Reinforcement-Learning-Based Proactive Control for Enabling Power Grid Resilience to Wildfire-Reference-Cited by-同舟云学术

Reinforcement-Learning-Based Proactive Control for Enabling Power Grid Resilience to Wildfire

Published:2024-01 Issue:1 Volume:20 Page:795-805
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Kadir Salah Uddin¹^ORCID,Majumder Subir²^ORCID,Srivastava Anurag K.²^ORCID,Chhokra Ajay Dev³,Neema Himanshu³^ORCID,Dubey Abhishek³^ORCID,Laszka Aron⁴^ORCID

Affiliation:

1. University of Houston, Houston, TX, USA

2. School of Electrical Engineering and Computer Science, West Virginia University, Morgantown, WV, USA

3. Vanderbilt University, Nashville, TN, USA

4. Pennsylvania State University, State College, PA, USA

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

https://ieeexplore.ieee.org/ielam/9424/10355676/10091911-aam.pdf

Reference33 articles.

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2. Population exposure to pre-emptive de-energization aimed at averting wildfires in Northern California

3. Guest Editorial: Data-Driven Reliable and Resilient Energy System Against Disasters

4. Why the 1977 blackout was one of New Yorks darkest hours;Latson;Time,2015

5. Resilience Enhancement With Sequentially Proactive Operation Strategies

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2. Wildfire-Induced Risk Assessment to Enable Resilient and Sustainable Electric Power Grid;Energies;2024-01-07

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4. Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues;Electricity;2023-12-01

5. Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events;Sustainability;2023-08-21