 71 to 80 of about 702 (166)

A Multi‐Stage NSGA‐III Optimization Model for False Data Injection Attacks in Integrated Power‐Hydrogen Cyber‐Physical Systems

IET Renewable Power Generation, Volume 19, Issue 1, January/December 2025.
A three‐stage false data injection attack (FDIA) optimization model is developed that comprehensively targets interdependencies in integrated power and hydrogen systems. A multi‐objective optimization approach is implemented using the NSGA‐III algorithm to balance the conflicting objectives of maximizing system disruption and minimizing detection ...
Dong Hua, Haomin Huang, Peifeng Yan, Suisheng Liu, Qinglin Lin, Qian Li   +5 more
wiley   +1 more source

Modeling False Data Injection Attacks on Integrated Electricity-Gas Systems

, 2023
This work studies the modeling of false data injection attacks (FDIAs) on IEGSs. First, we introduce a static state estimation model and bad data detection method for IEGSs.
Li, Zuyi, Liu, Rong-Peng, Wang, Xiaozhe, Zgheib, Rawad   +3 more
core  

Multi‐Layered Optimization for Adaptive Decoy Placement in Cyber‐Resilient Power Systems Under Uncertain Attack Scenarios

IET Renewable Power Generation, Volume 19, Issue 1, January/December 2025.
This paper presents a novel multi‐layered optimization framework to enhance the resilience of cyber‐physical power systems against FDIAs under uncertain attack scenarios. The framework employs a tri‐level Stackelberg optimization approach to model the interactions between defenders, attackers, and system operations.
Hua Dong, Zhao Wei, Cui Peiyi, Liu Yiqing, Hua Hua   +4 more
wiley   +1 more source

A Comprehensive Survey on the Usage of Machine Learning to Detect False Data Injection Attacks in Smart Grids

IEEE Open Journal of the Computer Society
This article provides a comprehensive survey on the application of machine learning techniques for detecting False Data Injection Attacks (FDIA) in smart grids.
Kiara Nand, Zhibo Zhang, Jiankun Hu
doaj   +1 more source

Methodology for incisive foraging of high-risk junctions and elimination of injected false data in smart grid [PDF]

The present work represents a method for identification of the vulnerable nodes in smart grid as well as assessment of the performance of voltage stability indicator technique with the help of weighted least square scheme.
Biswas, Subrata, Ghosh, Poulami, Purkait, Prithwiraj   +2 more
core   +2 more sources

Unmanned Aerial Vehicles in Microgrid Defense

IET Renewable Power Generation, Volume 19, Issue 1, January/December 2025.
This paper introduces a groundbreaking approach using unmanned aerial vehicles (UAVs) integrated with a deep neural network‐robust optimization (DNN‐RO) framework to defend against sophisticated false data injection attacks (FDIAs). Our research pioneers a dynamic UAV‐based defense model, meticulously engineered and simulated across a 50 square ...
Alexis P. Zhao, Da Huo, Mohannad Alhazmi
wiley   +1 more source

Resilient Secondary Distributed Model Predictive Control for Autonomous Microgrid Against Cyber Threats

IET Renewable Power Generation, Volume 19, Issue 1, January/December 2025.
This paper presents a novel framework for enhancing the cyber‐resilience of microgrids (MGs) by integrating consensus‐based distributed model predictive control (DMPC) with a residual‐based Luenberger sliding mode observer (LSMO). ABSTRACT This paper aims to present a novel framework for enhancing the cyber‐resilience of microgrids (MGs) by integrating
Saima Ali, Laiq Khan, Saghir Ahmad, Zahid Ullah   +3 more
wiley   +1 more source

A Cyber‐Resilient Model for Online Wind Power Forecasting Based on Lifelong Learning

IET Renewable Power Generation, Volume 19, Issue 1, January/December 2025.
This paper presents a cyber‐resilient online WPF) method based on LL, aimed at improving forecasting accuracy. The method is tested in two scenarios across four Iranian regions, with and without cyber‐attacks. The results show that the online LL model outperforms conventional models, achieving the lowest MSE values, both under clean data conditions and
Arash Moradzadeh, Behnam Mohammadi‐Ivatloo, Mehdi Abapour, Reza Ghorbani   +3 more
wiley   +1 more source

Enhanced Detection of False Data Injection Attacks Using Hybrid Clustering‐Classification for Various Penetration and Distribution Levels of Renewables

IET Renewable Power Generation, Volume 19, Issue 1, January/December 2025.
A new false data injection attack detection method for AC state estimation in grids with various types (wind and solar), distributions, and penetration levels of renewables that is developed based on hybrid machine learning, leveraging soft and hard clustering before the classification‐based anomaly detection.
Farhad Pirhadi, Hossein Seifi, Hamed Delkhosh   +2 more
wiley   +1 more source

Detecting and mitigating cyber-attacks in AC microgrid composed of marine current turbine DFIGs to improve energy management system [PDF]

In this paper the new effective approaches for detecting and mitigating the important cyber-attacks occurred in an AC microgrid (ACMG) renewable energy, including false data injection attack (FDIA), hijack attack (HjA) and denial of service (DoS) are ...
Guerrero Zapata, Josep Maria, Mahvash, Hossein, Taher, Seyed Abbas   +2 more
core   +1 more source