Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions

Z Shi, W Yao, Z Li, L Zeng, Y Zhao, R Zhang, Y Tang… - Applied Energy, 2020 - Elsevier
Smart grid is the new trend for clean, sustainable, efficient and reliable energy generation,
delivery and use. To ensure stable and secure operation is essential for the smart grid …

Cascading failures in internet of things: review and perspectives on reliability and resilience

L Xing - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
In the Internet of Things (IoT), various devices operate collaboratively in collecting data,
relaying information to one another, and processing information intelligently. Due to …

Deep reinforcement learning from demonstrations to assist service restoration in islanded microgrids

Y Du, D Wu - IEEE Transactions on Sustainable Energy, 2022 - ieeexplore.ieee.org
Microgrids can be operated in island mode during utility grid outages to support service
restoration and improve system resilience. To schedule and dispatch distributed energy …

Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review

F Ahsan, NH Dana, SK Sarker, L Li… - … and Control of …, 2023 - ieeexplore.ieee.org
Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …

A review on cybersecurity analysis, attack detection, and attack defense methods in cyber-physical power systems

D Du, M Zhu, X Li, M Fei, S Bu, L Wu… - Journal of Modern …, 2022 - ieeexplore.ieee.org
Potential malicious cyber-attacks to power systems which are connected to a wide range of
stakeholders from the top to tail will impose significant societal risks and challenges. The …

(Deep) reinforcement learning for electric power system control and related problems: A short review and perspectives

M Glavic - Annual Reviews in Control, 2019 - Elsevier
This paper reviews existing works on (deep) reinforcement learning considerations in
electric power system control. The works are reviewed as they relate to electric power …

Deep reinforcement learning framework for resilience enhancement of distribution systems under extreme weather events

Z Zhou, Z Wu, T Jin - International Journal of Electrical Power & Energy …, 2021 - Elsevier
More and more extreme weather events now occur due to global warming, and the existing
power systems are powerless to cope with such high-impact, low-probability events. In order …

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 …

Integrating reinforcement learning and optimal power dispatch to enhance power grid resilience

Q Li, X Zhang, J Guo, X Shan, Z Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Power grids are vulnerable to extreme events that may cause the failure of multiple
components and lead to severe power outages. It is of practical importance to design …

Machine learning applications in cascading failure analysis in power systems: A review

NM Sami, M Naeini - Electric Power Systems Research, 2024 - Elsevier
Cascading failures pose a significant threat to power grids and have garnered considerable
research interest in the power system domain. The inherent uncertainty and severe impact …