Reinforcement learning for selective key applications in power systems: Recent advances and future challenges
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …
modern power systems are confronted with new operational challenges, such as growing …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
A survey on the detection algorithms for false data injection attacks in smart grids
Cyber-physical attacks are the main substantial threats facing the utilization and
development of the various smart grid technologies. Among these attacks, false data …
development of the various smart grid technologies. Among these attacks, false data …
Deep reinforcement learning for power system applications: An overview
Due to increasing complexity, uncertainty and data dimensions in power systems,
conventional methods often meet bottlenecks when attempting to solve decision and control …
conventional methods often meet bottlenecks when attempting to solve decision and control …
Distributed control and communication strategies in networked microgrids
Q Zhou, M Shahidehpour, A Paaso… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
Networked microgrids (NMGs) provide a promising solution for accommodating various
distributed energy resources (DERs) and enhancing the system performance in terms of …
distributed energy resources (DERs) and enhancing the system performance in terms of …
Deep reinforcement learning for cyber security
The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …
being exposed to cyberattacks more than ever. The complexity and dynamics of …
Smart grid cyber-physical attack and defense: A review
Recent advances in the cyber-physical smart grid (CPSG) have enabled a broad range of
new devices based on the information and communication technology (ICT). However, these …
new devices based on the information and communication technology (ICT). However, these …
Machine learning driven smart electric power systems: Current trends and new perspectives
MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020 - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …
How machine learning changes the nature of cyberattacks on IoT networks: A survey
The Internet of Things (IoT) has continued gaining in popularity and importance in everyday
life in recent years. However, this development does not only present advantages. Indeed …
life in recent years. However, this development does not only present advantages. Indeed …
Reinforcement learning for iot security: A comprehensive survey
The number of connected smart devices has been increasing exponentially for different
Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT …
Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT …