Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets-Reference-Cited by-同舟云学术

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Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets

Published:2022-12-17 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Big Data (Big Data)
language:
Short-container-title:

Author:

Chen Yuzhou¹,Jiang Tian²,Heleno Miguel³,Moreira Alexandre³,Gel Yulia R.³

Affiliation:

1. Temple University,Department of Computer and Information Sciences,Philadelphia,USA

2. The University of Texas at Dallas,Department of Mathematical Sciences,Dallas,USA

3. Lawrence Berkeley National Laboratory,Energy Storage and Distributed Resources Division,Berkeley,USA

Funder

U.S. Department of Energy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020715.pdf?arnumber=10020715

Reference51 articles.

1. Machine learning driven smart electric power systems: Current trends and new perspectives

2. State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative assessment methodologies, and enhancement strategies

3. The learning to run a power network challenge,2020

4. L2rpn: Learning to run a power network in a sustainable world NeurIPS2020 challenge design;marot,2020

5. Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future

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1. Research on cascading fault-guided search of high-voltage lines based on GCN-LSTM;2024 6th Asia Energy and Electrical Engineering Symposium (AEEES);2024-03-28

2. Toward dynamic stability assessment of power grid topologies using graph neural networks;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-10-01

3. H$$^2$$-Nets: Hyper-hodge Convolutional Neural Networks for Time-Series Forecasting;Machine Learning and Knowledge Discovery in Databases: Research Track;2023

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