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 …
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
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 …
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 …
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
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 …
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
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 …
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 …
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 …
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
Recent reports indicate that electric vehicle charging stations (EVCSs) are susceptible to
remote exploitation through their vulnerable software/cyber components. More importantly …
remote exploitation through their vulnerable software/cyber components. More importantly …
Preserving microgrid sustainability through robust islanding detection scheme ensuring cyber-situational awareness
Along with the numerous environmental and operational benefits of renewable energy
sources (RESs) integration into modern distribution networks, maintaining system security in …
sources (RESs) integration into modern distribution networks, maintaining system security in …
TADNet: Temporal Attention Decomposition Networks for Probabilistic Energy Forecasting
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 …
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
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 …
rating (DLR) systems and their dependence on real-time weather data for system planning …