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 …

[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 …

Leveraging Deep Learning to Strengthen the Cyber-Resilience of Renewable Energy Supply Chains: A Survey

MN Halgamuge - IEEE Communications Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Deep learning shows immense potential for strengthening the cyber-resilience of renewable
energy supply chains. However, research gaps in comprehensive benchmarks, real-world …

[HTML][HTML] Reviewing 40 years of artificial intelligence applied to power systems–A taxonomic perspective

F Heymann, H Quest, TL Garcia, C Ballif, M Galus - Energy and AI, 2024 - Elsevier
Artificial intelligence (AI) as a multi-purpose technology is gaining increased attention and is
now widely used across all sectors of the economy. The growing complexity of planning and …

Resiliency of forecasting methods in different application areas of smart grids: A review and future prospects

MA Rahman, MR Islam, MA Hossain, MS Rana… - … Applications of Artificial …, 2024 - Elsevier
The cyber–physical infrastructure of a smart grid requires data-dependent artificial
intelligence (AI)-based forecasting schemes for predicting different aspects for the short-to …

Long-term PM2. 5 concentration prediction based on improved empirical mode decomposition and deep neural network combined with noise reduction auto-encoder …

M Teng, S Li, J Yang, S Wang, C Fan, Y Ding… - Journal of Cleaner …, 2023 - Elsevier
Effective prediction of PM 2.5 long-term concentration can help reduce exposure risks, but
few current studies based on machine learning have been able to credibly predict …

[HTML][HTML] Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations

K Sarieddine, MA Sayed, S Torabi, R Atallah… - International Journal of …, 2024 - Elsevier
Recent reports indicate that electric vehicle charging stations (EVCSs) are susceptible to
remote exploitation through their vulnerable software/cyber components. More importantly …

Preserving microgrid sustainability through robust islanding detection scheme ensuring cyber-situational awareness

M Tajdinian, M Mohammadpourfard, Y Weng… - Sustainable Cities and …, 2023 - Elsevier
Along with the numerous environmental and operational benefits of renewable energy
sources (RESs) integration into modern distribution networks, maintaining system security in …

TADNet: Temporal Attention Decomposition Networks for Probabilistic Energy Forecasting

J Ye, B Zhao, D Liu, Q Wei… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Many decision-making processes in the energy industry depend on probabilistic forecasting
to quantify the future uncertainties. However, existing methods are difficult to provide reliable …

Data-driven learning-based classification model for mitigating false data injection attacks on dynamic line rating systems

OA Lawal, J Teh, B Alharbi, CM Lai - Sustainable Energy, Grids and …, 2024 - Elsevier
The increasing need to explore electric power grid expansion technologies like dynamic line
rating (DLR) systems and their dependence on real-time weather data for system planning …