End-to-End Topology-Aware Machine Learning for Power System Reliability Assessment-Reference-Cited by-同舟云学术

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End-to-End Topology-Aware Machine Learning for Power System Reliability Assessment

Published:2022-06-12 Issue: Volume: Page:
ISSN:
Container-title:2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
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Author:

Zhu Yongli¹,Singh Chanan¹

Affiliation:

1. Texas A&M University,College Station,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9810556/9810557/09810623.pdf?arnumber=9810623

Reference12 articles.

1. Reliability Assessment of Electric Power Systems Using Monte Carlo Methods

2. The Impact of Restructuring Policy Changes on Power Grid Reliability

3. Reliability Analysis of Power Systems Integrated With High-Penetration of Power Converters

4. Power System Reliability Evaluation using Monte Carlo Simulation and Multi Label Classifier

5. Fast Grid Splitting Detection for N-1 Contingency Analysis by Graph Computing

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Composite reliability assessment of systems with grid‐edge renewable resources via quasi‐sequential Monte Carlo and cross‐entropy techniques;IET Generation, Transmission & Distribution;2023-10-03

2. Simplifying Rogowski Coil Modeling: Simulation and Experimental Verification;Sensors;2023-09-22

3. Assessing Bulk Power System Reliability by End-to-End Line Maintenance-Aware Learning;IEEE Access;2023

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