Model-free emergency frequency control based on reinforcement learning
Unexpected large power surges will cause instantaneous grid shock and, thus, emergency
control plans must be implemented to prevent the system from collapsing. In this article, with …
control plans must be implemented to prevent the system from collapsing. In this article, with …
Accelerated derivative-free deep reinforcement learning for large-scale grid emergency voltage control
Load shedding has been one of the most widely used and effective emergency control
approaches against voltage instability. With increased uncertainties and rapidly changing …
approaches against voltage instability. With increased uncertainties and rapidly changing …
Adaptive power system emergency control using deep reinforcement learning
Power system emergency control is generally regarded as the last safety net for grid security
and resiliency. Existing emergency control schemes are usually designed offline based on …
and resiliency. Existing emergency control schemes are usually designed offline based on …
Brain-inspired deep meta-reinforcement learning for active coordinated fault-tolerant load frequency control of multi-area grids
J Li, T Zhou, H Cui - IEEE transactions on automation science …, 2023 - ieeexplore.ieee.org
This paper proposes an active coordinated fault tolerance load frequency control (AFCT-
LFC) method, which effectively prevents sudden frequency changes caused by unit actuator …
LFC) method, which effectively prevents sudden frequency changes caused by unit actuator …
Resilient operation of distribution grids using deep reinforcement learning
MM Hosseini, M Parvania - IEEE Transactions on Industrial …, 2021 - ieeexplore.ieee.org
This article utilizes deep reinforcement learning to develop an intelligent resilience controller
(IRC) that devises fast real-time operation decisions to strategically dispatch distributed …
(IRC) that devises fast real-time operation decisions to strategically dispatch distributed …
Optimizing the post-disaster control of islanded microgrid: A multi-agent deep reinforcement learning approach
Extreme disasters may cause the power supply to the distribution system (DS) to be
interrupted. The DS is forced to operate in island mode and forms an islanded microgrid …
interrupted. The DS is forced to operate in island mode and forms an islanded microgrid …
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 …
MADDPG-based security situational awareness for smart grid with intelligent edge
Advanced communication and information technologies enable smart grids to be more
intelligent and automated, although many security issues are emerging. Security situational …
intelligent and automated, although many security issues are emerging. Security situational …
A novel automatic generation control method based on the large-scale electric vehicles and wind power integration into the grid
L Xi, H Li, J Zhu, Y Li, S Wang - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
In order to solve the problem of frequency instability of power system due to strong random
disturbance caused by large-scale electric vehicles and wind power grid connection, an …
disturbance caused by large-scale electric vehicles and wind power grid connection, an …
Stability constrained reinforcement learning for real-time voltage control
Deep reinforcement learning (RL) has been recognized as a promising tool to address the
challenges in real-time control of power systems. However, its deployment in real-world …
challenges in real-time control of power systems. However, its deployment in real-world …