Fast Contingency Filtering Using Machine Learning for Power System Planning-Reference-Cited by-同舟云学术

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Fast Contingency Filtering Using Machine Learning for Power System Planning

Published:2023-08-13 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE)
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Author:

Alvarez David L.¹,Nour Georges Abdul¹,Gaha Mohamed²,Côté Alain³

Affiliation:

1. Université du Québec à Trois-Rivières,Department of Industrial Engineering,Trois-Rivières,Canada

2. Hydro-Québec’s research institute (IREQ),Data Science and High Performance Computing,Varennes,Canada

3. Hydro-Québec’s research institute (IREQ),Asset Sustainability,Varennes,Canada

Funder

Hydro-Québec

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10274529/10274530/10274544.pdf?arnumber=10274544

Reference31 articles.

1. A Generic Approach for Solving Nonlinear-Discrete Security-Constrained Optimal Power Flow Problems in Large-Scale Systems

2. Integration of contingency analysis n-1 with systematic transmission capacity expansion planning: Ercot case study;majidi-qadikolai;IEEE Transactions on Power Systems,2016

3. Cloud Computing for Power System Simulations at ISO New England—Experiences and Challenges

4. An Iterative AC-SCOPF Approach Managing the Contingency and Corrective Control Failure Uncertainties With a Probabilistic Guarantee

5. Scikit-learn: Machine learning in python;pedregosa;Journal of Machine Learning Research,2011

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